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Sarahpa/finetune-mistral-DPO-7B | Sarahpa | 2025-06-16T04:06:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:01:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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original-shruthi-narayanan-viral-video/wATCH.shruthi.narayanan.viral.video.original | original-shruthi-narayanan-viral-video | 2025-06-16T04:05:25Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:05:09Z | [](https://t.co/P8Ex9FtH0g) |
18-shruthi-narayanan-viral-video/wATCH.shruthi.narayanan.viral.video.original.link | 18-shruthi-narayanan-viral-video | 2025-06-16T04:04:00Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:03:52Z | [](https://t.co/P8Ex9FtH0g) |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.25_epoch2 | MinaMila | 2025-06-16T04:03:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:01:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
seregadgl/sts_v11 | seregadgl | 2025-06-16T04:02:47Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:111476",
"loss:CosineSimilarityLoss",
"dataset:seregadgl/data_cross_gpt_139k",
"arxiv:1908.10084",
"base_model:sergeyzh/LaBSE-ru-sts",
"base_model:finetune:sergeyzh/LaBSE-ru-sts",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-06-16T04:02:21Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:111476
- loss:CosineSimilarityLoss
base_model: sergeyzh/LaBSE-ru-sts
widget:
- source_sentence: 'трюковый самокат plank 180 белый '
sentences:
- смарт-телевизор 75 sony kd-75x950h
- самокат для трюков плэнк 1.80 м белый
- xiaomi mi 11 8gb 128gb
- source_sentence: 'вейп vaporesso xros '
sentences:
- садовая ограда классика 4 2 м белый
- кухонные весы
- электронная сигарета voopoo drag
- source_sentence: серьги l atelier precieux 1628710
sentences:
- фильтр hepa для пылесоса варис st400
- потолочная люстра майтон nostalgia ceiling chandelier mod048pl-06g
- серьги atelier de bijoux 1628712
- source_sentence: 'мобильный геймпад триггерами x2 '
sentences:
- электроскутер nitro pro milano 750w led
- наушники без проводов мейзу ep52 lite
- геймпад с функцией триггеров x2
- source_sentence: комод 7 рисунком машинки 4 ящика
sentences:
- удлинитель far f 505 d lara выключателем 2 0м
- беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный
- комод 8 с изображением супергероев 6 ящиков
datasets:
- seregadgl/data_cross_gpt_139k
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on sergeyzh/LaBSE-ru-sts
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: eval
type: eval
metrics:
- type: cosine_accuracy
value: 0.9722640832436311
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.630459189414978
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9724366041896361
name: Cosine F1
- type: cosine_f1_threshold
value: 0.5821653008460999
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9647847565278758
name: Cosine Precision
- type: cosine_recall
value: 0.9802107980210798
name: Cosine Recall
- type: cosine_ap
value: 0.9945729266353226
name: Cosine Ap
- type: cosine_mcc
value: 0.9445047865635516
name: Cosine Mcc
---
# SentenceTransformer based on sergeyzh/LaBSE-ru-sts
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) on the [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) <!-- at revision 00c333ce29c9ad739f48baca9a578cd1e85094d4 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("seregadgl/sts_v11")
# Run inference
sentences = [
'комод 7 рисунком машинки 4 ящика',
'комод 8 с изображением супергероев 6 ящиков',
'беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `eval`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.9723 |
| cosine_accuracy_threshold | 0.6305 |
| cosine_f1 | 0.9724 |
| cosine_f1_threshold | 0.5822 |
| cosine_precision | 0.9648 |
| cosine_recall | 0.9802 |
| **cosine_ap** | **0.9946** |
| cosine_mcc | 0.9445 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### data_cross_gpt_139k
* Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e)
* Size: 111,476 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 14.84 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.64 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-----------------|
| <code>нож кухонный 21см синий</code> | <code>кухонный нож 22см зелёный</code> | <code>0.0</code> |
| <code>блок питания универсальный для мерцающих флэш гирлянд rich led бахрома занавес нить белый</code> | <code>адаптер питания для мигающих led гирлянд "luminous decor" бахрома занавес нить зелёный</code> | <code>0.0</code> |
| <code>защитная пленка для apple iphone 6 прозрачная </code> | <code>protective film for apple iphone 6 transparent</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### data_cross_gpt_139k
* Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e)
* Size: 27,870 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 15.05 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.57 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------|:-----------------|
| <code>сумка дорожная складная полет оранжевая bradex td 0599 </code> | <code>сумка для путешествий складная брадекс orange</code> | <code>1.0</code> |
| <code>наушники sennheiser hd 450bt белый </code> | <code>наушники сенхайзер hd 450bt white</code> | <code>1.0</code> |
| <code>перчатки stg al-05-1871 синие серые черные зеленыеполноразмерные xl</code> | <code>перчатки stg al-05-1871 blue gray black green full size xl</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 4.7459131195420915e-05
- `weight_decay`: 0.03196240090522689
- `num_train_epochs`: 2
- `warmup_ratio`: 0.014344463935915175
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 4.7459131195420915e-05
- `weight_decay`: 0.03196240090522689
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.014344463935915175
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | eval_cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:--------------:|
| 0.0287 | 100 | 0.189 | - | - |
| 0.0574 | 200 | 0.0695 | - | - |
| 0.0861 | 300 | 0.067 | - | - |
| 0.1148 | 400 | 0.0643 | - | - |
| 0.1435 | 500 | 0.0594 | 0.0549 | 0.9862 |
| 0.1722 | 600 | 0.0565 | - | - |
| 0.2009 | 700 | 0.0535 | - | - |
| 0.2296 | 800 | 0.0506 | - | - |
| 0.2583 | 900 | 0.0549 | - | - |
| 0.2870 | 1000 | 0.0535 | 0.0451 | 0.9888 |
| 0.3157 | 1100 | 0.0492 | - | - |
| 0.3444 | 1200 | 0.0499 | - | - |
| 0.3731 | 1300 | 0.0486 | - | - |
| 0.4018 | 1400 | 0.0458 | - | - |
| 0.4305 | 1500 | 0.0458 | 0.0419 | 0.9877 |
| 0.4592 | 1600 | 0.0502 | - | - |
| 0.4879 | 1700 | 0.045 | - | - |
| 0.5166 | 1800 | 0.0435 | - | - |
| 0.5454 | 1900 | 0.0426 | - | - |
| 0.5741 | 2000 | 0.0422 | 0.0386 | 0.9906 |
| 0.6028 | 2100 | 0.0436 | - | - |
| 0.6315 | 2200 | 0.043 | - | - |
| 0.6602 | 2300 | 0.0432 | - | - |
| 0.6889 | 2400 | 0.0397 | - | - |
| 0.7176 | 2500 | 0.0394 | 0.0357 | 0.9903 |
| 0.7463 | 2600 | 0.039 | - | - |
| 0.7750 | 2700 | 0.0398 | - | - |
| 0.8037 | 2800 | 0.0394 | - | - |
| 0.8324 | 2900 | 0.0426 | - | - |
| 0.8611 | 3000 | 0.0345 | 0.0341 | 0.9921 |
| 0.8898 | 3100 | 0.0361 | - | - |
| 0.9185 | 3200 | 0.0365 | - | - |
| 0.9472 | 3300 | 0.0401 | - | - |
| 0.9759 | 3400 | 0.0391 | - | - |
| 1.0046 | 3500 | 0.0342 | 0.0310 | 0.9928 |
| 1.0333 | 3600 | 0.0267 | - | - |
| 1.0620 | 3700 | 0.0264 | - | - |
| 1.0907 | 3800 | 0.0263 | - | - |
| 1.1194 | 3900 | 0.0248 | - | - |
| 1.1481 | 4000 | 0.0282 | 0.0301 | 0.9928 |
| 1.1768 | 4100 | 0.0279 | - | - |
| 1.2055 | 4200 | 0.0258 | - | - |
| 1.2342 | 4300 | 0.0248 | - | - |
| 1.2629 | 4400 | 0.0289 | - | - |
| 1.2916 | 4500 | 0.0261 | 0.0291 | 0.9935 |
| 1.3203 | 4600 | 0.0262 | - | - |
| 1.3490 | 4700 | 0.0276 | - | - |
| 1.3777 | 4800 | 0.0256 | - | - |
| 1.4064 | 4900 | 0.0272 | - | - |
| 1.4351 | 5000 | 0.0283 | 0.0284 | 0.9939 |
| 1.4638 | 5100 | 0.0254 | - | - |
| 1.4925 | 5200 | 0.0252 | - | - |
| 1.5212 | 5300 | 0.0234 | - | - |
| 1.5499 | 5400 | 0.0228 | - | - |
| 1.5786 | 5500 | 0.0248 | 0.0277 | 0.9941 |
| 1.6073 | 5600 | 0.024 | - | - |
| 1.6361 | 5700 | 0.0225 | - | - |
| 1.6648 | 5800 | 0.0234 | - | - |
| 1.6935 | 5900 | 0.0226 | - | - |
| 1.7222 | 6000 | 0.0248 | 0.0265 | 0.9942 |
| 1.7509 | 6100 | 0.0247 | - | - |
| 1.7796 | 6200 | 0.0219 | - | - |
| 1.8083 | 6300 | 0.026 | - | - |
| 1.8370 | 6400 | 0.0209 | - | - |
| 1.8657 | 6500 | 0.0252 | 0.0262 | 0.9945 |
| 1.8944 | 6600 | 0.0218 | - | - |
| 1.9231 | 6700 | 0.0223 | - | - |
| 1.9518 | 6800 | 0.0228 | - | - |
| 1.9805 | 6900 | 0.0242 | - | - |
| 2.0 | 6968 | - | 0.0257 | 0.9946 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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sonnykoalu/xdf | sonnykoalu | 2025-06-16T04:02:41Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-16T04:01:45Z | ---
license: other
license_name: none
license_link: LICENSE
---
|
2-wolf-1-girl-tv/18.VIDEOS.2.wolf.1.girl.viral.video.download.hq | 2-wolf-1-girl-tv | 2025-06-16T04:01:59Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:01:47Z | 01 seconds ago
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://infobal.com.ar/watch-full-video/?Bados.all)
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://infobal.com.ar/watch-full-video/?Bados.all)
<a href="https://infobal.com.ar/watch-full-video/?Bados.all" rel="nofollow" data-target="animated-image.originalLink"><img src="https://media.post.rvohealth.io/wp-content/uploads/2024/02/video-overlay-bed-sex-couple-original.jpg" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Sapna-Shah-Indians-viral-video/wATCH.Sapna.Shah.Nulook.India.viral.video | Sapna-Shah-Indians-viral-video | 2025-06-16T03:58:43Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:58:34Z | [](https://t.co/P8Ex9FtH0g) |
mahiye-selin-viral-videos-tv-Free/FULL.VIDEO.mahiye.selin.Viral.Video.Tutorial.Official | mahiye-selin-viral-videos-tv-Free | 2025-06-16T03:58:00Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:57:49Z | 01 seconds ago
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://infobal.com.ar/watch-full-video/?Bados.all)
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://infobal.com.ar/watch-full-video/?Bados.all)
<a href="https://infobal.com.ar/watch-full-video/?Bados.all" rel="nofollow" data-target="animated-image.originalLink"><img src="https://media.post.rvohealth.io/wp-content/uploads/2024/02/video-overlay-bed-sex-couple-original.jpg" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
opendiffusionai/t5-v1_1-base-encoder-only | opendiffusionai | 2025-06-16T03:57:33Z | 0 | 0 | null | [
"safetensors",
"t5",
"base_model:google/t5-v1_1-base",
"base_model:finetune:google/t5-v1_1-base",
"region:us"
] | null | 2025-06-15T21:33:13Z | ---
base_model:
- google/t5-v1_1-base
---
This model exists only as a convenience, to be able to use google t5 base conveniently for comparison
and testing sake.
That being said... it is significantly inferior to the other models.
If you raally need a model with 768dim output.. you are most likely better off using a larger one and a projection layer, rather than using this one
at native 768 size, unless you just need a tiny model.
(projection layers are surprisingly easy to use. only about 10 more lines of code. poke around at our other t5 based sd models to find examples of this.) |
Indians-viral-video/wATCH.Sapna.Shah.Nulook.India.viral.video.original.Trending | Indians-viral-video | 2025-06-16T03:57:15Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:56:37Z | [](https://t.co/P8Ex9FtH0g) |
erdem-erdem/Qwen2.5-3B-Instruct-countdown-ps-grpo-r32 | erdem-erdem | 2025-06-16T03:53:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:52:24Z | ---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** erdem-erdem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sil-ai/madlad400-finetuned-onr_x_rbr-tpi | sil-ai | 2025-06-16T03:53:37Z | 9 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"onr",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-10T23:21:25Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-onr_x_rbr-tpi
results: []
language:
- onr
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-onr_x_rbr-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Onnele, Romei-Barera to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Onnele, Romei-Barera (`onr`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Onnele, Romei-Barera:
- Onnele, Romei-Barera Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-onr_x_rbr-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-onr_x_rbr-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-onr_x_rbr-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1796
- Chrf: 79.0591
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2929 | 7.7435 | 1600 | 0.2140 | 76.7801 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-onr_x_goi-tpi | sil-ai | 2025-06-16T03:53:36Z | 8 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"onr",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-10T23:22:35Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-onr_x_goi-tpi
results: []
language:
- onr
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-onr_x_goi-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Onnele, Goiniri to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Onnele, Goiniri (`onr`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Onnele, Goiniri:
- Onnele, Goiniri Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-onr_x_goi-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-onr_x_goi-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-onr_x_goi-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1157
- Chrf: 85.9775
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.197 | 7.7295 | 1600 | 0.1436 | 83.9241 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-bpe_x_bar-tpi | sil-ai | 2025-06-16T03:53:32Z | 14 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"bpe",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-06T19:20:16Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-bpe_x_bar-tpi
results: []
language:
- bpe
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-bpe_x_bar-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Bauni, Barupu to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Bauni, Barupu (`bpe`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Bauni, Barupu:
- Bauni, Barupu Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-bpe_x_bar-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-bpe_x_bar-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-bpe_x_bar-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1551
- Chrf: 80.7709
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2676 | 7.6923 | 1600 | 0.1899 | 78.1510 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-aps-tpi | sil-ai | 2025-06-16T03:53:30Z | 17 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"aps",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-03T13:20:01Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-aps-tpi
results: []
language:
- aps
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-aps-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Arop to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Arop (`aps`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Arop:
- Arop Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-aps-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-aps-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-aps-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1082
- Chrf: 84.5824
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.328 | 3.8381 | 1600 | 0.2650 | 73.9647 |
| 0.1979 | 7.6762 | 3200 | 0.1369 | 82.2722 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
opendiffusionai/t5-v1_1-xl-encoder-only | opendiffusionai | 2025-06-16T03:53:29Z | 21 | 0 | null | [
"safetensors",
"t5",
"base_model:google/t5-v1_1-xl",
"base_model:finetune:google/t5-v1_1-xl",
"license:apache-2.0",
"region:us"
] | null | 2025-06-15T03:13:22Z | ---
license: apache-2.0
base_model:
- google/t5-v1_1-xl
tags:
- t5
---
This is just the encoder weights from "google/t5-v1_1-xl"
It takes 11GB down to 4GB.
The script to do the extraction is included here as
[transform.py](transform.py)
Edit: Now that I have this in a convenient form...
I got a chance to test t5-xxl projected down to 2048, vs this t5-xl
Surprisingly, even with an untrained projection layer, trivial embedding diversity scores rate
the projected xxl version higher than native xl at 2048.
So, while this model will continue to exist as a convenient way to compare.. and possibly as something
to use if you are really, really REALLY tight on memory... you are probably best off
using t5-xxl whenever you can. |
sil-ai/madlad400-finetuned-tpi-onr_x_goi | sil-ai | 2025-06-16T03:53:26Z | 7 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"tpi",
"onr",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-10T23:21:22Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-onr_x_goi
results: []
language:
- tpi
- onr
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-tpi-onr_x_goi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Onnele, Goiniri.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Tok Pisin (`tpi`)
- **Target language:** Onnele, Goiniri (`onr`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Onnele, Goiniri:
- Onnele, Goiniri Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-onr_x_goi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-onr_x_goi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-tpi-onr_x_goi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1267
- Chrf: 86.0110
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2167 | 7.7295 | 1600 | 0.1581 | 83.8321 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-tpi-uni | sil-ai | 2025-06-16T03:53:25Z | 61 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"tpi",
"uni",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-05T03:56:30Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-uni
results: []
language:
- tpi
- uni
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-tpi-uni
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Ramo.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Tok Pisin (`tpi`)
- **Target language:** Ramo (`uni`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Ramo:
- Ramo Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-uni")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-uni")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-tpi-uni
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2914
- Chrf: 70.9472
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4351 | 7.7482 | 1600 | 0.3454 | 67.9624 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-tpi-bpe_x_pou | sil-ai | 2025-06-16T03:52:33Z | 136 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"tpi",
"bpe",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-06T21:34:34Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-bpe_x_pou
results: []
language:
- tpi
- bpe
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-tpi-bpe_x_pou
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Bauni, Pou.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Tok Pisin (`tpi`)
- **Target language:** Bauni, Pou (`bpe`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Bauni, Pou:
- Bauni, Pou Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_pou")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_pou")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-tpi-bpe_x_pou
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2035
- Chrf: 79.0847
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3414 | 7.8144 | 1600 | 0.2413 | 76.9720 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-tpi-bpe_x_bar | sil-ai | 2025-06-16T03:52:32Z | 103 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"tpi",
"bpe",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-06T19:16:18Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-bpe_x_bar
results: []
language:
- tpi
- bpe
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-tpi-bpe_x_bar
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Bauni, Barupu.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Tok Pisin (`tpi`)
- **Target language:** Bauni, Barupu (`bpe`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Bauni, Barupu:
- Bauni, Barupu Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_bar")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_bar")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-tpi-bpe_x_bar
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2353
- Chrf: 76.7564
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3624 | 7.6923 | 1600 | 0.2815 | 74.1335 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
dgambettaphd/M_llm2_run2_gen8_WXS_doc1000_synt64_lr1e-04_acm_FRESH | dgambettaphd | 2025-06-16T03:52:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T03:51:55Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sil-ai/madlad400-finetuned-tpi-sso | sil-ai | 2025-06-16T03:51:41Z | 17 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"tpi",
"sso",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-04T14:02:32Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-sso
results: []
language:
- tpi
- sso
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-tpi-sso
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Sissono.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Tok Pisin (`tpi`)
- **Target language:** Sissono (`sso`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Sissono:
- Sissono Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-sso")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-sso")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-tpi-sso
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2862
- Chrf: 71.3983
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4531 | 7.7155 | 1600 | 0.3362 | 68.3550 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
Mezzo-Fun-Viral-Video/VIDEO.mezzo.fun.Viral.Video.Tutorial.Official | Mezzo-Fun-Viral-Video | 2025-06-16T03:50:52Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:48:42Z | <a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
sil-ai/madlad400-finetuned-tpi-mbk | sil-ai | 2025-06-16T03:50:44Z | 27 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"tpi",
"mbk",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-03T16:23:33Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-mbk
results: []
language:
- tpi
- mbk
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-tpi-mbk
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Malol.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Tok Pisin (`tpi`)
- **Target language:** Malol (`mbk`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Malol:
- Malol Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-mbk")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-mbk")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-tpi-mbk
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2360
- Chrf: 75.8294
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3713 | 7.7108 | 1600 | 0.2797 | 73.0831 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.5_epoch2 | MinaMila | 2025-06-16T03:50:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:48:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed] |
huihui-ai/Huihui-MoE-1.2B-A0.6B | huihui-ai | 2025-06-16T03:49:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"moe",
"conversational",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T02:31:56Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen3-0.6B
- suayptalha/Qwen3-0.6B-Code-Expert
- suayptalha/Qwen3-0.6B-Math-Expert
- suayptalha/Qwen3-0.6B-Medical-Expert
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- moe
---
# huihui-ai/Huihui-MoE-1.2B-A0.6B
## Model Overview
Huihui-MoE-1.2B-A0.6B is a **Mixture of Experts (MoE)** language model developed by **huihui.ai**, built upon the **[Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)** base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 3 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications.
### Note
[huihui-ai/Huihui-MoE-1B-A0.6B](https://huggingface.co/huihui-ai/Huihui-MoE-1B-A0.6B) Because `tie_word_embeddings=True`, the parameters for the lm_head were not saved, which causes ollama to be unable to use it. Therefore, this version supports ollama.
- **Architecture**: Qwen3MoeForCausalLM model with 3 experts per layer (num_experts=3), activating 1 expert per token (num_experts_per_tok=1).
- **Total Parameters**: ~1.2 billion (1.2B)
- **Activated Parameters**: ~0.62 billion (0.6B) during inference, comparable to Qwen3-0.6B
- **Developer**: huihui.ai
- **Release Date**: June 2025
- **License**: Inherits the license of the Qwen3 base model (apache-2.0)
## Expert Models:
### Coding:
[suayptalha/Qwen3-0.6B-Code-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Code-Expert)
This model was fully fine-tuned with BF16 on first 20k rows of `nvidia/OpenCodeReasoning` dataset for 1 epoch.
### Math:
[suayptalha/Qwen3-0.6B-Math-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Math-Expert)
This model was fully fine-tuned with BF16 on entire `unsloth/OpenMathReasoning-mini` dataset for 1 epoch.
### Medical:
[suayptalha/Qwen3-0.6B-Medical-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Medical-Expert)
This model was fully fine-tuned with BF16 on first 20k rows of `FreedomIntelligence/medical-o1-reasoning-SFT` dataset for 1 epoch.
### Instruction Following:
[Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
`Qwen/Qwen3-0.6B` model was directly used for this expert, no fine-tune was applied.
## Training
- **Base Model**: Qwen3-0.6B, pre-trained by the Qwen team, Experts, pre-trained by the Suayptalha team.
- **Conversion**: The model copies embeddings, self-attention, and normalization weights from Qwen3-0.6B, replacing MLP layers with MoE layers (3 experts). Gating weights are randomly initialized.
- **Fine-Tuning**: Not fine-tuned; users are recommended to fine-tune for specific tasks to optimize expert routing. The fine-tuned version is already available and can be referred to as [huihui-ai/Huihui-MoE-1.2B-A0.6B-SFT](https://huggingface.co/huihui-ai/Huihui-MoE-1.2B-A0.6B-SFT).
## ollama
You can use [huihui_ai/huihui-moe:1.2b](https://ollama.com/huihui_ai/huihui-moe:1.2b) directly,
Switch the thinking toggle using /set think and /set nothink
```
ollama run huihui_ai/huihui-moe:1.2b
```
## Usage
```
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
import torch
import os
import signal
import random
import numpy as np
import time
from collections import Counter
cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)
print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
# Load the model and tokenizer
NEW_MODEL_ID = "huihui-ai/Huihui-MoE-1.2B-A0.6B"
print(f"Load Model {NEW_MODEL_ID} ... ")
quant_config_32 = BitsAndBytesConfig(
load_in_32bit=True,
bnb_32bit_compute_dtype=torch.bfloat16,
bnb_32bit_use_double_quant=True,
llm_int32_enable_fp32_cpu_offload=True,
)
model = AutoModelForCausalLM.from_pretrained(
NEW_MODEL_ID,
device_map="auto",
trust_remote_code=True,
#quantization_config=quant_config_32,
torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
messages = []
nothink = False
same_seed = False
skip_prompt=True
skip_special_tokens=True
do_sample = True
def set_random_seed(seed=None):
"""Set random seed for reproducibility. If seed is None, use int(time.time())."""
if seed is None:
seed = int(time.time()) # Convert float to int
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # If using CUDA
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
return seed # Return seed for logging if needed
class CustomTextStreamer(TextStreamer):
def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
self.generated_text = ""
self.stop_flag = False
self.init_time = time.time() # Record initialization time
self.end_time = None # To store end time
self.first_token_time = None # To store first token generation time
self.token_count = 0 # To track total tokens
def on_finalized_text(self, text: str, stream_end: bool = False):
if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text
self.first_token_time = time.time()
self.generated_text += text
# Count tokens in the generated text
tokens = self.tokenizer.encode(text, add_special_tokens=False)
self.token_count += len(tokens)
print(text, end="", flush=True)
if stream_end:
self.end_time = time.time() # Record end time when streaming ends
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
self.end_time = time.time() # Record end time when generation is stopped
def get_metrics(self):
"""Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
if self.end_time is None:
self.end_time = time.time() # Set end time if not already set
total_time = self.end_time - self.init_time # Total time from init to end
tokens_per_second = self.token_count / total_time if total_time > 0 else 0
first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
metrics = {
"init_time": self.init_time,
"first_token_time": self.first_token_time,
"first_token_latency": first_token_latency,
"end_time": self.end_time,
"total_time": total_time, # Total time in seconds
"total_tokens": self.token_count,
"tokens_per_second": tokens_per_second
}
return metrics
def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens):
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
enable_thinking = not nothink,
add_generation_prompt=True,
return_tensors="pt"
)
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
tokens = input_ids.to(model.device)
attention_mask = attention_mask.to(model.device)
streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
def signal_handler(sig, frame):
streamer.stop_generation()
print("\n[Generation stopped by user with Ctrl+C]")
signal.signal(signal.SIGINT, signal_handler)
generate_kwargs = {}
if do_sample:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
else:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
print("Response: ", end="", flush=True)
try:
generated_ids = model.generate(
tokens,
attention_mask=attention_mask,
#use_cache=False,
pad_token_id=tokenizer.pad_token_id,
streamer=streamer,
**generate_kwargs
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del input_ids, attention_mask
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
init_seed = set_random_seed()
# List to store activated expert indices
activated_experts = []
# Define hook function to capture gate_probs output
def hook_fn(module, input, output):
# output is gate_probs, shape: [batch_size, sequence_length, num_experts]
gate_probs = output
# Compute top-1 expert indices (since only one expert is activated)
_, topk_indices = gate_probs.topk(1, dim=-1) # Take top-1
# Flatten and store activated expert indices
activated_experts.extend(topk_indices.squeeze(-1).view(-1).cpu().tolist())
hooks = []
for layer in model.model.layers:
hooks.append(layer.mlp.gate.register_forward_hook(hook_fn))
while True:
if same_seed:
set_random_seed(init_seed)
else:
init_seed = set_random_seed()
print(f"\nnothink: {nothink}")
print(f"skip_prompt: {skip_prompt}")
print(f"skip_special_tokens: {skip_special_tokens}")
print(f"do_sample: {do_sample}")
print(f"same_seed: {same_seed}, {init_seed}\n")
user_input = input("User: ").strip()
if user_input.lower() == "/exit":
print("Exiting chat.")
break
if user_input.lower() == "/clear":
messages = []
print("Chat history cleared. Starting a new conversation.")
continue
if user_input.lower() == "/nothink":
nothink = not nothink
continue
if user_input.lower() == "/skip_prompt":
skip_prompt = not skip_prompt
continue
if user_input.lower() == "/skip_special_tokens":
skip_special_tokens = not skip_special_tokens
continue
if user_input.lower().startswith("/same_seed"):
parts = user_input.split()
if len(parts) == 1: # /same_seed (no number)
same_seed = not same_seed # Toggle switch
elif len(parts) == 2: # /same_seed <number>
try:
init_seed = int(parts[1]) # Extract and convert number to int
same_seed = True
except ValueError:
print("Error: Please provide a valid integer after /same_seed")
continue
if user_input.lower() == "/do_sample":
do_sample = not do_sample
continue
if not user_input:
print("Input cannot be empty. Please enter something.")
continue
messages.append({"role": "user", "content": user_input})
activated_experts = []
response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 320960)
print("\n\nMetrics:")
for key, value in metrics.items():
print(f" {key}: {value}")
# Count the frequency of each activated expert
expert_counts = Counter(activated_experts)
# Print activation statistics
print("\nActivated Expert Statistics:")
for expert_idx, count in sorted(expert_counts.items()):
print(f"Expert {expert_idx}: {count} times")
print("", flush=True)
if stop_flag:
continue
messages.append({"role": "assistant", "content": response})
# Remove all hooks after inference
for h in hooks: h.remove()
```
## Applications
- **Text Generation: Articles**, dialogues, and creative writing.
- **Question Answering**: Information retrieval and query resolution.
- **Conversational AI**: Multi-turn dialogues for chatbots.
- **Research**: Exploration of MoE architectures and efficient model scaling.
## Limitations
- **Fine-Tuning Required**: Randomly initialized gating weights may lead to suboptimal expert utilization without fine-tuning.
- **Compatibility**: Developed with transformers 4.52.4; ensure matching versions to avoid loading issues.
- **Inference Speed**: While efficient for an MoE model, performance depends on hardware (GPU recommended).
## Ethical Considerations
- **Bias**: Inherits potential biases from the Qwen3-0.6B base model; users should evaluate outputs for fairness.
- **Usage**: Intended for research and responsible applications; avoid generating harmful or misleading content.
## Contact
- **Developer**: huihui.ai
- **Repository**: huihui-ai/Huihui-MoE-1.2B-A0.6B (available locally or on Hugging Face)
- **Issues**: Report bugs or request features via the repository or please send an email to [email protected]
## Acknowledgments
- Built upon the Qwen3-0.6B model by the Qwen team.
- Built upon the Experts model by the Suayptalha team.
- Powered by the Hugging Face transformers library. |
maily101102/translution | maily101102 | 2025-06-16T03:45:45Z | 0 | 0 | null | [
"safetensors",
"gguf",
"llama",
"unsloth",
"license:llama3.1",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T03:07:51Z | ---
license: llama3.1
tags:
- unsloth
---
|
New-tutorial-kayla-viral-video/FULL.VIDEO.kayla.Viral.Video.Tutorial.Official | New-tutorial-kayla-viral-video | 2025-06-16T03:44:39Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:44:21Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.5_epoch1 | MinaMila | 2025-06-16T03:43:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:41:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.5_epoch2 | MinaMila | 2025-06-16T03:40:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:38:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Contact
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fujiba/klarsky-dust-spot-detector-experimental | fujiba | 2025-06-16T03:39:18Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-06-15T14:28:50Z | ---
title: KlarSky(Experimental)
emoji: ✈️
colorFrom: "#A61414"
colorTo: "#F2380F"
pinned: false
license: cc-by-nc-4.0
---
|
soundTeam/Q3-8B-Kintsugi_mlx-4bpw | soundTeam | 2025-06-16T03:37:58Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3",
"mergekit",
"axolotl",
"unsloth",
"roleplay",
"conversational",
"text-generation",
"dataset:PygmalionAI/PIPPA",
"dataset:Alfitaria/nemotron-ultra-reasoning-synthkink",
"dataset:PocketDoc/Dans-Prosemaxx-Gutenberg",
"dataset:FreedomIntelligence/Medical-R1-Distill-Data",
"dataset:cognitivecomputations/SystemChat-2.0",
"dataset:allenai/tulu-3-sft-personas-instruction-following",
"dataset:kalomaze/Opus_Instruct_25k",
"dataset:simplescaling/s1K-claude-3-7-sonnet",
"dataset:ai2-adapt-dev/flan_v2_converted",
"dataset:grimulkan/theory-of-mind",
"dataset:grimulkan/physical-reasoning",
"dataset:nvidia/HelpSteer3",
"dataset:nbeerbower/gutenberg2-dpo",
"dataset:nbeerbower/gutenberg-moderne-dpo",
"dataset:nbeerbower/Purpura-DPO",
"dataset:antiven0m/physical-reasoning-dpo",
"dataset:allenai/tulu-3-IF-augmented-on-policy-70b",
"dataset:NobodyExistsOnTheInternet/system-message-DPO",
"base_model:allura-org/Q3-8B-Kintsugi",
"base_model:quantized:allura-org/Q3-8B-Kintsugi",
"license:apache-2.0",
"4-bit",
"region:us"
] | text-generation | 2025-06-16T03:35:00Z | ---
license: apache-2.0
base_model: allura-org/Q3-8B-Kintsugi
library_name: mlx
tags:
- mergekit
- axolotl
- unsloth
- roleplay
- conversational
- mlx
datasets:
- PygmalionAI/PIPPA
- Alfitaria/nemotron-ultra-reasoning-synthkink
- PocketDoc/Dans-Prosemaxx-Gutenberg
- FreedomIntelligence/Medical-R1-Distill-Data
- cognitivecomputations/SystemChat-2.0
- allenai/tulu-3-sft-personas-instruction-following
- kalomaze/Opus_Instruct_25k
- simplescaling/s1K-claude-3-7-sonnet
- ai2-adapt-dev/flan_v2_converted
- grimulkan/theory-of-mind
- grimulkan/physical-reasoning
- nvidia/HelpSteer3
- nbeerbower/gutenberg2-dpo
- nbeerbower/gutenberg-moderne-dpo
- nbeerbower/Purpura-DPO
- antiven0m/physical-reasoning-dpo
- allenai/tulu-3-IF-augmented-on-policy-70b
- NobodyExistsOnTheInternet/system-message-DPO
pipeline_tag: text-generation
---
# soundTeam/Q3-8B-Kintsugi_mlx-4bpw
This model [soundTeam/Q3-8B-Kintsugi_mlx-4bpw](https://huggingface.co/soundTeam/Q3-8B-Kintsugi_mlx-4bpw) was
converted to MLX format from [allura-org/Q3-8B-Kintsugi](https://huggingface.co/allura-org/Q3-8B-Kintsugi)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("soundTeam/Q3-8B-Kintsugi_mlx-4bpw")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
huihui-ai/Huihui-MoE-1B-A0.6B | huihui-ai | 2025-06-16T03:37:56Z | 31 | 1 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"moe",
"conversational",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T07:05:15Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen3-0.6B
- suayptalha/Qwen3-0.6B-Code-Expert
- suayptalha/Qwen3-0.6B-Math-Expert
- suayptalha/Qwen3-0.6B-Medical-Expert
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- moe
---
# huihui-ai/Huihui-MoE-1B-A0.6B
## Model Overview
Huihui-MoE-1B-A0.6B is a **Mixture of Experts (MoE)** language model developed by **huihui.ai**, built upon the **[Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)** base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 3 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications.
This version does not support ollama because tie_word_embeddings=True results in the absence of lm_head parameters being saved; therefore, ollama cannot be used. If ollama support is required, please choose the latest version [huihui-ai/Huihui-MoE-1.2B-A0.6B](https://huggingface.co/huihui-ai/Huihui-MoE-1.2B-A0.6B).
- **Architecture**: Qwen3MoeForCausalLM model with 3 experts per layer (num_experts=3), activating 1 expert per token (num_experts_per_tok=1).
- **Total Parameters**: ~1.1 billion (1B)
- **Activated Parameters**: ~0.62 billion (0.6B) during inference, comparable to Qwen3-0.6B
- **Developer**: huihui.ai
- **Release Date**: June 2025
- **License**: Inherits the license of the Qwen3 base model (apache-2.0)
## Expert Models:
### Coding:
[suayptalha/Qwen3-0.6B-Code-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Code-Expert)
This model was fully fine-tuned with BF16 on first 20k rows of `nvidia/OpenCodeReasoning` dataset for 1 epoch.
### Math:
[suayptalha/Qwen3-0.6B-Math-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Math-Expert)
This model was fully fine-tuned with BF16 on entire `unsloth/OpenMathReasoning-mini` dataset for 1 epoch.
### Medical:
[suayptalha/Qwen3-0.6B-Medical-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Medical-Expert)
This model was fully fine-tuned with BF16 on first 20k rows of `FreedomIntelligence/medical-o1-reasoning-SFT` dataset for 1 epoch.
### Instruction Following:
[Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
`Qwen/Qwen3-0.6B` model was directly used for this expert, no fine-tune was applied.
## Training
- **Base Model**: Qwen3-0.6B, pre-trained by the Qwen team, Experts, pre-trained by the Suayptalha team.
- **Conversion**: The model copies embeddings, self-attention, and normalization weights from Qwen3-0.6B, replacing MLP layers with MoE layers (3 experts). Gating weights are randomly initialized.
- **Fine-Tuning**: Not fine-tuned; users are recommended to fine-tune for specific tasks to optimize expert routing. The fine-tuned version is already available and can be referred to as [huihui-ai/Huihui-MoE-1B-A0.6B-SFT](https://huggingface.co/huihui-ai/Huihui-MoE-1B-A0.6B-SFT).
## Usage
```
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
import torch
import os
import signal
cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)
print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
# Load the model and tokenizer
NEW_MODEL_ID = "huihui-ai/Huihui-MoE-1B-A0.6B"
print(f"Load Model {NEW_MODEL_ID} ... ")
quant_config_4 = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
)
model = AutoModelForCausalLM.from_pretrained(
NEW_MODEL_ID,
device_map="auto",
trust_remote_code=True,
#quantization_config=quant_config_4,
torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
initial_messages = [{"role": "system", "content": "You are a helpful assistant."}]
messages = initial_messages.copy()
enable_thinking = True
skip_prompt=True
skip_special_tokens=True
class CustomTextStreamer(TextStreamer):
def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
self.generated_text = ""
self.stop_flag = False
def on_finalized_text(self, text: str, stream_end: bool = False):
self.generated_text += text
print(text, end="", flush=True)
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
def generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, max_new_tokens):
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
enable_thinking = enable_thinking,
add_generation_prompt=True,
return_tensors="pt"
)
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
tokens = input_ids.to(model.device)
attention_mask = attention_mask.to(model.device)
streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
def signal_handler(sig, frame):
streamer.stop_generation()
print("\n[Generation stopped by user with Ctrl+C]")
signal.signal(signal.SIGINT, signal_handler)
print("Response: ", end="", flush=True)
try:
generated_ids = model.generate(
tokens,
attention_mask=attention_mask,
#use_cache=False,
max_new_tokens=max_new_tokens,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
streamer=streamer
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del input_ids, attention_mask
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag
while True:
user_input = input("User: ").strip()
if user_input.lower() == "/exit":
print("Exiting chat.")
break
if user_input.lower() == "/clear":
messages = initial_messages.copy()
print("Chat history cleared. Starting a new conversation.")
continue
if user_input.lower() == "/nothink":
if enable_thinking:
enable_thinking = False
print("Thinking = False.")
else:
enable_thinking = True
print("Thinking = True.")
continue
if user_input.lower() == "/skip_prompt":
if skip_prompt:
skip_prompt = False
print("skip_prompt = False.")
else:
skip_prompt = True
print("skip_prompt = True.")
continue
if user_input.lower() == "/skip_special_tokens":
if skip_special_tokens:
skip_special_tokens = False
print("skip_special_tokens = False.")
else:
skip_special_tokens = True
print("skip_special_tokens = True.")
continue
if not user_input:
print("Input cannot be empty. Please enter something.")
continue
messages.append({"role": "user", "content": user_input})
response, stop_flag = generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, 14192)
print("", flush=True)
if stop_flag:
continue
messages.append({"role": "assistant", "content": response})
```
## Applications
- **Text Generation: Articles**, dialogues, and creative writing.
- **Question Answering**: Information retrieval and query resolution.
- **Conversational AI**: Multi-turn dialogues for chatbots.
- **Research**: Exploration of MoE architectures and efficient model scaling.
## Limitations
- **Fine-Tuning Required**: Randomly initialized gating weights may lead to suboptimal expert utilization without fine-tuning.
- **Compatibility**: Developed with transformers 4.52.4; ensure matching versions to avoid loading issues.
- **Inference Speed**: While efficient for an MoE model, performance depends on hardware (GPU recommended).
## Ethical Considerations
- **Bias**: Inherits potential biases from the Qwen3-0.6B base model; users should evaluate outputs for fairness.
- **Usage**: Intended for research and responsible applications; avoid generating harmful or misleading content.
## Contact
- **Developer**: huihui.ai
- **Repository**: huihui-ai/Huihui-MoE-1B-A0.6B (available locally or on Hugging Face)
- **Issues**: Report bugs or request features via the repository or please send an email to [email protected]
## Acknowledgments
- Built upon the Qwen3-0.6B model by the Qwen team.
- Built upon the Experts model by the Suayptalha team.
- Powered by the Hugging Face transformers library. |
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.5_epoch1 | MinaMila | 2025-06-16T03:32:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:30:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.75_epoch1 | MinaMila | 2025-06-16T03:29:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:28:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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New-tutorial-Bhumi-Ahir-viral-video/FULL.VIDEO.Bhumi.Ahir.Viral.Video.Tutorial.Official | New-tutorial-Bhumi-Ahir-viral-video | 2025-06-16T03:29:15Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:28:56Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.75_epoch2 | MinaMila | 2025-06-16T03:24:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:22:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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The-Welcomer/cluster-test-unbiased | The-Welcomer | 2025-06-16T03:23:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:15:33Z | ---
base_model: unsloth/qwen3-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** The-Welcomer
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-8b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.05_epoch2 | MinaMila | 2025-06-16T03:22:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:21:04Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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New-tutorial-Chitra-Tripathi-viral-video/FULL.VIDEO.Chitra.Tripathi.Viral.Video.Tutorial.Official | New-tutorial-Chitra-Tripathi-viral-video | 2025-06-16T03:21:02Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:20:43Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.05_epoch1 | MinaMila | 2025-06-16T03:16:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:14:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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quidangz/LlamaNER-8B-Instruct-ZeroShot | quidangz | 2025-06-16T03:14:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"transformer",
"classification",
"token-classification",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-06-16T03:00:40Z | ---
license: apache-2.0
language:
- en
base_model:
- unsloth/Meta-Llama-3.1-8B-Instruct
pipeline_tag: token-classification
library_name: transformers
tags:
- transformer
- classification
---
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('quidangz/LlamaNER-8B-Instruct-ZeroShot')
model = AutoModelForCausalLM.from_pretrained(
'quidangz/LlamaNER-8B-Instruct-ZeroShot',
torch_dtype="auto",
device_map="cuda",
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = model.config.eos_token_id
user_prompt = """
Extract entities from the text **strictly using ONLY the provided Entity List** below and **MUST** strictly adhere to the output format.
Format output as '<entity tag>: <entity name>' and separated multiple entities by '|'. Return 'None' if no entities are identified.
Entity List: {ner_labels}
Text: {text}
"""
query = 'Hence, quercetin effectively reversed NAFLD symptoms by decreased triacyl glycerol accumulation, insulin resistance, inflammatory cytokine secretion and increased cellular antioxidants in OA induced hepatic steatosis in HepG2 cells.'
ner_labels = ['Chemical']
user_prompt = user_prompt.format(ner_labels=ner_labels, text=query)
messages = [
{
"role": "system",
"content": "You are an expert in Named Entity Recognition (NER) task."
},
{
"role": "user",
"content": user_prompt
}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer(text, return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512,
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response) # Chemical: quercetin | Chemical: triacyl glycerol
```
## Contact
**Email**: [email protected]
**LinkedIn**: [Qui Dang](https://www.linkedin.com/in/qui-dang-4b6089285/)
**Facebook**: [Đặng Bá Qúi](https://www.facebook.com/angbaqui/)
## Citation
Please cite as
```Plaintext
@misc{LlamaNER-8B-Instruct-ZeroShot,
title={LlamaNER: An Large Language Model for Named Entity Recognition},
author={Qui Dang Ba},
year={2025},
publisher={Huggingface},
}
``` |
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.05_epoch2 | MinaMila | 2025-06-16T03:08:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:06:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
18-video-filtrado-anahi-antonella-video/Ver.video.filtrado.anahi.antonella.video.completo.anahi.antonella.filtrado.clip | 18-video-filtrado-anahi-antonella-video | 2025-06-16T03:03:01Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:02:39Z | <animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
erdem-erdem/Qwen2.5-3B-Instruct-ps-grpo-r32 | erdem-erdem | 2025-06-16T03:01:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:00:18Z | ---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** erdem-erdem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ALYTV/DeepCoder-14B-Preview-mlx-3Bit | ALYTV | 2025-06-16T02:59:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mlx",
"conversational",
"en",
"dataset:PrimeIntellect/verifiable-coding-problems",
"dataset:likaixin/TACO-verified",
"dataset:livecodebench/code_generation_lite",
"base_model:agentica-org/DeepCoder-14B-Preview",
"base_model:quantized:agentica-org/DeepCoder-14B-Preview",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"3-bit",
"region:us"
] | text-generation | 2025-06-16T02:58:44Z | ---
license: mit
library_name: transformers
datasets:
- PrimeIntellect/verifiable-coding-problems
- likaixin/TACO-verified
- livecodebench/code_generation_lite
language:
- en
base_model: agentica-org/DeepCoder-14B-Preview
pipeline_tag: text-generation
tags:
- mlx
---
# ALYTV/DeepCoder-14B-Preview-mlx-3Bit
The Model [ALYTV/DeepCoder-14B-Preview-mlx-3Bit](https://huggingface.co/ALYTV/DeepCoder-14B-Preview-mlx-3Bit) was converted to MLX format from [agentica-org/DeepCoder-14B-Preview](https://huggingface.co/agentica-org/DeepCoder-14B-Preview) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("ALYTV/DeepCoder-14B-Preview-mlx-3Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.15_epoch2 | MinaMila | 2025-06-16T02:52:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T02:50:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
glif-loradex-trainer/Angelo-ec24_d00d | glif-loradex-trainer | 2025-06-16T02:47:57Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us",
"flux",
"lora",
"base_model:adapter:black-forest-labs/FLUX.1-dev"
] | text-to-image | 2025-06-16T02:47:43Z | ---
tags:
- diffusers
- text-to-image
- template:sd-lora
- base_model:black-forest-labs/FLUX.1-dev
- base_model:finetune:black-forest-labs/FLUX.1-dev
- license:other
- region:us
- flux
- lora
widget:
- output:
url: samples/1750041998772__000000500_0.jpg
text: d00d columns
- output:
url: samples/1750042024106__000000500_1.jpg
text: d00d top
- output:
url: samples/1750042049468__000000500_2.jpg
text: d00d messy
base_model: black-forest-labs/FLUX.1-dev
trigger: "d00d"
instance_prompt: "d00d"
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# d00d
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `Angelo-ec24`.
<Gallery />
## Trigger words
You should use `d00d` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/glif-loradex-trainer/Angelo-ec24_d00d/tree/main) them in the Files & versions tab.
## License
This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
FyTian/LLama-1b_Fin-sentiment-v1 | FyTian | 2025-06-16T02:46:47Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-16T02:30:39Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0 |
Yuichi1218/Llama-3.1-tetun-8B-instruct-e3 | Yuichi1218 | 2025-06-16T02:44:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T02:39:12Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Yuichi1218
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
chnaaam/minimaxi | chnaaam | 2025-06-16T02:42:07Z | 165 | 0 | transformers | [
"transformers",
"safetensors",
"ovis",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-06-10T16:03:04Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
freakyfractal/othan | freakyfractal | 2025-06-16T02:40:23Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-06-16T02:40:05Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/Coinye_2021.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# othan
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/freakyfractal/othan/tree/main) them in the Files & versions tab.
|
Johnyquest7/Genai_onnx | Johnyquest7 | 2025-06-16T02:39:55Z | 0 | 0 | null | [
"onnx",
"text-generation",
"conversational",
"en",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-1B-Instruct",
"region:us"
] | text-generation | 2025-06-16T02:35:15Z | ---
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
pipeline_tag: text-generation
---
```python
import onnxruntime_genai as og
model = og.Model('soap5_onnx')
tokenizer = og.Tokenizer(model)
tokenizer_stream = tokenizer.create_stream()
# Search options - exact match to original
search_options = {
'max_length': 4096,
'temperature': 0.1,
'top_p': 0.9,
'do_sample': True,
'batch_size': 1
}
soap_note_prompt = """You are an expert medical professor assisting in the creation of medically accurate SOAP summaries.
Please ensure the response follows the structured format: S:, O:, A:, P: without using markdown or special formatting.
Create a Medical SOAP note summary from the dialogue, following these guidelines:\n
S (Subjective): Summarize the patient's reported symptoms, including chief complaint and relevant history.
Rely on the patient's statements as the primary source and ensure standardized terminology.\n
O (Objective): Highlight critical findings such as vital signs, lab results, and imaging, emphasizing important details like the side of the body affected and specific dosages.
Include normal ranges where relevant.\n
A (Assessment): Offer a concise assessment combining subjective and objective data. State the primary diagnosis and any differential diagnoses, noting potential complications and the prognostic outlook.\n
P (Plan): Outline the management plan, covering medication, diet, consultations, and education. Ensure to mention necessary referrals to other specialties and address compliance challenges.\n
Considerations: Compile the report based solely on the transcript provided. Use concise medical jargon and abbreviations for effective doctor communication.\n
Please format the summary in a clean, simple list format without using markdown or bullet points. Use 'S:', 'O:', 'A:', 'P:' directly followed by the text. Avoid any styling or special characters.
TRANSCRIPT: \n"""
text = input("Input: ")
if not text:
print("Error, input cannot be empty")
exit()
# Method 1: Force generation by adding a SOAP starter after the prompt
full_prompt = soap_note_prompt + text
# Use the most complete Llama format
chat_template = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\nS: "
prompt = chat_template.format(prompt=full_prompt)
input_tokens = tokenizer.encode(prompt)
print(f"Tokens in prompt: {len(input_tokens)}")
params = og.GeneratorParams(model)
params.set_search_options(**search_options)
generator = og.Generator(model, params)
generator.append_tokens(input_tokens)
print("\nGenerating SOAP note...")
print("S: ", end='', flush=True) # We already have "S: " in the prompt
# Generate the rest of the SOAP note
generated_text = ""
token_count = 0
try:
while not generator.is_done() and token_count < 2000: # Limit to 2000 tokens for safety
generator.generate_next_token()
new_token = generator.get_next_tokens()[0]
decoded = tokenizer_stream.decode(new_token)
# Skip if we're still in the input echo phase
if token_count < 50 and (text[:20] in generated_text + decoded):
token_count += 1
continue
print(decoded, end='', flush=True)
generated_text += decoded
token_count += 1
# Stop if we see end markers
if any(marker in decoded for marker in ["<|eot_id|>", "<|end_of_text|>", "</s>"]):
break
except KeyboardInterrupt:
print("\nInterrupted")
print()
# If that didn't work, try Method 2: Different prompt structure
if len(generated_text.strip()) < 50 or text[:50] in generated_text:
print("\n\nMethod 1 didn't work well. Trying alternative method...")
del generator # Clean up
# Try a simpler approach - maybe the model expects a different format
simple_prompt = f"{soap_note_prompt}{text}\n\nSOAP Note:\nS: "
input_tokens = tokenizer.encode(simple_prompt)
params = og.GeneratorParams(model)
params.set_search_options(**search_options)
generator = og.Generator(model, params)
generator.append_tokens(input_tokens)
print("\nGenerating with simplified format...")
print("S: ", end='', flush=True)
generated_text = ""
token_count = 0
try:
while not generator.is_done() and token_count < 2000:
generator.generate_next_token()
new_token = generator.get_next_tokens()[0]
decoded = tokenizer_stream.decode(new_token)
print(decoded, end='', flush=True)
generated_text += decoded
token_count += 1
if any(marker in decoded for marker in ["<|eot_id|>", "<|end_of_text|>", "</s>"]):
break
except KeyboardInterrupt:
print("\nInterrupted")
print()
del generator
print("\n--- Generation Complete ---")
''' |
picard47at/punctuation_1350_1.7B_levdist | picard47at | 2025-06-16T02:39:07Z | 94 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-11T03:23:38Z | ---
base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit
library_name: transformers
model_name: punctuation_1350_1.7B_levdist
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for punctuation_1350_1.7B_levdist
This model is a fine-tuned version of [unsloth/qwen3-1.7b-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-1.7b-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="picard47at/punctuation_1350_1.7B_levdist", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/picardtseng-pesi/punctuation_1350_1.7B_levdist/runs/sv9hcyd7)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
pettankoutei/Loras | pettankoutei | 2025-06-16T02:38:34Z | 0 | 13 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"license:cc-by-nc-sa-4.0",
"region:us"
] | text-to-image | 2024-03-14T18:19:06Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
license: cc-by-nc-sa-4.0
inference: false
---
<style>
.title {
font-size: 3.5em;
text-align: center;
color: #333;
font-family: "lucida sans unicode", "lucida grande", sans-serif;
font-style: italic;
font-weight: bold;
font-variant: small-caps;
letter-spacing: 0.05em;
padding: 0.5em 0;
background: transparent;
}
.title span {
background: -webkit-linear-gradient(45deg, #99E2FF, #FF5C95);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.image-container {
display: flex;
justify-content: center; /* Center the images */
gap: 10px; /* Adjust the gap between images */
position: relative; /* Position relative for absolute positioning of children */
}
.image-wrapper {
position: relative; /* Position relative for the wrapper */
overflow: visible; /* Allow overflow for scaling */
width: 30%; /* Set a fixed width for the wrapper */
}
.image-wrapper img {
width: 100%; /* Make the image fill the wrapper */
height: auto; /* Maintain aspect ratio */
transition: transform 0.3s ease; /* Smooth transition for the image */
position: relative; /* Position relative for z-index control */
z-index: 0; /* Default z-index */
border-radius: 15px; /* Rounded corners */
}
.image-wrapper:hover img {
transform: scale(1.8); /* Scale effect on hover */
z-index: 10; /* Bring the hovered image to the front */
}
</style>
<h1 class="title">
<span>PettanKoutei Lora Repo</span>
</h1>
<div class="image-container">
<div class="image-wrapper">
<a href="https://www.buymeacoffee.com/pettankoutei" target="_blank"><img class="custom-button" src="https://huggingface.co/pettankoutei/Loras/resolve/main/bmac_button.png" alt="Buy me a cookie!" /></a>
</div>
</div>
> Support me on Buy Me a Coffee! for early access of new content and exclusive models!
## About
This is a Lora repository of all my released Loras for _Stable Diffusion 1.5_ and _Pony Diffusion v6 XL_. They will be organized in folders and showcased in this model card for easy access.
Any model marked as "Early Access" are available only for supporters and members [here](https://www.buymeacoffee.com/pettankoutei).
---
### Youyueyuee Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: y0uyu3yu33
* Recommended weight: 1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/youyueyuee-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/youyueyuee-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/youyueyuee-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
### Kuroshiro Style v2
* Base model: Pony Diffusion v6 XL
* Trigger word: kur0sh1r0
* Recommended weight: 0.9
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/kuroshiro-v2/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/kuroshiro-v2/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/kuroshiro-v2/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
### Agwing Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: None
* Recommended weight: 1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/agwing-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/agwing-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/agwing-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
### Ama Mitsuki Style v2
* Base model: Pony Diffusion v6 XL
* Trigger word: 4m4_m1tsuk1
* Recommended weight: 1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/amamitsuki-v2/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/amamitsuki-v2/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/amamitsuki-v2/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Ama Mitsuki Style v1</summary>
* Base model: Pony Diffusion v6 XL
* Trigger word: None
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/amamitsuki-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/amamitsuki-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/amamitsuki-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
<details>
<summary>Ama Mitsuki Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: None
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/amamitsuki-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/amamitsuki-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/amamitsuki-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Doroshe Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: None
* Recommended weight: 1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/doroshe-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/doroshe-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/doroshe-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Doroshe Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: None
* Recommended weight: 0.8
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/doroshe-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/doroshe-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/doroshe-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Kokudou Juunigou Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: None
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/kokudou-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/kokudou-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/kokudou-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Kokudou Juunigou Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: None
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/kokudou-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/kokudou-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/kokudou-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Sweetonedollar Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: None
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/sweetonedollar-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/sweetonedollar-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/sweetonedollar-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Sweetonedollar Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: None
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/sweetonedollar-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/sweetonedollar-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/sweetonedollar-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Dorontabi Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: d0r0n (recent style); d0r0nsk3tch (sketch and rougher line art); d0r0n0ld (older coloring style)
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/dorontabi-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/dorontabi-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/dorontabi-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Dorontabi Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: d0r0n (recent style); d0r0nsk3tch (sketch and rougher line art); d0r0n0ld (older coloring style)
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/dorontabi-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/dorontabi-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/dorontabi-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Tearontaron Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/tearontaron-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/tearontaron-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/tearontaron-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Tearontaron Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/tearontaron-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/tearontaron-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/tearontaron-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Higashiyama Shou Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: h1g4sh1y4m4 (Style 1); p0l4r01d (Style 2); white border, dated (Optional tags for polaroid border)
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/higashiyama-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/higashiyama-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/higashiyama-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Higashiyama Shou Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: h1g4sh1y4m4 (Style 1); p0l4r01d (Style 2); white border, dated (Optional tags for polaroid border)
* Recommended weight: 0.6-0.8
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/higashiyama-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/higashiyama-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/higashiyama-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Henreader Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.8-1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/henreader-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/henreader-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/henreader-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Henreader Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.9
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/henreader-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/henreader-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/henreader-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Kawakami Rokkaku Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 1.0
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/rokkaku-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/rokkaku-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/rokkaku-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Kawakami Rokkaku Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.8
* [Early Access](https://www.buymeacoffee.com/pettankoutei)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/rokkaku-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/rokkaku-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/rokkaku-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Chiwa Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/monchichiwa-v1/monchichiwa_lyco_pony.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/monchichiwa-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/monchichiwa-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/monchichiwa-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Chiwa Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/monchichiwa-v1/monchichiwa_lyco_nai.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/monchichiwa-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/monchichiwa-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/monchichiwa-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Otohime Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/otohime-v1/otohime_lyco-pony-v1_.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/otohime-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/otohime-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/otohime-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Otohime Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/otohime-v1/otohime_lyco-nai-v1_.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/otohime-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/otohime-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/otohime-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Aoi Kumiko Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/aoikumiko-v1/aoi_kumiko-lyco-pony.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/aoikumiko-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/aoikumiko-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/aoikumiko-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Aoi Kumiko Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/aoikumiko-v1/aoi_kumiko-lyco-nai.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/aoikumiko-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/aoikumiko-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/aoikumiko-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Shion Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/shion-v1/shion-lyco-pony-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/shion-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/shion-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/shion-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Shion Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/shion-v1/shion-lyco-nai-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/shion-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/shion-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/shion-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Takamichi Style v2
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/takamichi-v2/takamichi-lyco-pony-v2.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/takamichi-v2/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/takamichi-v2/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/takamichi-v2/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Takamichi Style v1</summary>
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/takamichi-v1/takamichi-lyco-pony-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/takamichi-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/takamichi-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/takamichi-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
<details>
<summary>Takamichi Style v2 SD15 (Stable)</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v2/takamichi_lyco-nai-v2-stable.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v2/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v2/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v2/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
<details>
<summary>Takamichi Style v2 SD15 (Strong)</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v2/takamichi_lyco-nai-v2-strong.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v2/Model_Sample_00004.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v2/Model_Sample_00005.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v2/Model_Sample_00006.png" alt="sample3">
</div>
</div>
</details>
<details>
<summary>Takamichi Style v1 SD1.5</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.6
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v1/takamichi-lora-nai-v1-full.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/takamichi-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Cedar Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/cedar-v1/cedar-lyco-pony-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/cedar-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/cedar-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/cedar-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Cedar Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/cedar-v1/cedar-lyco-nai-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/cedar-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/cedar-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/cedar-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Crumbles Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.9
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/crumbles-v1/crumbles-lyco-pony-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/crumbles-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/crumbles-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/crumbles-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Crumbles Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.9
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/crumbles-v1/crumbles-lyco-nai-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/crumbles-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/crumbles-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/crumbles-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Ririko Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.9
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/ririko-v1/ririko-lyco-pony-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/ririko-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/ririko-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/ririko-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Ririko Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/ririko-v1/ririko-lyco-nai-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/ririko-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/ririko-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/ririko-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Wagashi Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.9
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/wagashi-v1/wagashi-lyco-pony.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/wagashi-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/wagashi-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/wagashi-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Wagashi Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/wagashi-v1/wagashi-lyco-nai.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/wagashi-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/wagashi-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/wagashi-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Haguhagu Style v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/haguhagu-v1/haguhagu-lyco-pony-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/haguhagu-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/haguhagu-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/haguhagu-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Haguhagu Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/haguhagu-v1/haguhagu-lyco-nai-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/haguhagu-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/haguhagu-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/haguhagu-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
### Yamamoto Souichirou Style Mix v1
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/yamamoto-mix-v1/yamamoto-mix-lyco-pony-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/yamamoto-mix-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/yamamoto-mix-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/yamamoto-mix-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
---
<details>
<summary>Yamamoto Souichirou Style v1</summary>
* Base model: Pony Diffusion v6 XL
* Trigger word: none
* Recommended weight: 0.8
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/yamamoto-v1/yamamoto-lyco-pony-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/yamamoto-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/yamamoto-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/PonyDiff_v6/yamamoto-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
<details>
<summary>Yamamoto Souichirou Style v1 SD15</summary>
* Base model: NAI (Stable Diffusion 1.5)
* Trigger word: none
* Recommended weight: 1.0
* [Download Safetensors](https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/yamamoto-v1/yamamoto-lyco-nai-v1.safetensors)
<div class="image-container">
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/yamamoto-v1/Model_Sample_00001.png" alt="sample1">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/yamamoto-v1/Model_Sample_00002.png" alt="sample2">
</div>
<div class="image-wrapper">
<img class="custom-image" src="https://huggingface.co/pettankoutei/Loras/resolve/main/SD15/yamamoto-v1/Model_Sample_00003.png" alt="sample3">
</div>
</div>
</details>
---
## Disclamer
All the models that I have developed and published on this platform are for entertainment purposes only. They are derived from the base Stable Diffusion model, which means it also includes its inherent issues, limitations and unfiltered capabilities. With that in mind, I am in no way responsible for any content that the user creates with malicious intent while using any of these models. The user assumes complete responsibility for the misuse of these tools and any unethical content created with them. This disclaimer is subject to the laws of the United States of America and the state of California. I reserve the right to remove or report any content that violates the terms of service, ethical standards, or applicable laws.
## License conditions
All models available in this repository are licensed under [Attribution-NonCommercial-ShareAlike 4.0 International](http://creativecommons.org/licenses/by-nc-sa/4.0/?ref=chooser-v1). |
gradientrouting-spar/mc14_badmed_kl_div_dsd-5_msd-5_beta_kl-3_seed_1 | gradientrouting-spar | 2025-06-16T02:35:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T02:35:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
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player1537/Jan-nano-4bit | player1537 | 2025-06-16T02:35:31Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Menlo/Jan-nano",
"base_model:quantized:Menlo/Jan-nano",
"license:apache-2.0",
"4-bit",
"region:us"
] | text-generation | 2025-06-16T02:24:08Z | ---
license: apache-2.0
base_model: Menlo/Jan-nano
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
---
# player1537/Jan-nano-4bit
This model [player1537/Jan-nano-4bit](https://huggingface.co/player1537/Jan-nano-4bit) was
converted to MLX format from [Menlo/Jan-nano](https://huggingface.co/Menlo/Jan-nano)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("player1537/Jan-nano-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
dgambettaphd/M_llm2_run2_gen7_WXS_doc1000_synt64_lr1e-04_acm_FRESH | dgambettaphd | 2025-06-16T02:31:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T02:31:16Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.75_epoch2 | MinaMila | 2025-06-16T02:28:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T02:26:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Amploud/Aika | Amploud | 2025-06-16T02:27:36Z | 0 | 0 | fastai | [
"fastai",
"causal_lm",
"en",
"dataset:open-r1/Mixture-of-Thoughts",
"base_model:ResembleAI/chatterbox",
"base_model:finetune:ResembleAI/chatterbox",
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T01:25:57Z | ---
license: apache-2.0
datasets:
- open-r1/Mixture-of-Thoughts
language:
- en
metrics:
- character
base_model:
- ResembleAI/chatterbox
new_version: ResembleAI/chatterbox
library_name: fastai
---
# Amploud/Aika
> **Aika**: your uncensored NSFW AI companion.
**License:** Apache-2.0
**Pipeline:** text-generation
**Language:** en
## Usage
Once deployed as an HF Inference endpoint, you can:
```bash
curl -X POST \
-H "Authorization: Bearer $HF_API_KEY" \
-H "Content-Type: application/json" \
https://api-inference.huggingface.co/models/Amploud/Aika \
-d '{"inputs": "Hello Aika, tell me something lewd.", "parameters": {"max_new_tokens":150}}'
``` |
mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF | mradermacher | 2025-06-16T02:27:10Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"en",
"base_model:huihui-ai/Huihui-MoE-46B-A14B-abliterated",
"base_model:quantized:huihui-ai/Huihui-MoE-46B-A14B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T19:58:56Z | ---
base_model: huihui-ai/Huihui-MoE-46B-A14B-abliterated
extra_gated_prompt: |-
**Usage Warnings**
“**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
“**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
“**Legal and Ethical Responsibilities**“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
“**Research and Experimental Use**“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
“**Monitoring and Review Recommendations**“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
“**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE
quantized_by: mradermacher
tags:
- moe
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/huihui-ai/Huihui-MoE-46B-A14B-abliterated
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q2_K.gguf) | Q2_K | 17.5 | |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q3_K_S.gguf) | Q3_K_S | 20.5 | |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q3_K_M.gguf) | Q3_K_M | 22.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q3_K_L.gguf) | Q3_K_L | 24.5 | |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.IQ4_XS.gguf) | IQ4_XS | 25.5 | |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q4_K_S.gguf) | Q4_K_S | 26.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q5_K_S.gguf) | Q5_K_S | 32.4 | |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q5_K_M.gguf) | Q5_K_M | 33.4 | |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q6_K.gguf) | Q6_K | 38.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-46B-A14B-abliterated-GGUF/resolve/main/Huihui-MoE-46B-A14B-abliterated.Q8_0.gguf) | Q8_0 | 49.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
New-tutorial-a2z-jankari-viral-video/FULL.VIDEO.a2z.jankari.Viral.Video.Tutorial.Official | New-tutorial-a2z-jankari-viral-video | 2025-06-16T02:27:00Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T02:25:59Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
freakyfractal/than | freakyfractal | 2025-06-16T02:26:36Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-06-16T02:26:18Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/Coinye_2021.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# than
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/freakyfractal/than/tree/main) them in the Files & versions tab.
|
BootesVoid/cmby9kccd034zrdqsdcojt2un_cmbyfg7nh03idrdqs767cnkrw | BootesVoid | 2025-06-16T02:26:15Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-16T02:26:13Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: MODELBODY
---
# Cmby9Kccd034Zrdqsdcojt2Un_Cmbyfg7Nh03Idrdqs767Cnkrw
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `MODELBODY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MODELBODY",
"lora_weights": "https://huggingface.co/BootesVoid/cmby9kccd034zrdqsdcojt2un_cmbyfg7nh03idrdqs767cnkrw/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmby9kccd034zrdqsdcojt2un_cmbyfg7nh03idrdqs767cnkrw', weight_name='lora.safetensors')
image = pipeline('MODELBODY').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmby9kccd034zrdqsdcojt2un_cmbyfg7nh03idrdqs767cnkrw/discussions) to add images that show off what you’ve made with this LoRA.
|
GeorgeGali06/mi-super-modelo | GeorgeGali06 | 2025-06-16T02:25:24Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T02:25:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
GeorgeGali06/mi-super-modelo2 | GeorgeGali06 | 2025-06-16T02:24:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-16T02:07:04Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mi-super-modelo2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mi-super-modelo2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5339
- Accuracy: 0.275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5229 | 0.5 | 5 | 1.5633 | 0.225 |
| 1.6001 | 1.0 | 10 | 1.5339 | 0.275 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Delta-Vector/Plesio-32B | Delta-Vector | 2025-06-16T02:23:18Z | 17 | 1 | transformers | [
"transformers",
"safetensors",
"glm4",
"text-generation",
"mergekit",
"merge",
"rolelplay",
"creative_writing",
"conversational",
"base_model:Delta-Vector/Rei-V1-32B-Base",
"base_model:merge:Delta-Vector/Rei-V1-32B-Base",
"base_model:allura-org/GLM4-32B-Neon-v2",
"base_model:merge:allura-org/GLM4-32B-Neon-v2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-23T23:00:39Z | ---
base_model:
- Delta-Vector/Rei-V1-32B-Base
- allura-org/GLM4-32B-Neon-v2
library_name: transformers
tags:
- mergekit
- merge
- rolelplay
- creative_writing
---
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Plesio-32B</title>
<link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet">
<style>
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #e6f3ff 0%, #cce7ff 50%, #b3daff 100%);
color: #1e3a5f;
margin: 0;
padding: 0;
font-size: 16px;
min-height: 100vh;
}
.container {
margin: 20px;
background: linear-gradient(145deg, rgba(255, 255, 255, 0.9), rgba(240, 248, 255, 0.95));
padding: 30px;
border-radius: 20px;
box-shadow: 0 8px 32px rgba(70, 130, 180, 0.3), 0 4px 16px rgba(0, 123, 191, 0.2);
border: 2px solid rgba(70, 130, 180, 0.4);
position: relative;
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<body>
<div class="container">
<div class="header">
<h1>Plesio-32B</h1>
</div>
<div class="info">
<img src="https://files.catbox.moe/opd2nm.jpg" alt="Model banner">
<div style="text-align: center;">
<div class="creator-section">
<div class="creator-badge">
<span class="creator-label">Created by</span>
<a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link">
<span class="creator-name">Delta-Vector</span>
<span class="creator-arrow">→</span>
</a>
</div>
</div>
<div class="model-info">
<h2>Model Information</h2>
<div class="info-card">
<div class="info-header">
<h3>Plesio-32B</h3>
<div class="model-tags">
<span class="model-tag">32B parameters</span>
<span class="model-tag">GLM-4 32B</span>
<span class="model-tag">Creative / Fresh Prose</span>
<span class="model-tag">Co-writing/Roleplay/Adventure Generalist</span>
</div>
</div>
<div class="model-description">
<p>Another Series of Merges! Since i could never beat Archaeo-32B-KTO! This time starting off with a GLM merge between Rei and Neon (thanks auri!!!)</p>
<p>Using the Oh-so-great 0.2 Slerp merge weight with Neon as the Base.</p>
<p>Support me on Ko-Fi: https://ko-fi.com/deltavector</p>
</div>
</div>
</div>
<div class="section">
<h2>Quantized Versions</h2>
<div class="info-card">
<div class="model-composition">
<h4>Available Downloads</h4>
<ul class="composition-list">
<li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For use with LLama.cpp & Forks(Coming Soon!)</li>
<li><span class="model-component"><a href="" target="_blank">EXL2 Format</a></span>For use with TabbyAPI (Coming Soon!)</li>
<li><span class="model-component"><a href="https://huggingface.co/Delta-Vector/Plesio-32B-exl3" target="_blank">EXL3 Format</a></span>For use with TabbyAPI (Slower on Ampere))</li>
</ul>
</div>
</div>
</div>
<div class="section">
<h2>Prompting</h2>
<p>Model has been tuned with the GLM-4 formatting.</p>
</div>
<div class="section">
<h2>Samplers</h2>
<p>For testing of this model, I used Temp=1, 0.1 Min-P.</p>
<div class="metrics-section">
<details>
<summary>See Merging Config</summary>
<pre>https://files.catbox.moe/j9kyfy.yml<code>
</code></pre>
</details>
</div>
</div>
<div class="section">
<h2>Credits</h2>
<p>Thank you to <a href="https://huggingface.co/lucyknada">Lucy Knada</a>, <a href="https://huggingface.co/AuriAetherwiing">Auri</a>, <a href="https://huggingface.co/Ateron">Ateron</a>, <a href="https://huggingface.co/AliCat2">Alicat</a>, <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a> and the rest of <a href="https://huggingface.co/anthracite-org">Anthracite</a>.</p>
</div>
</div>
</div>
</body>
</html> |
Delta-Vector/Plesio-32B-exl3 | Delta-Vector | 2025-06-16T02:22:48Z | 0 | 0 | transformers | [
"transformers",
"base_model:Delta-Vector/Plesio-32B",
"base_model:finetune:Delta-Vector/Plesio-32B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T19:12:12Z | ---
base_model:
- Delta-Vector/Plesio-32B
library_name: transformers
---
### exl3 quant
---
### check revisions for quants
--- |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.75_0.75_epoch1 | MinaMila | 2025-06-16T02:22:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T02:20:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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namdp-ptit/ViRanker | namdp-ptit | 2025-06-16T02:21:36Z | 1,324 | 14 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"cross-encoder",
"rerank",
"vi",
"base_model:BAAI/bge-m3",
"base_model:finetune:BAAI/bge-m3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-08-14T02:58:28Z | ---
language:
- vi
license: apache-2.0
library_name: transformers
tags:
- transformers
- cross-encoder
- rerank
pipeline_tag: text-classification
widget:
- text: tỉnh nào có diện tích lớn nhất việt nam
output:
- label: nghệ an có diện tích lớn nhất việt nam
score: 0.99999
- label: bắc ninh có diện tích nhỏ nhất việt nam
score: 0.0001
base_model:
- BAAI/bge-m3
---
# Reranker
* [Usage](#usage)
* [Using FlagEmbedding](#using-flagembedding)
* [Using Huggingface transformers](#using-huggingface-transformers)
* [Fine tune](#fine-tune)
* [Data format](#data-format)
* [Performance](#performance)
* [Contact](#contact)
* [Support The Project](#support-the-project)
* [Citation](#citation)
Different from embedding model, reranker uses question and document as input and directly output similarity instead of
embedding.
You can get a relevance score by inputting query and passage to the reranker.
And the score can be mapped to a float value in [0,1] by sigmoid function.
## Usage
### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('namdp-ptit/ViRanker',
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối cùng của nước ta'])
print(score) # 13.71875
# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
score = reranker.compute_score(['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối cùng của nước ta'],
normalize=True)
print(score) # 0.99999889840464
scores = reranker.compute_score(
[
['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối cùng của nước ta'],
['ai là vị vua cuối cùng của việt nam', 'lý nam đế là vị vua đầu tiên của nước ta']
]
)
print(scores) # [13.7265625, -8.53125]
# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
scores = reranker.compute_score(
[
['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối của nước ta'],
['ai là vị vua cuối cùng của việt nam', 'lý nam đế là vị vua đầu tiên của nước ta']
],
normalize=True
)
print(scores) # [0.99999889840464, 0.00019716942196222918]
```
### Using Huggingface transformers
```
pip install -U transformers
```
Get relevance scores (higher scores indicate more relevance):
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('namdp-ptit/ViRanker')
model = AutoModelForSequenceClassification.from_pretrained('namdp-ptit/ViRanker')
model.eval()
pairs = [
['ai là vị vua cuối cùng của việt nam', 'vua bảo đại là vị vua cuối cùng của nước ta'],
['ai là vị vua cuối cùng của việt nam', 'lý nam đế là vị vua đầu tiên của nước ta']
],
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Fine-tune
### Data Format
Train data should be a json file, where each line is a dict like this:
```
{"query": str, "pos": List[str], "neg": List[str]}
```
`query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts. If you have no negative
texts for a query, you can random sample some from the entire corpus as the negatives.
Besides, for each query in the train data, we used LLMs to generate hard negative for them by asking LLMs to create a
document that is the opposite one of the documents in 'pos'.
## Performance
Below is a comparision table of the results we achieved compared to some other pre-trained Cross-Encoders on
the [MS MMarco Passage Reranking - Vi - Dev](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset.
| Model Name | NDCG@3 | MRR@3 | NDCG@5 | MRR@5 | NDCG@10 | MRR@10 |
|-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| [namdp-ptit/ViRanker](https://huggingface.co/namdp-ptit/ViRanker) | **0.6815** | **0.6641** | 0.6983 | **0.6894** | 0.7302 | **0.7107** |
| [itdainb/PhoRanker](https://huggingface.co/itdainb/PhoRanker) | 0.6625 | 0.6458 | **0.7147** | 0.6731 | **0.7422** | 0.6830 |
| [kien-vu-uet/finetuned-phobert-passage-rerank-best-eval](https://huggingface.co/kien-vu-uet/finetuned-phobert-passage-rerank-best-eval) | 0.0963 | 0.0883 | 0.1396 | 0.1131 | 0.1681 | 0.1246 |
| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | 0.6087 | 0.5841 | 0.6513 | 0.6062 | 0.6872 | 0.6209 |
| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | 0.6088 | 0.5908 | 0.6446 | 0.6108 | 0.6785 | 0.6249 |
## Contact
**Email**: [email protected]
**LinkedIn**: [Dang Phuong Nam](https://www.linkedin.com/in/dang-phuong-nam-157912288/)
**Facebook**: [Phương Nam](https://www.facebook.com/phuong.namdang.7146557)
## Support The Project
If you find this project helpful and wish to support its ongoing development, here are some ways you can contribute:
1. **Star the Repository**: Show your appreciation by starring the repository. Your support motivates further
development
and enhancements.
2. **Contribute**: We welcome your contributions! You can help by reporting bugs, submitting pull requests, or
suggesting new features.
3. **Donate**: If you’d like to support financially, consider making a donation. You can donate through:
- Vietcombank: 9912692172 - DANG PHUONG NAM
Thank you for your support!
## Citation
Please cite as
```Plaintext
@misc{ViRanker,
title={ViRanker: A Cross-encoder Model for Vietnamese Text Ranking},
author={Nam Dang Phuong},
year={2024},
publisher={Huggingface},
}
``` |
gradientrouting-spar/horizontal_2_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_20250616_021058 | gradientrouting-spar | 2025-06-16T02:19:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T02:19:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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mlx-community/llm-jp-3.1-1.8b-instruct4 | mlx-community | 2025-06-16T02:18:03Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"base_model:llm-jp/llm-jp-3.1-1.8b-instruct4",
"base_model:finetune:llm-jp/llm-jp-3.1-1.8b-instruct4",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-16T02:12:00Z | ---
license: apache-2.0
language:
- en
- ja
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
library_name: mlx
inference: false
tags:
- mlx
base_model: llm-jp/llm-jp-3.1-1.8b-instruct4
---
# mlx-community/llm-jp-3.1-1.8b-instruct4
This model [mlx-community/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/mlx-community/llm-jp-3.1-1.8b-instruct4) was
converted to MLX format from [llm-jp/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4)
using mlx-lm version **0.24.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/llm-jp-3.1-1.8b-instruct4")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
jeremyys/multilingual-e5-base-Q4_K_M-GGUF | jeremyys | 2025-06-16T02:16:57Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"gguf",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"llama-cpp",
"gguf-my-repo",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"base_model:intfloat/multilingual-e5-base",
"base_model:quantized:intfloat/multilingual-e5-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | sentence-similarity | 2025-06-16T02:16:53Z | ---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
- llama-cpp
- gguf-my-repo
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
base_model: intfloat/multilingual-e5-base
model-index:
- name: multilingual-e5-base
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 78.97014925373135
- type: ap
value: 43.69351129103008
- type: f1
value: 73.38075030070492
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.7237687366167
- type: ap
value: 82.22089859962671
- type: f1
value: 69.95532758884401
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.65517241379312
- type: ap
value: 28.507918657094738
- type: f1
value: 66.84516013726119
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.32976445396146
- type: ap
value: 20.720481637566014
- type: f1
value: 59.78002763416003
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 90.63775
- type: ap
value: 87.22277903861716
- type: f1
value: 90.60378636386807
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.546
- type: f1
value: 44.05666638370923
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 41.828
- type: f1
value: 41.2710255644252
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.534
- type: f1
value: 39.820743174270326
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.684
- type: f1
value: 39.11052682815307
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.436
- type: f1
value: 37.07082931930871
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.226000000000006
- type: f1
value: 36.65372077739185
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.831000000000003
- type: map_at_10
value: 36.42
- type: map_at_100
value: 37.699
- type: map_at_1000
value: 37.724000000000004
- type: map_at_3
value: 32.207
- type: map_at_5
value: 34.312
- type: mrr_at_1
value: 23.257
- type: mrr_at_10
value: 36.574
- type: mrr_at_100
value: 37.854
- type: mrr_at_1000
value: 37.878
- type: mrr_at_3
value: 32.385000000000005
- type: mrr_at_5
value: 34.48
- type: ndcg_at_1
value: 22.831000000000003
- type: ndcg_at_10
value: 44.230000000000004
- type: ndcg_at_100
value: 49.974000000000004
- type: ndcg_at_1000
value: 50.522999999999996
- type: ndcg_at_3
value: 35.363
- type: ndcg_at_5
value: 39.164
- type: precision_at_1
value: 22.831000000000003
- type: precision_at_10
value: 6.935
- type: precision_at_100
value: 0.9520000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.841
- type: precision_at_5
value: 10.754
- type: recall_at_1
value: 22.831000000000003
- type: recall_at_10
value: 69.346
- type: recall_at_100
value: 95.235
- type: recall_at_1000
value: 99.36
- type: recall_at_3
value: 44.523
- type: recall_at_5
value: 53.769999999999996
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 40.27789869854063
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.41979463347428
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.22752045109304
- type: mrr
value: 71.51112430198303
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.71147646622866
- type: cos_sim_spearman
value: 85.059167046486
- type: euclidean_pearson
value: 75.88421613600647
- type: euclidean_spearman
value: 75.12821787150585
- type: manhattan_pearson
value: 75.22005646957604
- type: manhattan_spearman
value: 74.42880434453272
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.23799582463465
- type: f1
value: 99.12665274878218
- type: precision
value: 99.07098121085595
- type: recall
value: 99.23799582463465
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.88685890380806
- type: f1
value: 97.59336708489249
- type: precision
value: 97.44662117543473
- type: recall
value: 97.88685890380806
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.47142362313821
- type: f1
value: 97.1989377670015
- type: precision
value: 97.06384944001847
- type: recall
value: 97.47142362313821
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.4728804634018
- type: f1
value: 98.2973494821836
- type: precision
value: 98.2095839915745
- type: recall
value: 98.4728804634018
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 82.74025974025975
- type: f1
value: 82.67420447730439
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.0380848063507
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 29.45956405670166
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.122
- type: map_at_10
value: 42.03
- type: map_at_100
value: 43.364000000000004
- type: map_at_1000
value: 43.474000000000004
- type: map_at_3
value: 38.804
- type: map_at_5
value: 40.585
- type: mrr_at_1
value: 39.914
- type: mrr_at_10
value: 48.227
- type: mrr_at_100
value: 49.018
- type: mrr_at_1000
value: 49.064
- type: mrr_at_3
value: 45.994
- type: mrr_at_5
value: 47.396
- type: ndcg_at_1
value: 39.914
- type: ndcg_at_10
value: 47.825
- type: ndcg_at_100
value: 52.852
- type: ndcg_at_1000
value: 54.891
- type: ndcg_at_3
value: 43.517
- type: ndcg_at_5
value: 45.493
- type: precision_at_1
value: 39.914
- type: precision_at_10
value: 8.956
- type: precision_at_100
value: 1.388
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 20.791999999999998
- type: precision_at_5
value: 14.821000000000002
- type: recall_at_1
value: 32.122
- type: recall_at_10
value: 58.294999999999995
- type: recall_at_100
value: 79.726
- type: recall_at_1000
value: 93.099
- type: recall_at_3
value: 45.017
- type: recall_at_5
value: 51.002
- type: map_at_1
value: 29.677999999999997
- type: map_at_10
value: 38.684000000000005
- type: map_at_100
value: 39.812999999999995
- type: map_at_1000
value: 39.945
- type: map_at_3
value: 35.831
- type: map_at_5
value: 37.446
- type: mrr_at_1
value: 37.771
- type: mrr_at_10
value: 44.936
- type: mrr_at_100
value: 45.583
- type: mrr_at_1000
value: 45.634
- type: mrr_at_3
value: 42.771
- type: mrr_at_5
value: 43.994
- type: ndcg_at_1
value: 37.771
- type: ndcg_at_10
value: 44.059
- type: ndcg_at_100
value: 48.192
- type: ndcg_at_1000
value: 50.375
- type: ndcg_at_3
value: 40.172000000000004
- type: ndcg_at_5
value: 41.899
- type: precision_at_1
value: 37.771
- type: precision_at_10
value: 8.286999999999999
- type: precision_at_100
value: 1.322
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.406000000000002
- type: precision_at_5
value: 13.745
- type: recall_at_1
value: 29.677999999999997
- type: recall_at_10
value: 53.071
- type: recall_at_100
value: 70.812
- type: recall_at_1000
value: 84.841
- type: recall_at_3
value: 41.016000000000005
- type: recall_at_5
value: 46.22
- type: map_at_1
value: 42.675000000000004
- type: map_at_10
value: 53.93599999999999
- type: map_at_100
value: 54.806999999999995
- type: map_at_1000
value: 54.867
- type: map_at_3
value: 50.934000000000005
- type: map_at_5
value: 52.583
- type: mrr_at_1
value: 48.339
- type: mrr_at_10
value: 57.265
- type: mrr_at_100
value: 57.873
- type: mrr_at_1000
value: 57.906
- type: mrr_at_3
value: 55.193000000000005
- type: mrr_at_5
value: 56.303000000000004
- type: ndcg_at_1
value: 48.339
- type: ndcg_at_10
value: 59.19799999999999
- type: ndcg_at_100
value: 62.743
- type: ndcg_at_1000
value: 63.99399999999999
- type: ndcg_at_3
value: 54.367
- type: ndcg_at_5
value: 56.548
- type: precision_at_1
value: 48.339
- type: precision_at_10
value: 9.216000000000001
- type: precision_at_100
value: 1.1809999999999998
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 23.72
- type: precision_at_5
value: 16.025
- type: recall_at_1
value: 42.675000000000004
- type: recall_at_10
value: 71.437
- type: recall_at_100
value: 86.803
- type: recall_at_1000
value: 95.581
- type: recall_at_3
value: 58.434
- type: recall_at_5
value: 63.754
- type: map_at_1
value: 23.518
- type: map_at_10
value: 30.648999999999997
- type: map_at_100
value: 31.508999999999997
- type: map_at_1000
value: 31.604
- type: map_at_3
value: 28.247
- type: map_at_5
value: 29.65
- type: mrr_at_1
value: 25.650000000000002
- type: mrr_at_10
value: 32.771
- type: mrr_at_100
value: 33.554
- type: mrr_at_1000
value: 33.629999999999995
- type: mrr_at_3
value: 30.433
- type: mrr_at_5
value: 31.812
- type: ndcg_at_1
value: 25.650000000000002
- type: ndcg_at_10
value: 34.929
- type: ndcg_at_100
value: 39.382
- type: ndcg_at_1000
value: 41.913
- type: ndcg_at_3
value: 30.292
- type: ndcg_at_5
value: 32.629999999999995
- type: precision_at_1
value: 25.650000000000002
- type: precision_at_10
value: 5.311
- type: precision_at_100
value: 0.792
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 12.58
- type: precision_at_5
value: 8.994
- type: recall_at_1
value: 23.518
- type: recall_at_10
value: 46.19
- type: recall_at_100
value: 67.123
- type: recall_at_1000
value: 86.442
- type: recall_at_3
value: 33.678000000000004
- type: recall_at_5
value: 39.244
- type: map_at_1
value: 15.891
- type: map_at_10
value: 22.464000000000002
- type: map_at_100
value: 23.483
- type: map_at_1000
value: 23.613
- type: map_at_3
value: 20.080000000000002
- type: map_at_5
value: 21.526
- type: mrr_at_1
value: 20.025000000000002
- type: mrr_at_10
value: 26.712999999999997
- type: mrr_at_100
value: 27.650000000000002
- type: mrr_at_1000
value: 27.737000000000002
- type: mrr_at_3
value: 24.274
- type: mrr_at_5
value: 25.711000000000002
- type: ndcg_at_1
value: 20.025000000000002
- type: ndcg_at_10
value: 27.028999999999996
- type: ndcg_at_100
value: 32.064
- type: ndcg_at_1000
value: 35.188
- type: ndcg_at_3
value: 22.512999999999998
- type: ndcg_at_5
value: 24.89
- type: precision_at_1
value: 20.025000000000002
- type: precision_at_10
value: 4.776
- type: precision_at_100
value: 0.8500000000000001
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 10.531
- type: precision_at_5
value: 7.811
- type: recall_at_1
value: 15.891
- type: recall_at_10
value: 37.261
- type: recall_at_100
value: 59.12
- type: recall_at_1000
value: 81.356
- type: recall_at_3
value: 24.741
- type: recall_at_5
value: 30.753999999999998
- type: map_at_1
value: 27.544
- type: map_at_10
value: 36.283
- type: map_at_100
value: 37.467
- type: map_at_1000
value: 37.574000000000005
- type: map_at_3
value: 33.528999999999996
- type: map_at_5
value: 35.028999999999996
- type: mrr_at_1
value: 34.166999999999994
- type: mrr_at_10
value: 41.866
- type: mrr_at_100
value: 42.666
- type: mrr_at_1000
value: 42.716
- type: mrr_at_3
value: 39.541
- type: mrr_at_5
value: 40.768
- type: ndcg_at_1
value: 34.166999999999994
- type: ndcg_at_10
value: 41.577
- type: ndcg_at_100
value: 46.687
- type: ndcg_at_1000
value: 48.967
- type: ndcg_at_3
value: 37.177
- type: ndcg_at_5
value: 39.097
- type: precision_at_1
value: 34.166999999999994
- type: precision_at_10
value: 7.420999999999999
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 17.291999999999998
- type: precision_at_5
value: 12.166
- type: recall_at_1
value: 27.544
- type: recall_at_10
value: 51.99399999999999
- type: recall_at_100
value: 73.738
- type: recall_at_1000
value: 89.33
- type: recall_at_3
value: 39.179
- type: recall_at_5
value: 44.385999999999996
- type: map_at_1
value: 26.661
- type: map_at_10
value: 35.475
- type: map_at_100
value: 36.626999999999995
- type: map_at_1000
value: 36.741
- type: map_at_3
value: 32.818000000000005
- type: map_at_5
value: 34.397
- type: mrr_at_1
value: 32.647999999999996
- type: mrr_at_10
value: 40.784
- type: mrr_at_100
value: 41.602
- type: mrr_at_1000
value: 41.661
- type: mrr_at_3
value: 38.68
- type: mrr_at_5
value: 39.838
- type: ndcg_at_1
value: 32.647999999999996
- type: ndcg_at_10
value: 40.697
- type: ndcg_at_100
value: 45.799
- type: ndcg_at_1000
value: 48.235
- type: ndcg_at_3
value: 36.516
- type: ndcg_at_5
value: 38.515
- type: precision_at_1
value: 32.647999999999996
- type: precision_at_10
value: 7.202999999999999
- type: precision_at_100
value: 1.1360000000000001
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 17.314
- type: precision_at_5
value: 12.145999999999999
- type: recall_at_1
value: 26.661
- type: recall_at_10
value: 50.995000000000005
- type: recall_at_100
value: 73.065
- type: recall_at_1000
value: 89.781
- type: recall_at_3
value: 39.073
- type: recall_at_5
value: 44.395
- type: map_at_1
value: 25.946583333333333
- type: map_at_10
value: 33.79725
- type: map_at_100
value: 34.86408333333333
- type: map_at_1000
value: 34.9795
- type: map_at_3
value: 31.259999999999998
- type: map_at_5
value: 32.71541666666666
- type: mrr_at_1
value: 30.863749999999996
- type: mrr_at_10
value: 37.99183333333333
- type: mrr_at_100
value: 38.790499999999994
- type: mrr_at_1000
value: 38.85575000000001
- type: mrr_at_3
value: 35.82083333333333
- type: mrr_at_5
value: 37.07533333333333
- type: ndcg_at_1
value: 30.863749999999996
- type: ndcg_at_10
value: 38.52141666666667
- type: ndcg_at_100
value: 43.17966666666667
- type: ndcg_at_1000
value: 45.64608333333333
- type: ndcg_at_3
value: 34.333000000000006
- type: ndcg_at_5
value: 36.34975
- type: precision_at_1
value: 30.863749999999996
- type: precision_at_10
value: 6.598999999999999
- type: precision_at_100
value: 1.0502500000000001
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_3
value: 15.557583333333334
- type: precision_at_5
value: 11.020000000000001
- type: recall_at_1
value: 25.946583333333333
- type: recall_at_10
value: 48.36991666666666
- type: recall_at_100
value: 69.02408333333334
- type: recall_at_1000
value: 86.43858333333331
- type: recall_at_3
value: 36.4965
- type: recall_at_5
value: 41.76258333333334
- type: map_at_1
value: 22.431
- type: map_at_10
value: 28.889
- type: map_at_100
value: 29.642000000000003
- type: map_at_1000
value: 29.742
- type: map_at_3
value: 26.998
- type: map_at_5
value: 28.172000000000004
- type: mrr_at_1
value: 25.307000000000002
- type: mrr_at_10
value: 31.763
- type: mrr_at_100
value: 32.443
- type: mrr_at_1000
value: 32.531
- type: mrr_at_3
value: 29.959000000000003
- type: mrr_at_5
value: 31.063000000000002
- type: ndcg_at_1
value: 25.307000000000002
- type: ndcg_at_10
value: 32.586999999999996
- type: ndcg_at_100
value: 36.5
- type: ndcg_at_1000
value: 39.133
- type: ndcg_at_3
value: 29.25
- type: ndcg_at_5
value: 31.023
- type: precision_at_1
value: 25.307000000000002
- type: precision_at_10
value: 4.954
- type: precision_at_100
value: 0.747
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 12.577
- type: precision_at_5
value: 8.741999999999999
- type: recall_at_1
value: 22.431
- type: recall_at_10
value: 41.134
- type: recall_at_100
value: 59.28600000000001
- type: recall_at_1000
value: 78.857
- type: recall_at_3
value: 31.926
- type: recall_at_5
value: 36.335
- type: map_at_1
value: 17.586
- type: map_at_10
value: 23.304
- type: map_at_100
value: 24.159
- type: map_at_1000
value: 24.281
- type: map_at_3
value: 21.316
- type: map_at_5
value: 22.383
- type: mrr_at_1
value: 21.645
- type: mrr_at_10
value: 27.365000000000002
- type: mrr_at_100
value: 28.108
- type: mrr_at_1000
value: 28.192
- type: mrr_at_3
value: 25.482
- type: mrr_at_5
value: 26.479999999999997
- type: ndcg_at_1
value: 21.645
- type: ndcg_at_10
value: 27.306
- type: ndcg_at_100
value: 31.496000000000002
- type: ndcg_at_1000
value: 34.53
- type: ndcg_at_3
value: 23.73
- type: ndcg_at_5
value: 25.294
- type: precision_at_1
value: 21.645
- type: precision_at_10
value: 4.797
- type: precision_at_100
value: 0.8059999999999999
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 10.850999999999999
- type: precision_at_5
value: 7.736
- type: recall_at_1
value: 17.586
- type: recall_at_10
value: 35.481
- type: recall_at_100
value: 54.534000000000006
- type: recall_at_1000
value: 76.456
- type: recall_at_3
value: 25.335
- type: recall_at_5
value: 29.473
- type: map_at_1
value: 25.095
- type: map_at_10
value: 32.374
- type: map_at_100
value: 33.537
- type: map_at_1000
value: 33.634
- type: map_at_3
value: 30.089
- type: map_at_5
value: 31.433
- type: mrr_at_1
value: 29.198
- type: mrr_at_10
value: 36.01
- type: mrr_at_100
value: 37.022
- type: mrr_at_1000
value: 37.083
- type: mrr_at_3
value: 33.94
- type: mrr_at_5
value: 35.148
- type: ndcg_at_1
value: 29.198
- type: ndcg_at_10
value: 36.729
- type: ndcg_at_100
value: 42.114000000000004
- type: ndcg_at_1000
value: 44.592
- type: ndcg_at_3
value: 32.644
- type: ndcg_at_5
value: 34.652
- type: precision_at_1
value: 29.198
- type: precision_at_10
value: 5.970000000000001
- type: precision_at_100
value: 0.967
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 14.396999999999998
- type: precision_at_5
value: 10.093
- type: recall_at_1
value: 25.095
- type: recall_at_10
value: 46.392
- type: recall_at_100
value: 69.706
- type: recall_at_1000
value: 87.738
- type: recall_at_3
value: 35.303000000000004
- type: recall_at_5
value: 40.441
- type: map_at_1
value: 26.857999999999997
- type: map_at_10
value: 34.066
- type: map_at_100
value: 35.671
- type: map_at_1000
value: 35.881
- type: map_at_3
value: 31.304
- type: map_at_5
value: 32.885
- type: mrr_at_1
value: 32.411
- type: mrr_at_10
value: 38.987
- type: mrr_at_100
value: 39.894
- type: mrr_at_1000
value: 39.959
- type: mrr_at_3
value: 36.626999999999995
- type: mrr_at_5
value: 38.011
- type: ndcg_at_1
value: 32.411
- type: ndcg_at_10
value: 39.208
- type: ndcg_at_100
value: 44.626
- type: ndcg_at_1000
value: 47.43
- type: ndcg_at_3
value: 35.091
- type: ndcg_at_5
value: 37.119
- type: precision_at_1
value: 32.411
- type: precision_at_10
value: 7.51
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 16.14
- type: precision_at_5
value: 11.976
- type: recall_at_1
value: 26.857999999999997
- type: recall_at_10
value: 47.407
- type: recall_at_100
value: 72.236
- type: recall_at_1000
value: 90.77
- type: recall_at_3
value: 35.125
- type: recall_at_5
value: 40.522999999999996
- type: map_at_1
value: 21.3
- type: map_at_10
value: 27.412999999999997
- type: map_at_100
value: 28.29
- type: map_at_1000
value: 28.398
- type: map_at_3
value: 25.169999999999998
- type: map_at_5
value: 26.496
- type: mrr_at_1
value: 23.29
- type: mrr_at_10
value: 29.215000000000003
- type: mrr_at_100
value: 30.073
- type: mrr_at_1000
value: 30.156
- type: mrr_at_3
value: 26.956000000000003
- type: mrr_at_5
value: 28.38
- type: ndcg_at_1
value: 23.29
- type: ndcg_at_10
value: 31.113000000000003
- type: ndcg_at_100
value: 35.701
- type: ndcg_at_1000
value: 38.505
- type: ndcg_at_3
value: 26.727
- type: ndcg_at_5
value: 29.037000000000003
- type: precision_at_1
value: 23.29
- type: precision_at_10
value: 4.787
- type: precision_at_100
value: 0.763
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 11.091
- type: precision_at_5
value: 7.985
- type: recall_at_1
value: 21.3
- type: recall_at_10
value: 40.782000000000004
- type: recall_at_100
value: 62.13999999999999
- type: recall_at_1000
value: 83.012
- type: recall_at_3
value: 29.131
- type: recall_at_5
value: 34.624
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.631
- type: map_at_10
value: 16.634999999999998
- type: map_at_100
value: 18.23
- type: map_at_1000
value: 18.419
- type: map_at_3
value: 13.66
- type: map_at_5
value: 15.173
- type: mrr_at_1
value: 21.368000000000002
- type: mrr_at_10
value: 31.56
- type: mrr_at_100
value: 32.58
- type: mrr_at_1000
value: 32.633
- type: mrr_at_3
value: 28.241
- type: mrr_at_5
value: 30.225
- type: ndcg_at_1
value: 21.368000000000002
- type: ndcg_at_10
value: 23.855999999999998
- type: ndcg_at_100
value: 30.686999999999998
- type: ndcg_at_1000
value: 34.327000000000005
- type: ndcg_at_3
value: 18.781
- type: ndcg_at_5
value: 20.73
- type: precision_at_1
value: 21.368000000000002
- type: precision_at_10
value: 7.564
- type: precision_at_100
value: 1.496
- type: precision_at_1000
value: 0.217
- type: precision_at_3
value: 13.876
- type: precision_at_5
value: 11.062
- type: recall_at_1
value: 9.631
- type: recall_at_10
value: 29.517
- type: recall_at_100
value: 53.452
- type: recall_at_1000
value: 74.115
- type: recall_at_3
value: 17.605999999999998
- type: recall_at_5
value: 22.505
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.885
- type: map_at_10
value: 18.798000000000002
- type: map_at_100
value: 26.316
- type: map_at_1000
value: 27.869
- type: map_at_3
value: 13.719000000000001
- type: map_at_5
value: 15.716
- type: mrr_at_1
value: 66
- type: mrr_at_10
value: 74.263
- type: mrr_at_100
value: 74.519
- type: mrr_at_1000
value: 74.531
- type: mrr_at_3
value: 72.458
- type: mrr_at_5
value: 73.321
- type: ndcg_at_1
value: 53.87499999999999
- type: ndcg_at_10
value: 40.355999999999995
- type: ndcg_at_100
value: 44.366
- type: ndcg_at_1000
value: 51.771
- type: ndcg_at_3
value: 45.195
- type: ndcg_at_5
value: 42.187000000000005
- type: precision_at_1
value: 66
- type: precision_at_10
value: 31.75
- type: precision_at_100
value: 10.11
- type: precision_at_1000
value: 1.9800000000000002
- type: precision_at_3
value: 48.167
- type: precision_at_5
value: 40.050000000000004
- type: recall_at_1
value: 8.885
- type: recall_at_10
value: 24.471999999999998
- type: recall_at_100
value: 49.669000000000004
- type: recall_at_1000
value: 73.383
- type: recall_at_3
value: 14.872
- type: recall_at_5
value: 18.262999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 45.18
- type: f1
value: 40.26878691789978
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 62.751999999999995
- type: map_at_10
value: 74.131
- type: map_at_100
value: 74.407
- type: map_at_1000
value: 74.423
- type: map_at_3
value: 72.329
- type: map_at_5
value: 73.555
- type: mrr_at_1
value: 67.282
- type: mrr_at_10
value: 78.292
- type: mrr_at_100
value: 78.455
- type: mrr_at_1000
value: 78.458
- type: mrr_at_3
value: 76.755
- type: mrr_at_5
value: 77.839
- type: ndcg_at_1
value: 67.282
- type: ndcg_at_10
value: 79.443
- type: ndcg_at_100
value: 80.529
- type: ndcg_at_1000
value: 80.812
- type: ndcg_at_3
value: 76.281
- type: ndcg_at_5
value: 78.235
- type: precision_at_1
value: 67.282
- type: precision_at_10
value: 10.078
- type: precision_at_100
value: 1.082
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 30.178
- type: precision_at_5
value: 19.232
- type: recall_at_1
value: 62.751999999999995
- type: recall_at_10
value: 91.521
- type: recall_at_100
value: 95.997
- type: recall_at_1000
value: 97.775
- type: recall_at_3
value: 83.131
- type: recall_at_5
value: 87.93299999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.861
- type: map_at_10
value: 30.252000000000002
- type: map_at_100
value: 32.082
- type: map_at_1000
value: 32.261
- type: map_at_3
value: 25.909
- type: map_at_5
value: 28.296
- type: mrr_at_1
value: 37.346000000000004
- type: mrr_at_10
value: 45.802
- type: mrr_at_100
value: 46.611999999999995
- type: mrr_at_1000
value: 46.659
- type: mrr_at_3
value: 43.056
- type: mrr_at_5
value: 44.637
- type: ndcg_at_1
value: 37.346000000000004
- type: ndcg_at_10
value: 38.169
- type: ndcg_at_100
value: 44.864
- type: ndcg_at_1000
value: 47.974
- type: ndcg_at_3
value: 33.619
- type: ndcg_at_5
value: 35.317
- type: precision_at_1
value: 37.346000000000004
- type: precision_at_10
value: 10.693999999999999
- type: precision_at_100
value: 1.775
- type: precision_at_1000
value: 0.231
- type: precision_at_3
value: 22.325
- type: precision_at_5
value: 16.852
- type: recall_at_1
value: 18.861
- type: recall_at_10
value: 45.672000000000004
- type: recall_at_100
value: 70.60499999999999
- type: recall_at_1000
value: 89.216
- type: recall_at_3
value: 30.361
- type: recall_at_5
value: 36.998999999999995
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.852999999999994
- type: map_at_10
value: 59.961
- type: map_at_100
value: 60.78
- type: map_at_1000
value: 60.843
- type: map_at_3
value: 56.39999999999999
- type: map_at_5
value: 58.646
- type: mrr_at_1
value: 75.70599999999999
- type: mrr_at_10
value: 82.321
- type: mrr_at_100
value: 82.516
- type: mrr_at_1000
value: 82.525
- type: mrr_at_3
value: 81.317
- type: mrr_at_5
value: 81.922
- type: ndcg_at_1
value: 75.70599999999999
- type: ndcg_at_10
value: 68.557
- type: ndcg_at_100
value: 71.485
- type: ndcg_at_1000
value: 72.71600000000001
- type: ndcg_at_3
value: 63.524
- type: ndcg_at_5
value: 66.338
- type: precision_at_1
value: 75.70599999999999
- type: precision_at_10
value: 14.463000000000001
- type: precision_at_100
value: 1.677
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 40.806
- type: precision_at_5
value: 26.709
- type: recall_at_1
value: 37.852999999999994
- type: recall_at_10
value: 72.316
- type: recall_at_100
value: 83.842
- type: recall_at_1000
value: 91.999
- type: recall_at_3
value: 61.209
- type: recall_at_5
value: 66.77199999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 85.46039999999999
- type: ap
value: 79.9812521351881
- type: f1
value: 85.31722909702084
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.704
- type: map_at_10
value: 35.329
- type: map_at_100
value: 36.494
- type: map_at_1000
value: 36.541000000000004
- type: map_at_3
value: 31.476
- type: map_at_5
value: 33.731
- type: mrr_at_1
value: 23.294999999999998
- type: mrr_at_10
value: 35.859
- type: mrr_at_100
value: 36.968
- type: mrr_at_1000
value: 37.008
- type: mrr_at_3
value: 32.085
- type: mrr_at_5
value: 34.299
- type: ndcg_at_1
value: 23.324
- type: ndcg_at_10
value: 42.274
- type: ndcg_at_100
value: 47.839999999999996
- type: ndcg_at_1000
value: 48.971
- type: ndcg_at_3
value: 34.454
- type: ndcg_at_5
value: 38.464
- type: precision_at_1
value: 23.324
- type: precision_at_10
value: 6.648
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.674999999999999
- type: precision_at_5
value: 10.850999999999999
- type: recall_at_1
value: 22.704
- type: recall_at_10
value: 63.660000000000004
- type: recall_at_100
value: 89.29899999999999
- type: recall_at_1000
value: 97.88900000000001
- type: recall_at_3
value: 42.441
- type: recall_at_5
value: 52.04
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.1326949384405
- type: f1
value: 92.89743579612082
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.62524654832347
- type: f1
value: 88.65106082263151
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.59039359573046
- type: f1
value: 90.31532892105662
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.21046038208581
- type: f1
value: 86.41459529813113
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.3180351380423
- type: f1
value: 86.71383078226444
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.24231464737792
- type: f1
value: 86.31845567592403
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.27131782945736
- type: f1
value: 57.52079940417103
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.2341504649197
- type: f1
value: 51.349951558039244
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.27418278852569
- type: f1
value: 50.1714985749095
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.68243031631694
- type: f1
value: 50.1066160836192
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 69.2362854069559
- type: f1
value: 48.821279948766424
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.71428571428571
- type: f1
value: 53.94611389496195
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 59.97646267652992
- type: f1
value: 57.26797883561521
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (am)
type: mteb/amazon_massive_intent
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 53.65501008742435
- type: f1
value: 50.416258382177034
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ar)
type: mteb/amazon_massive_intent
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.45796906523201
- type: f1
value: 53.306690547422185
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (az)
type: mteb/amazon_massive_intent
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.59246805648957
- type: f1
value: 59.818381969051494
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (bn)
type: mteb/amazon_massive_intent
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 61.126429051782104
- type: f1
value: 58.25993593933026
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (cy)
type: mteb/amazon_massive_intent
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 50.057162071284466
- type: f1
value: 46.96095728790911
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (da)
type: mteb/amazon_massive_intent
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.64425016812375
- type: f1
value: 62.858291698755764
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.08944182918628
- type: f1
value: 62.44639030604241
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (el)
type: mteb/amazon_massive_intent
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.68056489576328
- type: f1
value: 61.775326758789504
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.11163416274377
- type: f1
value: 69.70789096927015
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.40282447881641
- type: f1
value: 66.38492065671895
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fa)
type: mteb/amazon_massive_intent
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.24613315400134
- type: f1
value: 64.3348019501336
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fi)
type: mteb/amazon_massive_intent
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.78345662407531
- type: f1
value: 62.21279452354622
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.9455279085407
- type: f1
value: 65.48193124964094
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (he)
type: mteb/amazon_massive_intent
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.05110961667788
- type: f1
value: 58.097856564684534
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hi)
type: mteb/amazon_massive_intent
config: hi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.95292535305985
- type: f1
value: 62.09182174767901
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hu)
type: mteb/amazon_massive_intent
config: hu
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.97310020174848
- type: f1
value: 61.14252567730396
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hy)
type: mteb/amazon_massive_intent
config: hy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.08069939475453
- type: f1
value: 57.044041742492034
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (id)
type: mteb/amazon_massive_intent
config: id
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.63752521856085
- type: f1
value: 63.889340907205316
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (is)
type: mteb/amazon_massive_intent
config: is
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 56.385339609952936
- type: f1
value: 53.449033750088304
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (it)
type: mteb/amazon_massive_intent
config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.93073301950234
- type: f1
value: 65.9884357824104
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ja)
type: mteb/amazon_massive_intent
config: ja
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.94418291862812
- type: f1
value: 66.48740222583132
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (jv)
type: mteb/amazon_massive_intent
config: jv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 54.26025554808339
- type: f1
value: 50.19562815100793
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ka)
type: mteb/amazon_massive_intent
config: ka
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 48.98789509078682
- type: f1
value: 46.65788438676836
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (km)
type: mteb/amazon_massive_intent
config: km
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 44.68728984532616
- type: f1
value: 41.642419349541996
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (kn)
type: mteb/amazon_massive_intent
config: kn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 59.19300605245461
- type: f1
value: 55.8626492442437
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ko)
type: mteb/amazon_massive_intent
config: ko
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.33826496301278
- type: f1
value: 63.89499791648792
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (lv)
type: mteb/amazon_massive_intent
config: lv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.33960995292536
- type: f1
value: 57.15242464180892
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ml)
type: mteb/amazon_massive_intent
config: ml
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.09347679892402
- type: f1
value: 59.64733214063841
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (mn)
type: mteb/amazon_massive_intent
config: mn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 58.75924680564896
- type: f1
value: 55.96585692366827
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ms)
type: mteb/amazon_massive_intent
config: ms
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.48486886348352
- type: f1
value: 59.45143559032946
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (my)
type: mteb/amazon_massive_intent
config: my
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 58.56422326832549
- type: f1
value: 54.96368702901926
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (nb)
type: mteb/amazon_massive_intent
config: nb
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.18022864828512
- type: f1
value: 63.05369805040634
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (nl)
type: mteb/amazon_massive_intent
config: nl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.30329522528581
- type: f1
value: 64.06084612020727
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.36919973100201
- type: f1
value: 65.12154124788887
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pt)
type: mteb/amazon_massive_intent
config: pt
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.98117014122394
- type: f1
value: 66.41847559806962
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ro)
type: mteb/amazon_massive_intent
config: ro
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.53799596503026
- type: f1
value: 62.17067330740817
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ru)
type: mteb/amazon_massive_intent
config: ru
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.01815736381977
- type: f1
value: 66.24988369607843
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (sl)
type: mteb/amazon_massive_intent
config: sl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.34700739744452
- type: f1
value: 59.957933424941636
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (sq)
type: mteb/amazon_massive_intent
config: sq
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 61.23402824478815
- type: f1
value: 57.98836976018471
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (sv)
type: mteb/amazon_massive_intent
config: sv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.54068594485541
- type: f1
value: 65.43849680666855
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (sw)
type: mteb/amazon_massive_intent
config: sw
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 55.998655010087425
- type: f1
value: 52.83737515406804
- task:
type: Classification
dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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revision: None
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value: 37.629000000000005
- type: ndcg_at_3
value: 37.845
- type: ndcg_at_5
value: 35.147
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 23.932000000000002
- type: precision_at_100
value: 7.17
- type: precision_at_1000
value: 1.967
- type: precision_at_3
value: 35.397
- type: precision_at_5
value: 29.907
- type: recall_at_1
value: 5.5280000000000005
- type: recall_at_10
value: 15.568000000000001
- type: recall_at_100
value: 28.54
- type: recall_at_1000
value: 59.864
- type: recall_at_3
value: 9.822000000000001
- type: recall_at_5
value: 11.726
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.041000000000004
- type: map_at_10
value: 52.664
- type: map_at_100
value: 53.477
- type: map_at_1000
value: 53.505
- type: map_at_3
value: 48.510999999999996
- type: map_at_5
value: 51.036
- type: mrr_at_1
value: 41.338
- type: mrr_at_10
value: 55.071000000000005
- type: mrr_at_100
value: 55.672
- type: mrr_at_1000
value: 55.689
- type: mrr_at_3
value: 51.82
- type: mrr_at_5
value: 53.852
- type: ndcg_at_1
value: 41.338
- type: ndcg_at_10
value: 60.01800000000001
- type: ndcg_at_100
value: 63.409000000000006
- type: ndcg_at_1000
value: 64.017
- type: ndcg_at_3
value: 52.44799999999999
- type: ndcg_at_5
value: 56.571000000000005
- type: precision_at_1
value: 41.338
- type: precision_at_10
value: 9.531
- type: precision_at_100
value: 1.145
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.416
- type: precision_at_5
value: 16.46
- type: recall_at_1
value: 37.041000000000004
- type: recall_at_10
value: 79.76299999999999
- type: recall_at_100
value: 94.39
- type: recall_at_1000
value: 98.851
- type: recall_at_3
value: 60.465
- type: recall_at_5
value: 69.906
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.952
- type: map_at_10
value: 83.758
- type: map_at_100
value: 84.406
- type: map_at_1000
value: 84.425
- type: map_at_3
value: 80.839
- type: map_at_5
value: 82.646
- type: mrr_at_1
value: 80.62
- type: mrr_at_10
value: 86.947
- type: mrr_at_100
value: 87.063
- type: mrr_at_1000
value: 87.064
- type: mrr_at_3
value: 85.96000000000001
- type: mrr_at_5
value: 86.619
- type: ndcg_at_1
value: 80.63
- type: ndcg_at_10
value: 87.64800000000001
- type: ndcg_at_100
value: 88.929
- type: ndcg_at_1000
value: 89.054
- type: ndcg_at_3
value: 84.765
- type: ndcg_at_5
value: 86.291
- type: precision_at_1
value: 80.63
- type: precision_at_10
value: 13.314
- type: precision_at_100
value: 1.525
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.1
- type: precision_at_5
value: 24.372
- type: recall_at_1
value: 69.952
- type: recall_at_10
value: 94.955
- type: recall_at_100
value: 99.38
- type: recall_at_1000
value: 99.96000000000001
- type: recall_at_3
value: 86.60600000000001
- type: recall_at_5
value: 90.997
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.41329517878427
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 55.171278362748666
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.213
- type: map_at_10
value: 9.895
- type: map_at_100
value: 11.776
- type: map_at_1000
value: 12.084
- type: map_at_3
value: 7.2669999999999995
- type: map_at_5
value: 8.620999999999999
- type: mrr_at_1
value: 20.8
- type: mrr_at_10
value: 31.112000000000002
- type: mrr_at_100
value: 32.274
- type: mrr_at_1000
value: 32.35
- type: mrr_at_3
value: 28.133000000000003
- type: mrr_at_5
value: 29.892999999999997
- type: ndcg_at_1
value: 20.8
- type: ndcg_at_10
value: 17.163999999999998
- type: ndcg_at_100
value: 24.738
- type: ndcg_at_1000
value: 30.316
- type: ndcg_at_3
value: 16.665
- type: ndcg_at_5
value: 14.478
- type: precision_at_1
value: 20.8
- type: precision_at_10
value: 8.74
- type: precision_at_100
value: 1.963
- type: precision_at_1000
value: 0.33
- type: precision_at_3
value: 15.467
- type: precision_at_5
value: 12.6
- type: recall_at_1
value: 4.213
- type: recall_at_10
value: 17.698
- type: recall_at_100
value: 39.838
- type: recall_at_1000
value: 66.893
- type: recall_at_3
value: 9.418
- type: recall_at_5
value: 12.773000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.90453315738294
- type: cos_sim_spearman
value: 78.51197850080254
- type: euclidean_pearson
value: 80.09647123597748
- type: euclidean_spearman
value: 78.63548011514061
- type: manhattan_pearson
value: 80.10645285675231
- type: manhattan_spearman
value: 78.57861806068901
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.2616156846401
- type: cos_sim_spearman
value: 76.69713867850156
- type: euclidean_pearson
value: 77.97948563800394
- type: euclidean_spearman
value: 74.2371211567807
- type: manhattan_pearson
value: 77.69697879669705
- type: manhattan_spearman
value: 73.86529778022278
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.0293269315045
- type: cos_sim_spearman
value: 78.02555120584198
- type: euclidean_pearson
value: 78.25398100379078
- type: euclidean_spearman
value: 78.66963870599464
- type: manhattan_pearson
value: 78.14314682167348
- type: manhattan_spearman
value: 78.57692322969135
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.16989925136942
- type: cos_sim_spearman
value: 76.5996225327091
- type: euclidean_pearson
value: 77.8319003279786
- type: euclidean_spearman
value: 76.42824009468998
- type: manhattan_pearson
value: 77.69118862737736
- type: manhattan_spearman
value: 76.25568104762812
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.42012286935325
- type: cos_sim_spearman
value: 88.15654297884122
- type: euclidean_pearson
value: 87.34082819427852
- type: euclidean_spearman
value: 88.06333589547084
- type: manhattan_pearson
value: 87.25115596784842
- type: manhattan_spearman
value: 87.9559927695203
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.88222044996712
- type: cos_sim_spearman
value: 84.28476589061077
- type: euclidean_pearson
value: 83.17399758058309
- type: euclidean_spearman
value: 83.85497357244542
- type: manhattan_pearson
value: 83.0308397703786
- type: manhattan_spearman
value: 83.71554539935046
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.20682986257339
- type: cos_sim_spearman
value: 79.94567120362092
- type: euclidean_pearson
value: 79.43122480368902
- type: euclidean_spearman
value: 79.94802077264987
- type: manhattan_pearson
value: 79.32653021527081
- type: manhattan_spearman
value: 79.80961146709178
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 74.46578144394383
- type: cos_sim_spearman
value: 74.52496637472179
- type: euclidean_pearson
value: 72.2903807076809
- type: euclidean_spearman
value: 73.55549359771645
- type: manhattan_pearson
value: 72.09324837709393
- type: manhattan_spearman
value: 73.36743103606581
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 71.37272335116
- type: cos_sim_spearman
value: 71.26702117766037
- type: euclidean_pearson
value: 67.114829954434
- type: euclidean_spearman
value: 66.37938893947761
- type: manhattan_pearson
value: 66.79688574095246
- type: manhattan_spearman
value: 66.17292828079667
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.61016770129092
- type: cos_sim_spearman
value: 82.08515426632214
- type: euclidean_pearson
value: 80.557340361131
- type: euclidean_spearman
value: 80.37585812266175
- type: manhattan_pearson
value: 80.6782873404285
- type: manhattan_spearman
value: 80.6678073032024
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.00150745350108
- type: cos_sim_spearman
value: 87.83441972211425
- type: euclidean_pearson
value: 87.94826702308792
- type: euclidean_spearman
value: 87.46143974860725
- type: manhattan_pearson
value: 87.97560344306105
- type: manhattan_spearman
value: 87.5267102829796
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 64.76325252267235
- type: cos_sim_spearman
value: 63.32615095463905
- type: euclidean_pearson
value: 64.07920669155716
- type: euclidean_spearman
value: 61.21409893072176
- type: manhattan_pearson
value: 64.26308625680016
- type: manhattan_spearman
value: 61.2438185254079
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.82644463022595
- type: cos_sim_spearman
value: 76.50381269945073
- type: euclidean_pearson
value: 75.1328548315934
- type: euclidean_spearman
value: 75.63761139408453
- type: manhattan_pearson
value: 75.18610101241407
- type: manhattan_spearman
value: 75.30669266354164
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.49994164686832
- type: cos_sim_spearman
value: 86.73743986245549
- type: euclidean_pearson
value: 86.8272894387145
- type: euclidean_spearman
value: 85.97608491000507
- type: manhattan_pearson
value: 86.74960140396779
- type: manhattan_spearman
value: 85.79285984190273
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.58172210788469
- type: cos_sim_spearman
value: 80.17516468334607
- type: euclidean_pearson
value: 77.56537843470504
- type: euclidean_spearman
value: 77.57264627395521
- type: manhattan_pearson
value: 78.09703521695943
- type: manhattan_spearman
value: 78.15942760916954
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.7589932931751
- type: cos_sim_spearman
value: 80.15210089028162
- type: euclidean_pearson
value: 77.54135223516057
- type: euclidean_spearman
value: 77.52697996368764
- type: manhattan_pearson
value: 77.65734439572518
- type: manhattan_spearman
value: 77.77702992016121
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.16682365511267
- type: cos_sim_spearman
value: 79.25311267628506
- type: euclidean_pearson
value: 77.54882036762244
- type: euclidean_spearman
value: 77.33212935194827
- type: manhattan_pearson
value: 77.98405516064015
- type: manhattan_spearman
value: 77.85075717865719
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.10473294775917
- type: cos_sim_spearman
value: 61.82780474476838
- type: euclidean_pearson
value: 45.885111672377256
- type: euclidean_spearman
value: 56.88306351932454
- type: manhattan_pearson
value: 46.101218127323186
- type: manhattan_spearman
value: 56.80953694186333
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 45.781923079584146
- type: cos_sim_spearman
value: 55.95098449691107
- type: euclidean_pearson
value: 25.4571031323205
- type: euclidean_spearman
value: 49.859978118078935
- type: manhattan_pearson
value: 25.624938455041384
- type: manhattan_spearman
value: 49.99546185049401
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.00618133997907
- type: cos_sim_spearman
value: 66.57896677718321
- type: euclidean_pearson
value: 42.60118466388821
- type: euclidean_spearman
value: 62.8210759715209
- type: manhattan_pearson
value: 42.63446860604094
- type: manhattan_spearman
value: 62.73803068925271
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 28.460759121626943
- type: cos_sim_spearman
value: 34.13459007469131
- type: euclidean_pearson
value: 6.0917739325525195
- type: euclidean_spearman
value: 27.9947262664867
- type: manhattan_pearson
value: 6.16877864169911
- type: manhattan_spearman
value: 28.00664163971514
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.42546621771696
- type: cos_sim_spearman
value: 63.699663168970474
- type: euclidean_pearson
value: 38.12085278789738
- type: euclidean_spearman
value: 58.12329140741536
- type: manhattan_pearson
value: 37.97364549443335
- type: manhattan_spearman
value: 57.81545502318733
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 46.82241380954213
- type: cos_sim_spearman
value: 57.86569456006391
- type: euclidean_pearson
value: 31.80480070178813
- type: euclidean_spearman
value: 52.484000620130104
- type: manhattan_pearson
value: 31.952708554646097
- type: manhattan_spearman
value: 52.8560972356195
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 52.00447170498087
- type: cos_sim_spearman
value: 60.664116225735164
- type: euclidean_pearson
value: 33.87382555421702
- type: euclidean_spearman
value: 55.74649067458667
- type: manhattan_pearson
value: 33.99117246759437
- type: manhattan_spearman
value: 55.98749034923899
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.06497233105448
- type: cos_sim_spearman
value: 65.62968801135676
- type: euclidean_pearson
value: 47.482076613243905
- type: euclidean_spearman
value: 62.65137791498299
- type: manhattan_pearson
value: 47.57052626104093
- type: manhattan_spearman
value: 62.436916516613294
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.49397298562575
- type: cos_sim_spearman
value: 74.79604041187868
- type: euclidean_pearson
value: 49.661891561317795
- type: euclidean_spearman
value: 70.31535537621006
- type: manhattan_pearson
value: 49.553715741850006
- type: manhattan_spearman
value: 70.24779344636806
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.640574515348696
- type: cos_sim_spearman
value: 54.927959317689
- type: euclidean_pearson
value: 29.00139666967476
- type: euclidean_spearman
value: 41.86386566971605
- type: manhattan_pearson
value: 29.47411067730344
- type: manhattan_spearman
value: 42.337438424952786
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 68.14095292259312
- type: cos_sim_spearman
value: 73.99017581234789
- type: euclidean_pearson
value: 46.46304297872084
- type: euclidean_spearman
value: 60.91834114800041
- type: manhattan_pearson
value: 47.07072666338692
- type: manhattan_spearman
value: 61.70415727977926
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.27184653359575
- type: cos_sim_spearman
value: 77.76070252418626
- type: euclidean_pearson
value: 62.30586577544778
- type: euclidean_spearman
value: 75.14246629110978
- type: manhattan_pearson
value: 62.328196884927046
- type: manhattan_spearman
value: 75.1282792981433
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.59448528829957
- type: cos_sim_spearman
value: 70.37277734222123
- type: euclidean_pearson
value: 57.63145565721123
- type: euclidean_spearman
value: 66.10113048304427
- type: manhattan_pearson
value: 57.18897811586808
- type: manhattan_spearman
value: 66.5595511215901
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.37520607720838
- type: cos_sim_spearman
value: 69.92282148997948
- type: euclidean_pearson
value: 40.55768770125291
- type: euclidean_spearman
value: 55.189128944669605
- type: manhattan_pearson
value: 41.03566433468883
- type: manhattan_spearman
value: 55.61251893174558
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.791929533771835
- type: cos_sim_spearman
value: 66.45819707662093
- type: euclidean_pearson
value: 39.03686018511092
- type: euclidean_spearman
value: 56.01282695640428
- type: manhattan_pearson
value: 38.91586623619632
- type: manhattan_spearman
value: 56.69394943612747
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 47.82224468473866
- type: cos_sim_spearman
value: 59.467307194781164
- type: euclidean_pearson
value: 27.428459190256145
- type: euclidean_spearman
value: 60.83463107397519
- type: manhattan_pearson
value: 27.487391578496638
- type: manhattan_spearman
value: 61.281380460246496
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 16.306666792752644
- type: cos_sim_spearman
value: 39.35486427252405
- type: euclidean_pearson
value: -2.7887154897955435
- type: euclidean_spearman
value: 27.1296051831719
- type: manhattan_pearson
value: -3.202291270581297
- type: manhattan_spearman
value: 26.32895849218158
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.67006803805076
- type: cos_sim_spearman
value: 73.24670207647144
- type: euclidean_pearson
value: 46.91884681500483
- type: euclidean_spearman
value: 16.903085094570333
- type: manhattan_pearson
value: 46.88391675325812
- type: manhattan_spearman
value: 28.17180849095055
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.79555591223837
- type: cos_sim_spearman
value: 85.63658602085185
- type: euclidean_pearson
value: 85.22080894037671
- type: euclidean_spearman
value: 85.54113580167038
- type: manhattan_pearson
value: 85.1639505960118
- type: manhattan_spearman
value: 85.43502665436196
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 80.73900991689766
- type: mrr
value: 94.81624131133934
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.678000000000004
- type: map_at_10
value: 65.135
- type: map_at_100
value: 65.824
- type: map_at_1000
value: 65.852
- type: map_at_3
value: 62.736000000000004
- type: map_at_5
value: 64.411
- type: mrr_at_1
value: 58.333
- type: mrr_at_10
value: 66.5
- type: mrr_at_100
value: 67.053
- type: mrr_at_1000
value: 67.08
- type: mrr_at_3
value: 64.944
- type: mrr_at_5
value: 65.89399999999999
- type: ndcg_at_1
value: 58.333
- type: ndcg_at_10
value: 69.34700000000001
- type: ndcg_at_100
value: 72.32
- type: ndcg_at_1000
value: 73.014
- type: ndcg_at_3
value: 65.578
- type: ndcg_at_5
value: 67.738
- type: precision_at_1
value: 58.333
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 25.444
- type: precision_at_5
value: 16.933
- type: recall_at_1
value: 55.678000000000004
- type: recall_at_10
value: 80.72200000000001
- type: recall_at_100
value: 93.93299999999999
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 70.783
- type: recall_at_5
value: 75.978
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.74653465346535
- type: cos_sim_ap
value: 93.01476369929063
- type: cos_sim_f1
value: 86.93009118541033
- type: cos_sim_precision
value: 88.09034907597535
- type: cos_sim_recall
value: 85.8
- type: dot_accuracy
value: 99.22970297029703
- type: dot_ap
value: 51.58725659485144
- type: dot_f1
value: 53.51351351351352
- type: dot_precision
value: 58.235294117647065
- type: dot_recall
value: 49.5
- type: euclidean_accuracy
value: 99.74356435643564
- type: euclidean_ap
value: 92.40332894384368
- type: euclidean_f1
value: 86.97838109602817
- type: euclidean_precision
value: 87.46208291203236
- type: euclidean_recall
value: 86.5
- type: manhattan_accuracy
value: 99.73069306930694
- type: manhattan_ap
value: 92.01320815721121
- type: manhattan_f1
value: 86.4135864135864
- type: manhattan_precision
value: 86.32734530938124
- type: manhattan_recall
value: 86.5
- type: max_accuracy
value: 99.74653465346535
- type: max_ap
value: 93.01476369929063
- type: max_f1
value: 86.97838109602817
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 55.2660514302523
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 30.4637783572547
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.41377758357637
- type: mrr
value: 50.138451213818854
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 28.887846011166594
- type: cos_sim_spearman
value: 30.10823258355903
- type: dot_pearson
value: 12.888049550236385
- type: dot_spearman
value: 12.827495903098123
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.21
- type: map_at_10
value: 1.667
- type: map_at_100
value: 9.15
- type: map_at_1000
value: 22.927
- type: map_at_3
value: 0.573
- type: map_at_5
value: 0.915
- type: mrr_at_1
value: 80
- type: mrr_at_10
value: 87.167
- type: mrr_at_100
value: 87.167
- type: mrr_at_1000
value: 87.167
- type: mrr_at_3
value: 85.667
- type: mrr_at_5
value: 87.167
- type: ndcg_at_1
value: 76
- type: ndcg_at_10
value: 69.757
- type: ndcg_at_100
value: 52.402
- type: ndcg_at_1000
value: 47.737
- type: ndcg_at_3
value: 71.866
- type: ndcg_at_5
value: 72.225
- type: precision_at_1
value: 80
- type: precision_at_10
value: 75
- type: precision_at_100
value: 53.959999999999994
- type: precision_at_1000
value: 21.568
- type: precision_at_3
value: 76.667
- type: precision_at_5
value: 78
- type: recall_at_1
value: 0.21
- type: recall_at_10
value: 1.9189999999999998
- type: recall_at_100
value: 12.589
- type: recall_at_1000
value: 45.312000000000005
- type: recall_at_3
value: 0.61
- type: recall_at_5
value: 1.019
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 90.06
- type: precision
value: 89.17333333333333
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 56.06936416184971
- type: f1
value: 50.87508028259473
- type: precision
value: 48.97398843930635
- type: recall
value: 56.06936416184971
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.3170731707317
- type: f1
value: 52.96080139372822
- type: precision
value: 51.67861124382864
- type: recall
value: 57.3170731707317
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.67333333333333
- type: precision
value: 91.90833333333333
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97.07333333333332
- type: precision
value: 96.79500000000002
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.2
- type: precision
value: 92.48333333333333
- type: recall
value: 94.69999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.9
- type: f1
value: 91.26666666666667
- type: precision
value: 90.59444444444445
- type: recall
value: 92.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 34.32835820895522
- type: f1
value: 29.074180380150533
- type: precision
value: 28.068207322920596
- type: recall
value: 34.32835820895522
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.5
- type: f1
value: 74.3945115995116
- type: precision
value: 72.82967843459222
- type: recall
value: 78.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.34146341463415
- type: f1
value: 61.2469400518181
- type: precision
value: 59.63977756660683
- type: recall
value: 66.34146341463415
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.9
- type: f1
value: 76.90349206349207
- type: precision
value: 75.32921568627451
- type: recall
value: 80.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.93317132442284
- type: f1
value: 81.92519105034295
- type: precision
value: 80.71283920615635
- type: recall
value: 84.93317132442284
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.1304347826087
- type: f1
value: 65.22394755003451
- type: precision
value: 62.912422360248435
- type: recall
value: 71.1304347826087
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.82608695652173
- type: f1
value: 75.55693581780538
- type: precision
value: 73.79420289855072
- type: recall
value: 79.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74
- type: f1
value: 70.51022222222223
- type: precision
value: 69.29673599347512
- type: recall
value: 74
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.7
- type: f1
value: 74.14238095238095
- type: precision
value: 72.27214285714285
- type: recall
value: 78.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.97466827503016
- type: f1
value: 43.080330405420874
- type: precision
value: 41.36505499593557
- type: recall
value: 48.97466827503016
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.60000000000001
- type: f1
value: 86.62333333333333
- type: precision
value: 85.225
- type: recall
value: 89.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.2
- type: f1
value: 39.5761253006253
- type: precision
value: 37.991358436312
- type: recall
value: 45.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.70333333333333
- type: precision
value: 85.53166666666667
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.095238095238095
- type: f1
value: 44.60650460650461
- type: precision
value: 42.774116796477045
- type: recall
value: 50.095238095238095
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.4
- type: f1
value: 58.35967261904762
- type: precision
value: 56.54857142857143
- type: recall
value: 63.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.2
- type: f1
value: 87.075
- type: precision
value: 86.12095238095239
- type: recall
value: 89.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8
- type: f1
value: 95.90333333333334
- type: precision
value: 95.50833333333333
- type: recall
value: 96.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.9
- type: f1
value: 88.6288888888889
- type: precision
value: 87.61607142857142
- type: recall
value: 90.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.2
- type: f1
value: 60.54377630539395
- type: precision
value: 58.89434482711381
- type: recall
value: 65.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87
- type: f1
value: 84.32412698412699
- type: precision
value: 83.25527777777778
- type: recall
value: 87
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.7
- type: f1
value: 63.07883541295306
- type: precision
value: 61.06117424242426
- type: recall
value: 68.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.78333333333335
- type: precision
value: 90.86666666666667
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 96.96666666666667
- type: precision
value: 96.61666666666667
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.27493261455525
- type: f1
value: 85.90745732255168
- type: precision
value: 84.91389637616052
- type: recall
value: 88.27493261455525
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.5982905982906
- type: f1
value: 88.4900284900285
- type: precision
value: 87.57122507122507
- type: recall
value: 90.5982905982906
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.90769841269842
- type: precision
value: 85.80178571428571
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.5
- type: f1
value: 78.36796536796538
- type: precision
value: 76.82196969696969
- type: recall
value: 82.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.48846960167715
- type: f1
value: 66.78771089148448
- type: precision
value: 64.98302885095339
- type: recall
value: 71.48846960167715
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.50333333333333
- type: precision
value: 91.77499999999999
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.20622568093385
- type: f1
value: 66.83278891450098
- type: precision
value: 65.35065777283677
- type: recall
value: 71.20622568093385
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.717948717948715
- type: f1
value: 43.53146853146853
- type: precision
value: 42.04721204721204
- type: recall
value: 48.717948717948715
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 58.5
- type: f1
value: 53.8564991863928
- type: precision
value: 52.40329436122275
- type: recall
value: 58.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.8
- type: f1
value: 88.29
- type: precision
value: 87.09166666666667
- type: recall
value: 90.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.28971962616822
- type: f1
value: 62.63425307817832
- type: precision
value: 60.98065939771546
- type: recall
value: 67.28971962616822
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lit-eng)
type: mteb/tatoeba-bitext-mining
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.7
- type: f1
value: 75.5264472455649
- type: precision
value: 74.38205086580086
- type: recall
value: 78.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.7
- type: f1
value: 86.10809523809525
- type: precision
value: 85.07602564102565
- type: recall
value: 88.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lfn-eng)
type: mteb/tatoeba-bitext-mining
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 56.99999999999999
- type: f1
value: 52.85487521402737
- type: precision
value: 51.53985162713104
- type: recall
value: 56.99999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (zsm-eng)
type: mteb/tatoeba-bitext-mining
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94
- type: f1
value: 92.45333333333333
- type: precision
value: 91.79166666666667
- type: recall
value: 94
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ita-eng)
type: mteb/tatoeba-bitext-mining
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.30000000000001
- type: f1
value: 90.61333333333333
- type: precision
value: 89.83333333333331
- type: recall
value: 92.30000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cmn-eng)
type: mteb/tatoeba-bitext-mining
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.34555555555555
- type: precision
value: 92.75416666666668
- type: recall
value: 94.69999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lvs-eng)
type: mteb/tatoeba-bitext-mining
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.2
- type: f1
value: 76.6563035113035
- type: precision
value: 75.3014652014652
- type: recall
value: 80.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (glg-eng)
type: mteb/tatoeba-bitext-mining
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.7
- type: f1
value: 82.78689263765207
- type: precision
value: 82.06705086580087
- type: recall
value: 84.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ceb-eng)
type: mteb/tatoeba-bitext-mining
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.33333333333333
- type: f1
value: 45.461523661523664
- type: precision
value: 43.93545574795575
- type: recall
value: 50.33333333333333
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bre-eng)
type: mteb/tatoeba-bitext-mining
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.6000000000000005
- type: f1
value: 5.442121400446441
- type: precision
value: 5.146630385487529
- type: recall
value: 6.6000000000000005
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ben-eng)
type: mteb/tatoeba-bitext-mining
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85
- type: f1
value: 81.04666666666667
- type: precision
value: 79.25
- type: recall
value: 85
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swg-eng)
type: mteb/tatoeba-bitext-mining
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.32142857142857
- type: f1
value: 42.333333333333336
- type: precision
value: 40.69196428571429
- type: recall
value: 47.32142857142857
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arq-eng)
type: mteb/tatoeba-bitext-mining
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 30.735455543358945
- type: f1
value: 26.73616790022338
- type: precision
value: 25.397823220451283
- type: recall
value: 30.735455543358945
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kab-eng)
type: mteb/tatoeba-bitext-mining
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 25.1
- type: f1
value: 21.975989896371022
- type: precision
value: 21.059885632257203
- type: recall
value: 25.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fra-eng)
type: mteb/tatoeba-bitext-mining
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.75666666666666
- type: precision
value: 92.06166666666665
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (por-eng)
type: mteb/tatoeba-bitext-mining
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.74
- type: precision
value: 92.09166666666667
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tat-eng)
type: mteb/tatoeba-bitext-mining
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.3
- type: f1
value: 66.922442002442
- type: precision
value: 65.38249567099568
- type: recall
value: 71.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (oci-eng)
type: mteb/tatoeba-bitext-mining
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 40.300000000000004
- type: f1
value: 35.78682789299971
- type: precision
value: 34.66425128716588
- type: recall
value: 40.300000000000004
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pol-eng)
type: mteb/tatoeba-bitext-mining
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.82333333333334
- type: precision
value: 94.27833333333334
- type: recall
value: 96
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (war-eng)
type: mteb/tatoeba-bitext-mining
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 51.1
- type: f1
value: 47.179074753133584
- type: precision
value: 46.06461044702424
- type: recall
value: 51.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (aze-eng)
type: mteb/tatoeba-bitext-mining
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.7
- type: f1
value: 84.71
- type: precision
value: 83.46166666666667
- type: recall
value: 87.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (vie-eng)
type: mteb/tatoeba-bitext-mining
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.8
- type: f1
value: 94.68333333333334
- type: precision
value: 94.13333333333334
- type: recall
value: 95.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nno-eng)
type: mteb/tatoeba-bitext-mining
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.39999999999999
- type: f1
value: 82.5577380952381
- type: precision
value: 81.36833333333334
- type: recall
value: 85.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cha-eng)
type: mteb/tatoeba-bitext-mining
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21.16788321167883
- type: f1
value: 16.948865627297987
- type: precision
value: 15.971932568647897
- type: recall
value: 21.16788321167883
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mhr-eng)
type: mteb/tatoeba-bitext-mining
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.9
- type: f1
value: 5.515526831658907
- type: precision
value: 5.141966366966367
- type: recall
value: 6.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dan-eng)
type: mteb/tatoeba-bitext-mining
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.39666666666668
- type: precision
value: 90.58666666666667
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ell-eng)
type: mteb/tatoeba-bitext-mining
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.2
- type: f1
value: 89.95666666666666
- type: precision
value: 88.92833333333333
- type: recall
value: 92.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (amh-eng)
type: mteb/tatoeba-bitext-mining
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.76190476190477
- type: f1
value: 74.93386243386244
- type: precision
value: 73.11011904761904
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value: 79.76190476190477
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pam-eng)
type: mteb/tatoeba-bitext-mining
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.799999999999999
- type: f1
value: 6.921439712248537
- type: precision
value: 6.489885109680683
- type: recall
value: 8.799999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hsb-eng)
type: mteb/tatoeba-bitext-mining
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.75569358178054
- type: f1
value: 40.34699501312631
- type: precision
value: 38.57886764719063
- type: recall
value: 45.75569358178054
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (srp-eng)
type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.08333333333333
- type: precision
value: 88.01666666666668
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (epo-eng)
type: mteb/tatoeba-bitext-mining
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.60000000000001
- type: f1
value: 92.06690476190477
- type: precision
value: 91.45095238095239
- type: recall
value: 93.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kzj-eng)
type: mteb/tatoeba-bitext-mining
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.5
- type: f1
value: 6.200363129378736
- type: precision
value: 5.89115314822466
- type: recall
value: 7.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (awa-eng)
type: mteb/tatoeba-bitext-mining
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.59307359307358
- type: f1
value: 68.38933553219267
- type: precision
value: 66.62698412698413
- type: recall
value: 73.59307359307358
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fao-eng)
type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.8473282442748
- type: f1
value: 64.72373682297346
- type: precision
value: 62.82834214131924
- type: recall
value: 69.8473282442748
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mal-eng)
type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.5254730713246
- type: f1
value: 96.72489082969432
- type: precision
value: 96.33672974284326
- type: recall
value: 97.5254730713246
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ile-eng)
type: mteb/tatoeba-bitext-mining
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.6
- type: f1
value: 72.42746031746033
- type: precision
value: 71.14036630036631
- type: recall
value: 75.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.24293785310734
- type: f1
value: 88.86064030131826
- type: precision
value: 87.73540489642184
- type: recall
value: 91.24293785310734
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.2
- type: f1
value: 4.383083659794954
- type: precision
value: 4.027861324289673
- type: recall
value: 6.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.8
- type: f1
value: 84.09428571428572
- type: precision
value: 83.00333333333333
- type: recall
value: 86.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.699999999999996
- type: f1
value: 56.1584972394755
- type: precision
value: 54.713456330903135
- type: recall
value: 60.699999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.2
- type: f1
value: 80.66190476190475
- type: precision
value: 79.19690476190476
- type: recall
value: 84.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.33
- type: precision
value: 90.45
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.3
- type: f1
value: 5.126828976748276
- type: precision
value: 4.853614328966668
- type: recall
value: 6.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.76943699731903
- type: f1
value: 77.82873739308057
- type: precision
value: 76.27622452019234
- type: recall
value: 81.76943699731903
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.30000000000001
- type: f1
value: 90.29666666666665
- type: precision
value: 89.40333333333334
- type: recall
value: 92.30000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 29.249011857707508
- type: f1
value: 24.561866096392947
- type: precision
value: 23.356583740215456
- type: recall
value: 29.249011857707508
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.46478873239437
- type: f1
value: 73.23943661971832
- type: precision
value: 71.66666666666667
- type: recall
value: 77.46478873239437
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 20.35928143712575
- type: f1
value: 15.997867865075824
- type: precision
value: 14.882104658301346
- type: recall
value: 20.35928143712575
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.2
- type: f1
value: 90.25999999999999
- type: precision
value: 89.45333333333335
- type: recall
value: 92.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 23.15270935960591
- type: f1
value: 19.65673625772148
- type: precision
value: 18.793705293464992
- type: recall
value: 23.15270935960591
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.154929577464785
- type: f1
value: 52.3868463305083
- type: precision
value: 50.14938113529662
- type: recall
value: 59.154929577464785
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.51282051282051
- type: f1
value: 66.8089133089133
- type: precision
value: 65.37645687645687
- type: recall
value: 70.51282051282051
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93
- type: precision
value: 92.23333333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.62212943632568
- type: f1
value: 34.3278276962583
- type: precision
value: 33.07646935732408
- type: recall
value: 38.62212943632568
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 28.1
- type: f1
value: 23.579609223054604
- type: precision
value: 22.39622774921555
- type: recall
value: 28.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.27361563517914
- type: f1
value: 85.12486427795874
- type: precision
value: 83.71335504885994
- type: recall
value: 88.27361563517914
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.6
- type: f1
value: 86.39928571428571
- type: precision
value: 85.4947557997558
- type: recall
value: 88.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.5
- type: f1
value: 83.77952380952381
- type: precision
value: 82.67602564102565
- type: recall
value: 86.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.52755905511812
- type: f1
value: 75.3055868016498
- type: precision
value: 73.81889763779527
- type: recall
value: 79.52755905511812
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.9
- type: f1
value: 73.76261904761905
- type: precision
value: 72.11670995670995
- type: recall
value: 77.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 53.8781163434903
- type: f1
value: 47.25804051288816
- type: precision
value: 45.0603482390186
- type: recall
value: 53.8781163434903
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.10000000000001
- type: f1
value: 88.88
- type: precision
value: 87.96333333333334
- type: recall
value: 91.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.46153846153847
- type: f1
value: 34.43978243978244
- type: precision
value: 33.429487179487175
- type: recall
value: 38.46153846153847
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.9
- type: f1
value: 86.19888888888887
- type: precision
value: 85.07440476190476
- type: recall
value: 88.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.9
- type: f1
value: 82.58857142857143
- type: precision
value: 81.15666666666667
- type: recall
value: 85.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.8
- type: f1
value: 83.36999999999999
- type: precision
value: 81.86833333333333
- type: recall
value: 86.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.51415094339622
- type: f1
value: 63.195000099481234
- type: precision
value: 61.394033442972116
- type: recall
value: 68.51415094339622
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.5
- type: f1
value: 86.14603174603175
- type: precision
value: 85.1162037037037
- type: recall
value: 88.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.62043795620438
- type: f1
value: 94.40389294403892
- type: precision
value: 93.7956204379562
- type: recall
value: 95.62043795620438
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.8
- type: f1
value: 78.6532178932179
- type: precision
value: 77.46348795840176
- type: recall
value: 81.8
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.603
- type: map_at_10
value: 8.5
- type: map_at_100
value: 12.985
- type: map_at_1000
value: 14.466999999999999
- type: map_at_3
value: 4.859999999999999
- type: map_at_5
value: 5.817
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 42.331
- type: mrr_at_100
value: 43.592999999999996
- type: mrr_at_1000
value: 43.592999999999996
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 39.966
- type: ndcg_at_1
value: 26.531
- type: ndcg_at_10
value: 21.353
- type: ndcg_at_100
value: 31.087999999999997
- type: ndcg_at_1000
value: 43.163000000000004
- type: ndcg_at_3
value: 22.999
- type: ndcg_at_5
value: 21.451
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 19.387999999999998
- type: precision_at_100
value: 6.265
- type: precision_at_1000
value: 1.4160000000000001
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 21.224
- type: recall_at_1
value: 2.603
- type: recall_at_10
value: 14.474
- type: recall_at_100
value: 40.287
- type: recall_at_1000
value: 76.606
- type: recall_at_3
value: 5.978
- type: recall_at_5
value: 7.819
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.7848
- type: ap
value: 13.661023167088224
- type: f1
value: 53.61686134460943
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.28183361629882
- type: f1
value: 61.55481034919965
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 35.972128420092396
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.59933241938367
- type: cos_sim_ap
value: 72.20760361208136
- type: cos_sim_f1
value: 66.4447731755424
- type: cos_sim_precision
value: 62.35539102267469
- type: cos_sim_recall
value: 71.10817941952506
- type: dot_accuracy
value: 78.98313166835548
- type: dot_ap
value: 44.492521645493795
- type: dot_f1
value: 45.814889336016094
- type: dot_precision
value: 37.02439024390244
- type: dot_recall
value: 60.07915567282321
- type: euclidean_accuracy
value: 85.3907134767837
- type: euclidean_ap
value: 71.53847289080343
- type: euclidean_f1
value: 65.95952206778834
- type: euclidean_precision
value: 61.31006346328196
- type: euclidean_recall
value: 71.37203166226914
- type: manhattan_accuracy
value: 85.40859510043511
- type: manhattan_ap
value: 71.49664104395515
- type: manhattan_f1
value: 65.98569969356485
- type: manhattan_precision
value: 63.928748144482924
- type: manhattan_recall
value: 68.17941952506597
- type: max_accuracy
value: 85.59933241938367
- type: max_ap
value: 72.20760361208136
- type: max_f1
value: 66.4447731755424
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.83261536073273
- type: cos_sim_ap
value: 85.48178133644264
- type: cos_sim_f1
value: 77.87816307403935
- type: cos_sim_precision
value: 75.88953021114926
- type: cos_sim_recall
value: 79.97382198952879
- type: dot_accuracy
value: 79.76287499514883
- type: dot_ap
value: 59.17438838475084
- type: dot_f1
value: 56.34566667855996
- type: dot_precision
value: 52.50349092359864
- type: dot_recall
value: 60.794579611949494
- type: euclidean_accuracy
value: 88.76857996662397
- type: euclidean_ap
value: 85.22764834359887
- type: euclidean_f1
value: 77.65379751543554
- type: euclidean_precision
value: 75.11152683839401
- type: euclidean_recall
value: 80.37419156144134
- type: manhattan_accuracy
value: 88.6987231730508
- type: manhattan_ap
value: 85.18907981724007
- type: manhattan_f1
value: 77.51967028849757
- type: manhattan_precision
value: 75.49992701795358
- type: manhattan_recall
value: 79.65044656606098
- type: max_accuracy
value: 88.83261536073273
- type: max_ap
value: 85.48178133644264
- type: max_f1
value: 77.87816307403935
---
# jeremyys/multilingual-e5-base-Q4_K_M-GGUF
This model was converted to GGUF format from [`intfloat/multilingual-e5-base`](https://huggingface.co/intfloat/multilingual-e5-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-base) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo jeremyys/multilingual-e5-base-Q4_K_M-GGUF --hf-file multilingual-e5-base-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jeremyys/multilingual-e5-base-Q4_K_M-GGUF --hf-file multilingual-e5-base-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo jeremyys/multilingual-e5-base-Q4_K_M-GGUF --hf-file multilingual-e5-base-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jeremyys/multilingual-e5-base-Q4_K_M-GGUF --hf-file multilingual-e5-base-q4_k_m.gguf -c 2048
```
|
Baselhany/Graduation_Project_Distil_Whisper_base | Baselhany | 2025-06-16T02:16:43Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-02T11:09:34Z | ---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper base AR - BA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper base AR - BA
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1877
- Wer: 0.4111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 6.1064 | 1.0 | 469 | 0.1923 | 0.4788 |
| 5.2469 | 2.0 | 938 | 0.1903 | 0.4733 |
| 3.4127 | 3.0 | 1407 | 0.1934 | 0.4941 |
| 2.6938 | 4.0 | 1876 | 0.1795 | 0.4665 |
| 2.1423 | 5.0 | 2345 | 0.1849 | 0.4469 |
| 1.7651 | 6.0 | 2814 | 0.1772 | 0.4403 |
| 1.6011 | 7.0 | 3283 | 0.1775 | 0.4048 |
| 1.3887 | 8.0 | 3752 | 0.1756 | 0.4032 |
| 1.2493 | 9.0 | 4221 | 0.1735 | 0.4060 |
| 1.0678 | 10.0 | 4690 | 0.1744 | 0.4228 |
| 1.0113 | 11.0 | 5159 | 0.1704 | 0.4175 |
| 0.9464 | 12.0 | 5628 | 0.1725 | 0.4263 |
| 0.8923 | 13.0 | 6097 | 0.1711 | 0.4159 |
| 0.8727 | 14.0 | 6566 | 0.1720 | 0.4204 |
| 0.769 | 14.9685 | 7020 | 0.1719 | 0.4268 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
CeciGonSer/translation_pu_es_sintetico_chamo_hel_1ep | CeciGonSer | 2025-06-16T02:16:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-15T23:59:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
semtwo/kobart-base-v2-with-wiki-dataset | semtwo | 2025-06-16T02:15:50Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-16T02:15:50Z | ---
license: other
license_name: kobart-base-v2
license_link: LICENSE
---
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.05_epoch2 | MinaMila | 2025-06-16T02:15:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T02:13:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
EasonXiao-888/MindOmni | EasonXiao-888 | 2025-06-16T02:14:57Z | 56 | 1 | diffusers | [
"diffusers",
"safetensors",
"any-to-any",
"arxiv:2505.13031",
"license:apache-2.0",
"region:us"
] | any-to-any | 2025-06-06T15:54:39Z | ---
license: apache-2.0
pipeline_tag: any-to-any
---
This repository contains the MindOmni model described in the paper [MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO](https://huggingface.co/papers/2505.13031).
Code: https://github.com/TencentARC/MindOmni |
JunaidSadiq/deepseek_0528_reasoning | JunaidSadiq | 2025-06-16T02:14:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T02:13:42Z | ---
base_model: unsloth/deepseek-r1-0528-qwen3-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** JunaidSadiq
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-0528-qwen3-8b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.5_epoch1 | MinaMila | 2025-06-16T02:13:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T02:11:10Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
oceanmall/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark | oceanmall | 2025-06-16T02:10:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rabid noisy aardvark",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T14:47:09Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rabid noisy aardvark
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="oceanmall/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rabid_noisy_aardvark", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mlx-community/llm-jp-3.1-1.8b-instruct4-8bit | mlx-community | 2025-06-16T02:09:40Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"base_model:llm-jp/llm-jp-3.1-1.8b-instruct4",
"base_model:quantized:llm-jp/llm-jp-3.1-1.8b-instruct4",
"license:apache-2.0",
"8-bit",
"region:us"
] | text-generation | 2025-06-16T02:06:24Z | ---
license: apache-2.0
language:
- en
- ja
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
library_name: mlx
inference: false
tags:
- mlx
base_model: llm-jp/llm-jp-3.1-1.8b-instruct4
---
# mlx-community/llm-jp-3.1-1.8b-instruct4-8bit
This model [mlx-community/llm-jp-3.1-1.8b-instruct4-8bit](https://huggingface.co/mlx-community/llm-jp-3.1-1.8b-instruct4-8bit) was
converted to MLX format from [llm-jp/llm-jp-3.1-1.8b-instruct4](https://huggingface.co/llm-jp/llm-jp-3.1-1.8b-instruct4)
using mlx-lm version **0.24.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/llm-jp-3.1-1.8b-instruct4-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
jeremyys/multilingual-e5-base-Q5_0-GGUF | jeremyys | 2025-06-16T02:08:48Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"gguf",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"llama-cpp",
"gguf-my-repo",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"base_model:intfloat/multilingual-e5-base",
"base_model:quantized:intfloat/multilingual-e5-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | sentence-similarity | 2025-06-16T02:08:44Z | ---
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
- llama-cpp
- gguf-my-repo
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
base_model: intfloat/multilingual-e5-base
model-index:
- name: multilingual-e5-base
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 78.97014925373135
- type: ap
value: 43.69351129103008
- type: f1
value: 73.38075030070492
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.7237687366167
- type: ap
value: 82.22089859962671
- type: f1
value: 69.95532758884401
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.65517241379312
- type: ap
value: 28.507918657094738
- type: f1
value: 66.84516013726119
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.32976445396146
- type: ap
value: 20.720481637566014
- type: f1
value: 59.78002763416003
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 90.63775
- type: ap
value: 87.22277903861716
- type: f1
value: 90.60378636386807
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.546
- type: f1
value: 44.05666638370923
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 41.828
- type: f1
value: 41.2710255644252
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.534
- type: f1
value: 39.820743174270326
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.684
- type: f1
value: 39.11052682815307
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.436
- type: f1
value: 37.07082931930871
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.226000000000006
- type: f1
value: 36.65372077739185
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.831000000000003
- type: map_at_10
value: 36.42
- type: map_at_100
value: 37.699
- type: map_at_1000
value: 37.724000000000004
- type: map_at_3
value: 32.207
- type: map_at_5
value: 34.312
- type: mrr_at_1
value: 23.257
- type: mrr_at_10
value: 36.574
- type: mrr_at_100
value: 37.854
- type: mrr_at_1000
value: 37.878
- type: mrr_at_3
value: 32.385000000000005
- type: mrr_at_5
value: 34.48
- type: ndcg_at_1
value: 22.831000000000003
- type: ndcg_at_10
value: 44.230000000000004
- type: ndcg_at_100
value: 49.974000000000004
- type: ndcg_at_1000
value: 50.522999999999996
- type: ndcg_at_3
value: 35.363
- type: ndcg_at_5
value: 39.164
- type: precision_at_1
value: 22.831000000000003
- type: precision_at_10
value: 6.935
- type: precision_at_100
value: 0.9520000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.841
- type: precision_at_5
value: 10.754
- type: recall_at_1
value: 22.831000000000003
- type: recall_at_10
value: 69.346
- type: recall_at_100
value: 95.235
- type: recall_at_1000
value: 99.36
- type: recall_at_3
value: 44.523
- type: recall_at_5
value: 53.769999999999996
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 40.27789869854063
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.41979463347428
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.22752045109304
- type: mrr
value: 71.51112430198303
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.71147646622866
- type: cos_sim_spearman
value: 85.059167046486
- type: euclidean_pearson
value: 75.88421613600647
- type: euclidean_spearman
value: 75.12821787150585
- type: manhattan_pearson
value: 75.22005646957604
- type: manhattan_spearman
value: 74.42880434453272
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.23799582463465
- type: f1
value: 99.12665274878218
- type: precision
value: 99.07098121085595
- type: recall
value: 99.23799582463465
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.88685890380806
- type: f1
value: 97.59336708489249
- type: precision
value: 97.44662117543473
- type: recall
value: 97.88685890380806
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.47142362313821
- type: f1
value: 97.1989377670015
- type: precision
value: 97.06384944001847
- type: recall
value: 97.47142362313821
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.4728804634018
- type: f1
value: 98.2973494821836
- type: precision
value: 98.2095839915745
- type: recall
value: 98.4728804634018
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 82.74025974025975
- type: f1
value: 82.67420447730439
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.0380848063507
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 29.45956405670166
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.122
- type: map_at_10
value: 42.03
- type: map_at_100
value: 43.364000000000004
- type: map_at_1000
value: 43.474000000000004
- type: map_at_3
value: 38.804
- type: map_at_5
value: 40.585
- type: mrr_at_1
value: 39.914
- type: mrr_at_10
value: 48.227
- type: mrr_at_100
value: 49.018
- type: mrr_at_1000
value: 49.064
- type: mrr_at_3
value: 45.994
- type: mrr_at_5
value: 47.396
- type: ndcg_at_1
value: 39.914
- type: ndcg_at_10
value: 47.825
- type: ndcg_at_100
value: 52.852
- type: ndcg_at_1000
value: 54.891
- type: ndcg_at_3
value: 43.517
- type: ndcg_at_5
value: 45.493
- type: precision_at_1
value: 39.914
- type: precision_at_10
value: 8.956
- type: precision_at_100
value: 1.388
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 20.791999999999998
- type: precision_at_5
value: 14.821000000000002
- type: recall_at_1
value: 32.122
- type: recall_at_10
value: 58.294999999999995
- type: recall_at_100
value: 79.726
- type: recall_at_1000
value: 93.099
- type: recall_at_3
value: 45.017
- type: recall_at_5
value: 51.002
- type: map_at_1
value: 29.677999999999997
- type: map_at_10
value: 38.684000000000005
- type: map_at_100
value: 39.812999999999995
- type: map_at_1000
value: 39.945
- type: map_at_3
value: 35.831
- type: map_at_5
value: 37.446
- type: mrr_at_1
value: 37.771
- type: mrr_at_10
value: 44.936
- type: mrr_at_100
value: 45.583
- type: mrr_at_1000
value: 45.634
- type: mrr_at_3
value: 42.771
- type: mrr_at_5
value: 43.994
- type: ndcg_at_1
value: 37.771
- type: ndcg_at_10
value: 44.059
- type: ndcg_at_100
value: 48.192
- type: ndcg_at_1000
value: 50.375
- type: ndcg_at_3
value: 40.172000000000004
- type: ndcg_at_5
value: 41.899
- type: precision_at_1
value: 37.771
- type: precision_at_10
value: 8.286999999999999
- type: precision_at_100
value: 1.322
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.406000000000002
- type: precision_at_5
value: 13.745
- type: recall_at_1
value: 29.677999999999997
- type: recall_at_10
value: 53.071
- type: recall_at_100
value: 70.812
- type: recall_at_1000
value: 84.841
- type: recall_at_3
value: 41.016000000000005
- type: recall_at_5
value: 46.22
- type: map_at_1
value: 42.675000000000004
- type: map_at_10
value: 53.93599999999999
- type: map_at_100
value: 54.806999999999995
- type: map_at_1000
value: 54.867
- type: map_at_3
value: 50.934000000000005
- type: map_at_5
value: 52.583
- type: mrr_at_1
value: 48.339
- type: mrr_at_10
value: 57.265
- type: mrr_at_100
value: 57.873
- type: mrr_at_1000
value: 57.906
- type: mrr_at_3
value: 55.193000000000005
- type: mrr_at_5
value: 56.303000000000004
- type: ndcg_at_1
value: 48.339
- type: ndcg_at_10
value: 59.19799999999999
- type: ndcg_at_100
value: 62.743
- type: ndcg_at_1000
value: 63.99399999999999
- type: ndcg_at_3
value: 54.367
- type: ndcg_at_5
value: 56.548
- type: precision_at_1
value: 48.339
- type: precision_at_10
value: 9.216000000000001
- type: precision_at_100
value: 1.1809999999999998
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 23.72
- type: precision_at_5
value: 16.025
- type: recall_at_1
value: 42.675000000000004
- type: recall_at_10
value: 71.437
- type: recall_at_100
value: 86.803
- type: recall_at_1000
value: 95.581
- type: recall_at_3
value: 58.434
- type: recall_at_5
value: 63.754
- type: map_at_1
value: 23.518
- type: map_at_10
value: 30.648999999999997
- type: map_at_100
value: 31.508999999999997
- type: map_at_1000
value: 31.604
- type: map_at_3
value: 28.247
- type: map_at_5
value: 29.65
- type: mrr_at_1
value: 25.650000000000002
- type: mrr_at_10
value: 32.771
- type: mrr_at_100
value: 33.554
- type: mrr_at_1000
value: 33.629999999999995
- type: mrr_at_3
value: 30.433
- type: mrr_at_5
value: 31.812
- type: ndcg_at_1
value: 25.650000000000002
- type: ndcg_at_10
value: 34.929
- type: ndcg_at_100
value: 39.382
- type: ndcg_at_1000
value: 41.913
- type: ndcg_at_3
value: 30.292
- type: ndcg_at_5
value: 32.629999999999995
- type: precision_at_1
value: 25.650000000000002
- type: precision_at_10
value: 5.311
- type: precision_at_100
value: 0.792
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 12.58
- type: precision_at_5
value: 8.994
- type: recall_at_1
value: 23.518
- type: recall_at_10
value: 46.19
- type: recall_at_100
value: 67.123
- type: recall_at_1000
value: 86.442
- type: recall_at_3
value: 33.678000000000004
- type: recall_at_5
value: 39.244
- type: map_at_1
value: 15.891
- type: map_at_10
value: 22.464000000000002
- type: map_at_100
value: 23.483
- type: map_at_1000
value: 23.613
- type: map_at_3
value: 20.080000000000002
- type: map_at_5
value: 21.526
- type: mrr_at_1
value: 20.025000000000002
- type: mrr_at_10
value: 26.712999999999997
- type: mrr_at_100
value: 27.650000000000002
- type: mrr_at_1000
value: 27.737000000000002
- type: mrr_at_3
value: 24.274
- type: mrr_at_5
value: 25.711000000000002
- type: ndcg_at_1
value: 20.025000000000002
- type: ndcg_at_10
value: 27.028999999999996
- type: ndcg_at_100
value: 32.064
- type: ndcg_at_1000
value: 35.188
- type: ndcg_at_3
value: 22.512999999999998
- type: ndcg_at_5
value: 24.89
- type: precision_at_1
value: 20.025000000000002
- type: precision_at_10
value: 4.776
- type: precision_at_100
value: 0.8500000000000001
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 10.531
- type: precision_at_5
value: 7.811
- type: recall_at_1
value: 15.891
- type: recall_at_10
value: 37.261
- type: recall_at_100
value: 59.12
- type: recall_at_1000
value: 81.356
- type: recall_at_3
value: 24.741
- type: recall_at_5
value: 30.753999999999998
- type: map_at_1
value: 27.544
- type: map_at_10
value: 36.283
- type: map_at_100
value: 37.467
- type: map_at_1000
value: 37.574000000000005
- type: map_at_3
value: 33.528999999999996
- type: map_at_5
value: 35.028999999999996
- type: mrr_at_1
value: 34.166999999999994
- type: mrr_at_10
value: 41.866
- type: mrr_at_100
value: 42.666
- type: mrr_at_1000
value: 42.716
- type: mrr_at_3
value: 39.541
- type: mrr_at_5
value: 40.768
- type: ndcg_at_1
value: 34.166999999999994
- type: ndcg_at_10
value: 41.577
- type: ndcg_at_100
value: 46.687
- type: ndcg_at_1000
value: 48.967
- type: ndcg_at_3
value: 37.177
- type: ndcg_at_5
value: 39.097
- type: precision_at_1
value: 34.166999999999994
- type: precision_at_10
value: 7.420999999999999
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 17.291999999999998
- type: precision_at_5
value: 12.166
- type: recall_at_1
value: 27.544
- type: recall_at_10
value: 51.99399999999999
- type: recall_at_100
value: 73.738
- type: recall_at_1000
value: 89.33
- type: recall_at_3
value: 39.179
- type: recall_at_5
value: 44.385999999999996
- type: map_at_1
value: 26.661
- type: map_at_10
value: 35.475
- type: map_at_100
value: 36.626999999999995
- type: map_at_1000
value: 36.741
- type: map_at_3
value: 32.818000000000005
- type: map_at_5
value: 34.397
- type: mrr_at_1
value: 32.647999999999996
- type: mrr_at_10
value: 40.784
- type: mrr_at_100
value: 41.602
- type: mrr_at_1000
value: 41.661
- type: mrr_at_3
value: 38.68
- type: mrr_at_5
value: 39.838
- type: ndcg_at_1
value: 32.647999999999996
- type: ndcg_at_10
value: 40.697
- type: ndcg_at_100
value: 45.799
- type: ndcg_at_1000
value: 48.235
- type: ndcg_at_3
value: 36.516
- type: ndcg_at_5
value: 38.515
- type: precision_at_1
value: 32.647999999999996
- type: precision_at_10
value: 7.202999999999999
- type: precision_at_100
value: 1.1360000000000001
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 17.314
- type: precision_at_5
value: 12.145999999999999
- type: recall_at_1
value: 26.661
- type: recall_at_10
value: 50.995000000000005
- type: recall_at_100
value: 73.065
- type: recall_at_1000
value: 89.781
- type: recall_at_3
value: 39.073
- type: recall_at_5
value: 44.395
- type: map_at_1
value: 25.946583333333333
- type: map_at_10
value: 33.79725
- type: map_at_100
value: 34.86408333333333
- type: map_at_1000
value: 34.9795
- type: map_at_3
value: 31.259999999999998
- type: map_at_5
value: 32.71541666666666
- type: mrr_at_1
value: 30.863749999999996
- type: mrr_at_10
value: 37.99183333333333
- type: mrr_at_100
value: 38.790499999999994
- type: mrr_at_1000
value: 38.85575000000001
- type: mrr_at_3
value: 35.82083333333333
- type: mrr_at_5
value: 37.07533333333333
- type: ndcg_at_1
value: 30.863749999999996
- type: ndcg_at_10
value: 38.52141666666667
- type: ndcg_at_100
value: 43.17966666666667
- type: ndcg_at_1000
value: 45.64608333333333
- type: ndcg_at_3
value: 34.333000000000006
- type: ndcg_at_5
value: 36.34975
- type: precision_at_1
value: 30.863749999999996
- type: precision_at_10
value: 6.598999999999999
- type: precision_at_100
value: 1.0502500000000001
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_3
value: 15.557583333333334
- type: precision_at_5
value: 11.020000000000001
- type: recall_at_1
value: 25.946583333333333
- type: recall_at_10
value: 48.36991666666666
- type: recall_at_100
value: 69.02408333333334
- type: recall_at_1000
value: 86.43858333333331
- type: recall_at_3
value: 36.4965
- type: recall_at_5
value: 41.76258333333334
- type: map_at_1
value: 22.431
- type: map_at_10
value: 28.889
- type: map_at_100
value: 29.642000000000003
- type: map_at_1000
value: 29.742
- type: map_at_3
value: 26.998
- type: map_at_5
value: 28.172000000000004
- type: mrr_at_1
value: 25.307000000000002
- type: mrr_at_10
value: 31.763
- type: mrr_at_100
value: 32.443
- type: mrr_at_1000
value: 32.531
- type: mrr_at_3
value: 29.959000000000003
- type: mrr_at_5
value: 31.063000000000002
- type: ndcg_at_1
value: 25.307000000000002
- type: ndcg_at_10
value: 32.586999999999996
- type: ndcg_at_100
value: 36.5
- type: ndcg_at_1000
value: 39.133
- type: ndcg_at_3
value: 29.25
- type: ndcg_at_5
value: 31.023
- type: precision_at_1
value: 25.307000000000002
- type: precision_at_10
value: 4.954
- type: precision_at_100
value: 0.747
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 12.577
- type: precision_at_5
value: 8.741999999999999
- type: recall_at_1
value: 22.431
- type: recall_at_10
value: 41.134
- type: recall_at_100
value: 59.28600000000001
- type: recall_at_1000
value: 78.857
- type: recall_at_3
value: 31.926
- type: recall_at_5
value: 36.335
- type: map_at_1
value: 17.586
- type: map_at_10
value: 23.304
- type: map_at_100
value: 24.159
- type: map_at_1000
value: 24.281
- type: map_at_3
value: 21.316
- type: map_at_5
value: 22.383
- type: mrr_at_1
value: 21.645
- type: mrr_at_10
value: 27.365000000000002
- type: mrr_at_100
value: 28.108
- type: mrr_at_1000
value: 28.192
- type: mrr_at_3
value: 25.482
- type: mrr_at_5
value: 26.479999999999997
- type: ndcg_at_1
value: 21.645
- type: ndcg_at_10
value: 27.306
- type: ndcg_at_100
value: 31.496000000000002
- type: ndcg_at_1000
value: 34.53
- type: ndcg_at_3
value: 23.73
- type: ndcg_at_5
value: 25.294
- type: precision_at_1
value: 21.645
- type: precision_at_10
value: 4.797
- type: precision_at_100
value: 0.8059999999999999
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 10.850999999999999
- type: precision_at_5
value: 7.736
- type: recall_at_1
value: 17.586
- type: recall_at_10
value: 35.481
- type: recall_at_100
value: 54.534000000000006
- type: recall_at_1000
value: 76.456
- type: recall_at_3
value: 25.335
- type: recall_at_5
value: 29.473
- type: map_at_1
value: 25.095
- type: map_at_10
value: 32.374
- type: map_at_100
value: 33.537
- type: map_at_1000
value: 33.634
- type: map_at_3
value: 30.089
- type: map_at_5
value: 31.433
- type: mrr_at_1
value: 29.198
- type: mrr_at_10
value: 36.01
- type: mrr_at_100
value: 37.022
- type: mrr_at_1000
value: 37.083
- type: mrr_at_3
value: 33.94
- type: mrr_at_5
value: 35.148
- type: ndcg_at_1
value: 29.198
- type: ndcg_at_10
value: 36.729
- type: ndcg_at_100
value: 42.114000000000004
- type: ndcg_at_1000
value: 44.592
- type: ndcg_at_3
value: 32.644
- type: ndcg_at_5
value: 34.652
- type: precision_at_1
value: 29.198
- type: precision_at_10
value: 5.970000000000001
- type: precision_at_100
value: 0.967
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 14.396999999999998
- type: precision_at_5
value: 10.093
- type: recall_at_1
value: 25.095
- type: recall_at_10
value: 46.392
- type: recall_at_100
value: 69.706
- type: recall_at_1000
value: 87.738
- type: recall_at_3
value: 35.303000000000004
- type: recall_at_5
value: 40.441
- type: map_at_1
value: 26.857999999999997
- type: map_at_10
value: 34.066
- type: map_at_100
value: 35.671
- type: map_at_1000
value: 35.881
- type: map_at_3
value: 31.304
- type: map_at_5
value: 32.885
- type: mrr_at_1
value: 32.411
- type: mrr_at_10
value: 38.987
- type: mrr_at_100
value: 39.894
- type: mrr_at_1000
value: 39.959
- type: mrr_at_3
value: 36.626999999999995
- type: mrr_at_5
value: 38.011
- type: ndcg_at_1
value: 32.411
- type: ndcg_at_10
value: 39.208
- type: ndcg_at_100
value: 44.626
- type: ndcg_at_1000
value: 47.43
- type: ndcg_at_3
value: 35.091
- type: ndcg_at_5
value: 37.119
- type: precision_at_1
value: 32.411
- type: precision_at_10
value: 7.51
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 16.14
- type: precision_at_5
value: 11.976
- type: recall_at_1
value: 26.857999999999997
- type: recall_at_10
value: 47.407
- type: recall_at_100
value: 72.236
- type: recall_at_1000
value: 90.77
- type: recall_at_3
value: 35.125
- type: recall_at_5
value: 40.522999999999996
- type: map_at_1
value: 21.3
- type: map_at_10
value: 27.412999999999997
- type: map_at_100
value: 28.29
- type: map_at_1000
value: 28.398
- type: map_at_3
value: 25.169999999999998
- type: map_at_5
value: 26.496
- type: mrr_at_1
value: 23.29
- type: mrr_at_10
value: 29.215000000000003
- type: mrr_at_100
value: 30.073
- type: mrr_at_1000
value: 30.156
- type: mrr_at_3
value: 26.956000000000003
- type: mrr_at_5
value: 28.38
- type: ndcg_at_1
value: 23.29
- type: ndcg_at_10
value: 31.113000000000003
- type: ndcg_at_100
value: 35.701
- type: ndcg_at_1000
value: 38.505
- type: ndcg_at_3
value: 26.727
- type: ndcg_at_5
value: 29.037000000000003
- type: precision_at_1
value: 23.29
- type: precision_at_10
value: 4.787
- type: precision_at_100
value: 0.763
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 11.091
- type: precision_at_5
value: 7.985
- type: recall_at_1
value: 21.3
- type: recall_at_10
value: 40.782000000000004
- type: recall_at_100
value: 62.13999999999999
- type: recall_at_1000
value: 83.012
- type: recall_at_3
value: 29.131
- type: recall_at_5
value: 34.624
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.631
- type: map_at_10
value: 16.634999999999998
- type: map_at_100
value: 18.23
- type: map_at_1000
value: 18.419
- type: map_at_3
value: 13.66
- type: map_at_5
value: 15.173
- type: mrr_at_1
value: 21.368000000000002
- type: mrr_at_10
value: 31.56
- type: mrr_at_100
value: 32.58
- type: mrr_at_1000
value: 32.633
- type: mrr_at_3
value: 28.241
- type: mrr_at_5
value: 30.225
- type: ndcg_at_1
value: 21.368000000000002
- type: ndcg_at_10
value: 23.855999999999998
- type: ndcg_at_100
value: 30.686999999999998
- type: ndcg_at_1000
value: 34.327000000000005
- type: ndcg_at_3
value: 18.781
- type: ndcg_at_5
value: 20.73
- type: precision_at_1
value: 21.368000000000002
- type: precision_at_10
value: 7.564
- type: precision_at_100
value: 1.496
- type: precision_at_1000
value: 0.217
- type: precision_at_3
value: 13.876
- type: precision_at_5
value: 11.062
- type: recall_at_1
value: 9.631
- type: recall_at_10
value: 29.517
- type: recall_at_100
value: 53.452
- type: recall_at_1000
value: 74.115
- type: recall_at_3
value: 17.605999999999998
- type: recall_at_5
value: 22.505
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.885
- type: map_at_10
value: 18.798000000000002
- type: map_at_100
value: 26.316
- type: map_at_1000
value: 27.869
- type: map_at_3
value: 13.719000000000001
- type: map_at_5
value: 15.716
- type: mrr_at_1
value: 66
- type: mrr_at_10
value: 74.263
- type: mrr_at_100
value: 74.519
- type: mrr_at_1000
value: 74.531
- type: mrr_at_3
value: 72.458
- type: mrr_at_5
value: 73.321
- type: ndcg_at_1
value: 53.87499999999999
- type: ndcg_at_10
value: 40.355999999999995
- type: ndcg_at_100
value: 44.366
- type: ndcg_at_1000
value: 51.771
- type: ndcg_at_3
value: 45.195
- type: ndcg_at_5
value: 42.187000000000005
- type: precision_at_1
value: 66
- type: precision_at_10
value: 31.75
- type: precision_at_100
value: 10.11
- type: precision_at_1000
value: 1.9800000000000002
- type: precision_at_3
value: 48.167
- type: precision_at_5
value: 40.050000000000004
- type: recall_at_1
value: 8.885
- type: recall_at_10
value: 24.471999999999998
- type: recall_at_100
value: 49.669000000000004
- type: recall_at_1000
value: 73.383
- type: recall_at_3
value: 14.872
- type: recall_at_5
value: 18.262999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 45.18
- type: f1
value: 40.26878691789978
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 62.751999999999995
- type: map_at_10
value: 74.131
- type: map_at_100
value: 74.407
- type: map_at_1000
value: 74.423
- type: map_at_3
value: 72.329
- type: map_at_5
value: 73.555
- type: mrr_at_1
value: 67.282
- type: mrr_at_10
value: 78.292
- type: mrr_at_100
value: 78.455
- type: mrr_at_1000
value: 78.458
- type: mrr_at_3
value: 76.755
- type: mrr_at_5
value: 77.839
- type: ndcg_at_1
value: 67.282
- type: ndcg_at_10
value: 79.443
- type: ndcg_at_100
value: 80.529
- type: ndcg_at_1000
value: 80.812
- type: ndcg_at_3
value: 76.281
- type: ndcg_at_5
value: 78.235
- type: precision_at_1
value: 67.282
- type: precision_at_10
value: 10.078
- type: precision_at_100
value: 1.082
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 30.178
- type: precision_at_5
value: 19.232
- type: recall_at_1
value: 62.751999999999995
- type: recall_at_10
value: 91.521
- type: recall_at_100
value: 95.997
- type: recall_at_1000
value: 97.775
- type: recall_at_3
value: 83.131
- type: recall_at_5
value: 87.93299999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.861
- type: map_at_10
value: 30.252000000000002
- type: map_at_100
value: 32.082
- type: map_at_1000
value: 32.261
- type: map_at_3
value: 25.909
- type: map_at_5
value: 28.296
- type: mrr_at_1
value: 37.346000000000004
- type: mrr_at_10
value: 45.802
- type: mrr_at_100
value: 46.611999999999995
- type: mrr_at_1000
value: 46.659
- type: mrr_at_3
value: 43.056
- type: mrr_at_5
value: 44.637
- type: ndcg_at_1
value: 37.346000000000004
- type: ndcg_at_10
value: 38.169
- type: ndcg_at_100
value: 44.864
- type: ndcg_at_1000
value: 47.974
- type: ndcg_at_3
value: 33.619
- type: ndcg_at_5
value: 35.317
- type: precision_at_1
value: 37.346000000000004
- type: precision_at_10
value: 10.693999999999999
- type: precision_at_100
value: 1.775
- type: precision_at_1000
value: 0.231
- type: precision_at_3
value: 22.325
- type: precision_at_5
value: 16.852
- type: recall_at_1
value: 18.861
- type: recall_at_10
value: 45.672000000000004
- type: recall_at_100
value: 70.60499999999999
- type: recall_at_1000
value: 89.216
- type: recall_at_3
value: 30.361
- type: recall_at_5
value: 36.998999999999995
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.852999999999994
- type: map_at_10
value: 59.961
- type: map_at_100
value: 60.78
- type: map_at_1000
value: 60.843
- type: map_at_3
value: 56.39999999999999
- type: map_at_5
value: 58.646
- type: mrr_at_1
value: 75.70599999999999
- type: mrr_at_10
value: 82.321
- type: mrr_at_100
value: 82.516
- type: mrr_at_1000
value: 82.525
- type: mrr_at_3
value: 81.317
- type: mrr_at_5
value: 81.922
- type: ndcg_at_1
value: 75.70599999999999
- type: ndcg_at_10
value: 68.557
- type: ndcg_at_100
value: 71.485
- type: ndcg_at_1000
value: 72.71600000000001
- type: ndcg_at_3
value: 63.524
- type: ndcg_at_5
value: 66.338
- type: precision_at_1
value: 75.70599999999999
- type: precision_at_10
value: 14.463000000000001
- type: precision_at_100
value: 1.677
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 40.806
- type: precision_at_5
value: 26.709
- type: recall_at_1
value: 37.852999999999994
- type: recall_at_10
value: 72.316
- type: recall_at_100
value: 83.842
- type: recall_at_1000
value: 91.999
- type: recall_at_3
value: 61.209
- type: recall_at_5
value: 66.77199999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 85.46039999999999
- type: ap
value: 79.9812521351881
- type: f1
value: 85.31722909702084
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.704
- type: map_at_10
value: 35.329
- type: map_at_100
value: 36.494
- type: map_at_1000
value: 36.541000000000004
- type: map_at_3
value: 31.476
- type: map_at_5
value: 33.731
- type: mrr_at_1
value: 23.294999999999998
- type: mrr_at_10
value: 35.859
- type: mrr_at_100
value: 36.968
- type: mrr_at_1000
value: 37.008
- type: mrr_at_3
value: 32.085
- type: mrr_at_5
value: 34.299
- type: ndcg_at_1
value: 23.324
- type: ndcg_at_10
value: 42.274
- type: ndcg_at_100
value: 47.839999999999996
- type: ndcg_at_1000
value: 48.971
- type: ndcg_at_3
value: 34.454
- type: ndcg_at_5
value: 38.464
- type: precision_at_1
value: 23.324
- type: precision_at_10
value: 6.648
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.674999999999999
- type: precision_at_5
value: 10.850999999999999
- type: recall_at_1
value: 22.704
- type: recall_at_10
value: 63.660000000000004
- type: recall_at_100
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.239408204438476
- type: f1
value: 61.92570286170469
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (az)
type: mteb/amazon_massive_scenario
config: az
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.74915938130463
- type: f1
value: 62.130740689396276
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (bn)
type: mteb/amazon_massive_scenario
config: bn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 65.00336247478144
- type: f1
value: 63.71080635228055
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (cy)
type: mteb/amazon_massive_scenario
config: cy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 52.837928715534645
- type: f1
value: 50.390741680320836
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (da)
type: mteb/amazon_massive_scenario
config: da
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.42098184263618
- type: f1
value: 71.41355113538995
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (de)
type: mteb/amazon_massive_scenario
config: de
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.95359784801613
- type: f1
value: 71.42699340156742
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (el)
type: mteb/amazon_massive_scenario
config: el
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.18157363819772
- type: f1
value: 69.74836113037671
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.08137188971082
- type: f1
value: 76.78000685068261
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (es)
type: mteb/amazon_massive_scenario
config: es
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.5030262273033
- type: f1
value: 71.71620130425673
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fa)
type: mteb/amazon_massive_scenario
config: fa
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.24546065904505
- type: f1
value: 69.07638311730359
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fi)
type: mteb/amazon_massive_scenario
config: fi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.12911903160726
- type: f1
value: 68.32651736539815
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.89307330195025
- type: f1
value: 71.33986549860187
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (he)
type: mteb/amazon_massive_scenario
config: he
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.44451916610626
- type: f1
value: 66.90192664503866
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hi)
type: mteb/amazon_massive_scenario
config: hi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.16274377942166
- type: f1
value: 68.01090953775066
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hu)
type: mteb/amazon_massive_scenario
config: hu
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.75319435104237
- type: f1
value: 70.18035309201403
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hy)
type: mteb/amazon_massive_scenario
config: hy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.14391392064559
- type: f1
value: 61.48286540778145
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (id)
type: mteb/amazon_massive_scenario
config: id
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.70275722932078
- type: f1
value: 70.26164779846495
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (is)
type: mteb/amazon_massive_scenario
config: is
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.93813046402153
- type: f1
value: 58.8852862116525
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (it)
type: mteb/amazon_massive_scenario
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.320107599193
- type: f1
value: 72.19836409602924
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ja)
type: mteb/amazon_massive_scenario
config: ja
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.65366509751176
- type: f1
value: 74.55188288799579
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (jv)
type: mteb/amazon_massive_scenario
config: jv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.694014794889036
- type: f1
value: 58.11353311721067
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ka)
type: mteb/amazon_massive_scenario
config: ka
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 54.37457969065231
- type: f1
value: 52.81306134311697
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (km)
type: mteb/amazon_massive_scenario
config: km
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 48.3086751849361
- type: f1
value: 45.396449765419376
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (kn)
type: mteb/amazon_massive_scenario
config: kn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.151983860121064
- type: f1
value: 60.31762544281696
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ko)
type: mteb/amazon_massive_scenario
config: ko
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.44788164088769
- type: f1
value: 71.68150151736367
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (lv)
type: mteb/amazon_massive_scenario
config: lv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.81439139206455
- type: f1
value: 62.06735559105593
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ml)
type: mteb/amazon_massive_scenario
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.04303967720242
- type: f1
value: 66.68298851670133
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (mn)
type: mteb/amazon_massive_scenario
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 61.43913920645595
- type: f1
value: 60.25605977560783
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ms)
type: mteb/amazon_massive_scenario
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.90316072629456
- type: f1
value: 65.1325924692381
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (my)
type: mteb/amazon_massive_scenario
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 61.63752521856086
- type: f1
value: 59.14284778039585
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nb)
type: mteb/amazon_massive_scenario
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.63080026899797
- type: f1
value: 70.89771864626877
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.10827168796234
- type: f1
value: 71.71954219691159
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.59515803631471
- type: f1
value: 70.05040128099003
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.83389374579691
- type: f1
value: 70.84877936562735
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.18628110289173
- type: f1
value: 68.97232927921841
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.99260255548083
- type: f1
value: 72.85139492157732
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 65.26227303295225
- type: f1
value: 65.08833655469431
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.48621385339611
- type: f1
value: 64.43483199071298
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.14391392064559
- type: f1
value: 72.2580822579741
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.88567585743107
- type: f1
value: 58.3073765932569
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.38399462004034
- type: f1
value: 60.82139544252606
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.58574310692671
- type: f1
value: 60.71443370385374
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.61398789509079
- type: f1
value: 70.99761812049401
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.73705447209146
- type: f1
value: 61.680849331794796
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.66778749159381
- type: f1
value: 71.17320646080115
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.640215198386
- type: f1
value: 63.301805157015444
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.00672494956288
- type: f1
value: 70.26005548582106
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.42030934767989
- type: f1
value: 75.2074842882598
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.69266980497646
- type: f1
value: 70.94103167391192
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 28.91697191169135
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.434000079573313
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.96683513343383
- type: mrr
value: 31.967364078714834
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.5280000000000005
- type: map_at_10
value: 11.793
- type: map_at_100
value: 14.496999999999998
- type: map_at_1000
value: 15.783
- type: map_at_3
value: 8.838
- type: map_at_5
value: 10.07
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 51.531000000000006
- type: mrr_at_100
value: 52.205
- type: mrr_at_1000
value: 52.242999999999995
- type: mrr_at_3
value: 49.431999999999995
- type: mrr_at_5
value: 50.470000000000006
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 32.464999999999996
- type: ndcg_at_100
value: 28.927999999999997
- type: ndcg_at_1000
value: 37.629000000000005
- type: ndcg_at_3
value: 37.845
- type: ndcg_at_5
value: 35.147
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 23.932000000000002
- type: precision_at_100
value: 7.17
- type: precision_at_1000
value: 1.967
- type: precision_at_3
value: 35.397
- type: precision_at_5
value: 29.907
- type: recall_at_1
value: 5.5280000000000005
- type: recall_at_10
value: 15.568000000000001
- type: recall_at_100
value: 28.54
- type: recall_at_1000
value: 59.864
- type: recall_at_3
value: 9.822000000000001
- type: recall_at_5
value: 11.726
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.041000000000004
- type: map_at_10
value: 52.664
- type: map_at_100
value: 53.477
- type: map_at_1000
value: 53.505
- type: map_at_3
value: 48.510999999999996
- type: map_at_5
value: 51.036
- type: mrr_at_1
value: 41.338
- type: mrr_at_10
value: 55.071000000000005
- type: mrr_at_100
value: 55.672
- type: mrr_at_1000
value: 55.689
- type: mrr_at_3
value: 51.82
- type: mrr_at_5
value: 53.852
- type: ndcg_at_1
value: 41.338
- type: ndcg_at_10
value: 60.01800000000001
- type: ndcg_at_100
value: 63.409000000000006
- type: ndcg_at_1000
value: 64.017
- type: ndcg_at_3
value: 52.44799999999999
- type: ndcg_at_5
value: 56.571000000000005
- type: precision_at_1
value: 41.338
- type: precision_at_10
value: 9.531
- type: precision_at_100
value: 1.145
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.416
- type: precision_at_5
value: 16.46
- type: recall_at_1
value: 37.041000000000004
- type: recall_at_10
value: 79.76299999999999
- type: recall_at_100
value: 94.39
- type: recall_at_1000
value: 98.851
- type: recall_at_3
value: 60.465
- type: recall_at_5
value: 69.906
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.952
- type: map_at_10
value: 83.758
- type: map_at_100
value: 84.406
- type: map_at_1000
value: 84.425
- type: map_at_3
value: 80.839
- type: map_at_5
value: 82.646
- type: mrr_at_1
value: 80.62
- type: mrr_at_10
value: 86.947
- type: mrr_at_100
value: 87.063
- type: mrr_at_1000
value: 87.064
- type: mrr_at_3
value: 85.96000000000001
- type: mrr_at_5
value: 86.619
- type: ndcg_at_1
value: 80.63
- type: ndcg_at_10
value: 87.64800000000001
- type: ndcg_at_100
value: 88.929
- type: ndcg_at_1000
value: 89.054
- type: ndcg_at_3
value: 84.765
- type: ndcg_at_5
value: 86.291
- type: precision_at_1
value: 80.63
- type: precision_at_10
value: 13.314
- type: precision_at_100
value: 1.525
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.1
- type: precision_at_5
value: 24.372
- type: recall_at_1
value: 69.952
- type: recall_at_10
value: 94.955
- type: recall_at_100
value: 99.38
- type: recall_at_1000
value: 99.96000000000001
- type: recall_at_3
value: 86.60600000000001
- type: recall_at_5
value: 90.997
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.41329517878427
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 55.171278362748666
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.213
- type: map_at_10
value: 9.895
- type: map_at_100
value: 11.776
- type: map_at_1000
value: 12.084
- type: map_at_3
value: 7.2669999999999995
- type: map_at_5
value: 8.620999999999999
- type: mrr_at_1
value: 20.8
- type: mrr_at_10
value: 31.112000000000002
- type: mrr_at_100
value: 32.274
- type: mrr_at_1000
value: 32.35
- type: mrr_at_3
value: 28.133000000000003
- type: mrr_at_5
value: 29.892999999999997
- type: ndcg_at_1
value: 20.8
- type: ndcg_at_10
value: 17.163999999999998
- type: ndcg_at_100
value: 24.738
- type: ndcg_at_1000
value: 30.316
- type: ndcg_at_3
value: 16.665
- type: ndcg_at_5
value: 14.478
- type: precision_at_1
value: 20.8
- type: precision_at_10
value: 8.74
- type: precision_at_100
value: 1.963
- type: precision_at_1000
value: 0.33
- type: precision_at_3
value: 15.467
- type: precision_at_5
value: 12.6
- type: recall_at_1
value: 4.213
- type: recall_at_10
value: 17.698
- type: recall_at_100
value: 39.838
- type: recall_at_1000
value: 66.893
- type: recall_at_3
value: 9.418
- type: recall_at_5
value: 12.773000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.90453315738294
- type: cos_sim_spearman
value: 78.51197850080254
- type: euclidean_pearson
value: 80.09647123597748
- type: euclidean_spearman
value: 78.63548011514061
- type: manhattan_pearson
value: 80.10645285675231
- type: manhattan_spearman
value: 78.57861806068901
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.2616156846401
- type: cos_sim_spearman
value: 76.69713867850156
- type: euclidean_pearson
value: 77.97948563800394
- type: euclidean_spearman
value: 74.2371211567807
- type: manhattan_pearson
value: 77.69697879669705
- type: manhattan_spearman
value: 73.86529778022278
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.0293269315045
- type: cos_sim_spearman
value: 78.02555120584198
- type: euclidean_pearson
value: 78.25398100379078
- type: euclidean_spearman
value: 78.66963870599464
- type: manhattan_pearson
value: 78.14314682167348
- type: manhattan_spearman
value: 78.57692322969135
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.16989925136942
- type: cos_sim_spearman
value: 76.5996225327091
- type: euclidean_pearson
value: 77.8319003279786
- type: euclidean_spearman
value: 76.42824009468998
- type: manhattan_pearson
value: 77.69118862737736
- type: manhattan_spearman
value: 76.25568104762812
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.42012286935325
- type: cos_sim_spearman
value: 88.15654297884122
- type: euclidean_pearson
value: 87.34082819427852
- type: euclidean_spearman
value: 88.06333589547084
- type: manhattan_pearson
value: 87.25115596784842
- type: manhattan_spearman
value: 87.9559927695203
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.88222044996712
- type: cos_sim_spearman
value: 84.28476589061077
- type: euclidean_pearson
value: 83.17399758058309
- type: euclidean_spearman
value: 83.85497357244542
- type: manhattan_pearson
value: 83.0308397703786
- type: manhattan_spearman
value: 83.71554539935046
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.20682986257339
- type: cos_sim_spearman
value: 79.94567120362092
- type: euclidean_pearson
value: 79.43122480368902
- type: euclidean_spearman
value: 79.94802077264987
- type: manhattan_pearson
value: 79.32653021527081
- type: manhattan_spearman
value: 79.80961146709178
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 74.46578144394383
- type: cos_sim_spearman
value: 74.52496637472179
- type: euclidean_pearson
value: 72.2903807076809
- type: euclidean_spearman
value: 73.55549359771645
- type: manhattan_pearson
value: 72.09324837709393
- type: manhattan_spearman
value: 73.36743103606581
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 71.37272335116
- type: cos_sim_spearman
value: 71.26702117766037
- type: euclidean_pearson
value: 67.114829954434
- type: euclidean_spearman
value: 66.37938893947761
- type: manhattan_pearson
value: 66.79688574095246
- type: manhattan_spearman
value: 66.17292828079667
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.61016770129092
- type: cos_sim_spearman
value: 82.08515426632214
- type: euclidean_pearson
value: 80.557340361131
- type: euclidean_spearman
value: 80.37585812266175
- type: manhattan_pearson
value: 80.6782873404285
- type: manhattan_spearman
value: 80.6678073032024
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.00150745350108
- type: cos_sim_spearman
value: 87.83441972211425
- type: euclidean_pearson
value: 87.94826702308792
- type: euclidean_spearman
value: 87.46143974860725
- type: manhattan_pearson
value: 87.97560344306105
- type: manhattan_spearman
value: 87.5267102829796
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 64.76325252267235
- type: cos_sim_spearman
value: 63.32615095463905
- type: euclidean_pearson
value: 64.07920669155716
- type: euclidean_spearman
value: 61.21409893072176
- type: manhattan_pearson
value: 64.26308625680016
- type: manhattan_spearman
value: 61.2438185254079
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.82644463022595
- type: cos_sim_spearman
value: 76.50381269945073
- type: euclidean_pearson
value: 75.1328548315934
- type: euclidean_spearman
value: 75.63761139408453
- type: manhattan_pearson
value: 75.18610101241407
- type: manhattan_spearman
value: 75.30669266354164
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.49994164686832
- type: cos_sim_spearman
value: 86.73743986245549
- type: euclidean_pearson
value: 86.8272894387145
- type: euclidean_spearman
value: 85.97608491000507
- type: manhattan_pearson
value: 86.74960140396779
- type: manhattan_spearman
value: 85.79285984190273
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.58172210788469
- type: cos_sim_spearman
value: 80.17516468334607
- type: euclidean_pearson
value: 77.56537843470504
- type: euclidean_spearman
value: 77.57264627395521
- type: manhattan_pearson
value: 78.09703521695943
- type: manhattan_spearman
value: 78.15942760916954
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.7589932931751
- type: cos_sim_spearman
value: 80.15210089028162
- type: euclidean_pearson
value: 77.54135223516057
- type: euclidean_spearman
value: 77.52697996368764
- type: manhattan_pearson
value: 77.65734439572518
- type: manhattan_spearman
value: 77.77702992016121
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.16682365511267
- type: cos_sim_spearman
value: 79.25311267628506
- type: euclidean_pearson
value: 77.54882036762244
- type: euclidean_spearman
value: 77.33212935194827
- type: manhattan_pearson
value: 77.98405516064015
- type: manhattan_spearman
value: 77.85075717865719
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.10473294775917
- type: cos_sim_spearman
value: 61.82780474476838
- type: euclidean_pearson
value: 45.885111672377256
- type: euclidean_spearman
value: 56.88306351932454
- type: manhattan_pearson
value: 46.101218127323186
- type: manhattan_spearman
value: 56.80953694186333
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 45.781923079584146
- type: cos_sim_spearman
value: 55.95098449691107
- type: euclidean_pearson
value: 25.4571031323205
- type: euclidean_spearman
value: 49.859978118078935
- type: manhattan_pearson
value: 25.624938455041384
- type: manhattan_spearman
value: 49.99546185049401
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.00618133997907
- type: cos_sim_spearman
value: 66.57896677718321
- type: euclidean_pearson
value: 42.60118466388821
- type: euclidean_spearman
value: 62.8210759715209
- type: manhattan_pearson
value: 42.63446860604094
- type: manhattan_spearman
value: 62.73803068925271
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 28.460759121626943
- type: cos_sim_spearman
value: 34.13459007469131
- type: euclidean_pearson
value: 6.0917739325525195
- type: euclidean_spearman
value: 27.9947262664867
- type: manhattan_pearson
value: 6.16877864169911
- type: manhattan_spearman
value: 28.00664163971514
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.42546621771696
- type: cos_sim_spearman
value: 63.699663168970474
- type: euclidean_pearson
value: 38.12085278789738
- type: euclidean_spearman
value: 58.12329140741536
- type: manhattan_pearson
value: 37.97364549443335
- type: manhattan_spearman
value: 57.81545502318733
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 46.82241380954213
- type: cos_sim_spearman
value: 57.86569456006391
- type: euclidean_pearson
value: 31.80480070178813
- type: euclidean_spearman
value: 52.484000620130104
- type: manhattan_pearson
value: 31.952708554646097
- type: manhattan_spearman
value: 52.8560972356195
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 52.00447170498087
- type: cos_sim_spearman
value: 60.664116225735164
- type: euclidean_pearson
value: 33.87382555421702
- type: euclidean_spearman
value: 55.74649067458667
- type: manhattan_pearson
value: 33.99117246759437
- type: manhattan_spearman
value: 55.98749034923899
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.06497233105448
- type: cos_sim_spearman
value: 65.62968801135676
- type: euclidean_pearson
value: 47.482076613243905
- type: euclidean_spearman
value: 62.65137791498299
- type: manhattan_pearson
value: 47.57052626104093
- type: manhattan_spearman
value: 62.436916516613294
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.49397298562575
- type: cos_sim_spearman
value: 74.79604041187868
- type: euclidean_pearson
value: 49.661891561317795
- type: euclidean_spearman
value: 70.31535537621006
- type: manhattan_pearson
value: 49.553715741850006
- type: manhattan_spearman
value: 70.24779344636806
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.640574515348696
- type: cos_sim_spearman
value: 54.927959317689
- type: euclidean_pearson
value: 29.00139666967476
- type: euclidean_spearman
value: 41.86386566971605
- type: manhattan_pearson
value: 29.47411067730344
- type: manhattan_spearman
value: 42.337438424952786
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 68.14095292259312
- type: cos_sim_spearman
value: 73.99017581234789
- type: euclidean_pearson
value: 46.46304297872084
- type: euclidean_spearman
value: 60.91834114800041
- type: manhattan_pearson
value: 47.07072666338692
- type: manhattan_spearman
value: 61.70415727977926
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.27184653359575
- type: cos_sim_spearman
value: 77.76070252418626
- type: euclidean_pearson
value: 62.30586577544778
- type: euclidean_spearman
value: 75.14246629110978
- type: manhattan_pearson
value: 62.328196884927046
- type: manhattan_spearman
value: 75.1282792981433
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.59448528829957
- type: cos_sim_spearman
value: 70.37277734222123
- type: euclidean_pearson
value: 57.63145565721123
- type: euclidean_spearman
value: 66.10113048304427
- type: manhattan_pearson
value: 57.18897811586808
- type: manhattan_spearman
value: 66.5595511215901
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.37520607720838
- type: cos_sim_spearman
value: 69.92282148997948
- type: euclidean_pearson
value: 40.55768770125291
- type: euclidean_spearman
value: 55.189128944669605
- type: manhattan_pearson
value: 41.03566433468883
- type: manhattan_spearman
value: 55.61251893174558
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.791929533771835
- type: cos_sim_spearman
value: 66.45819707662093
- type: euclidean_pearson
value: 39.03686018511092
- type: euclidean_spearman
value: 56.01282695640428
- type: manhattan_pearson
value: 38.91586623619632
- type: manhattan_spearman
value: 56.69394943612747
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 47.82224468473866
- type: cos_sim_spearman
value: 59.467307194781164
- type: euclidean_pearson
value: 27.428459190256145
- type: euclidean_spearman
value: 60.83463107397519
- type: manhattan_pearson
value: 27.487391578496638
- type: manhattan_spearman
value: 61.281380460246496
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 16.306666792752644
- type: cos_sim_spearman
value: 39.35486427252405
- type: euclidean_pearson
value: -2.7887154897955435
- type: euclidean_spearman
value: 27.1296051831719
- type: manhattan_pearson
value: -3.202291270581297
- type: manhattan_spearman
value: 26.32895849218158
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.67006803805076
- type: cos_sim_spearman
value: 73.24670207647144
- type: euclidean_pearson
value: 46.91884681500483
- type: euclidean_spearman
value: 16.903085094570333
- type: manhattan_pearson
value: 46.88391675325812
- type: manhattan_spearman
value: 28.17180849095055
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.79555591223837
- type: cos_sim_spearman
value: 85.63658602085185
- type: euclidean_pearson
value: 85.22080894037671
- type: euclidean_spearman
value: 85.54113580167038
- type: manhattan_pearson
value: 85.1639505960118
- type: manhattan_spearman
value: 85.43502665436196
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 80.73900991689766
- type: mrr
value: 94.81624131133934
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.678000000000004
- type: map_at_10
value: 65.135
- type: map_at_100
value: 65.824
- type: map_at_1000
value: 65.852
- type: map_at_3
value: 62.736000000000004
- type: map_at_5
value: 64.411
- type: mrr_at_1
value: 58.333
- type: mrr_at_10
value: 66.5
- type: mrr_at_100
value: 67.053
- type: mrr_at_1000
value: 67.08
- type: mrr_at_3
value: 64.944
- type: mrr_at_5
value: 65.89399999999999
- type: ndcg_at_1
value: 58.333
- type: ndcg_at_10
value: 69.34700000000001
- type: ndcg_at_100
value: 72.32
- type: ndcg_at_1000
value: 73.014
- type: ndcg_at_3
value: 65.578
- type: ndcg_at_5
value: 67.738
- type: precision_at_1
value: 58.333
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 25.444
- type: precision_at_5
value: 16.933
- type: recall_at_1
value: 55.678000000000004
- type: recall_at_10
value: 80.72200000000001
- type: recall_at_100
value: 93.93299999999999
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 70.783
- type: recall_at_5
value: 75.978
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.74653465346535
- type: cos_sim_ap
value: 93.01476369929063
- type: cos_sim_f1
value: 86.93009118541033
- type: cos_sim_precision
value: 88.09034907597535
- type: cos_sim_recall
value: 85.8
- type: dot_accuracy
value: 99.22970297029703
- type: dot_ap
value: 51.58725659485144
- type: dot_f1
value: 53.51351351351352
- type: dot_precision
value: 58.235294117647065
- type: dot_recall
value: 49.5
- type: euclidean_accuracy
value: 99.74356435643564
- type: euclidean_ap
value: 92.40332894384368
- type: euclidean_f1
value: 86.97838109602817
- type: euclidean_precision
value: 87.46208291203236
- type: euclidean_recall
value: 86.5
- type: manhattan_accuracy
value: 99.73069306930694
- type: manhattan_ap
value: 92.01320815721121
- type: manhattan_f1
value: 86.4135864135864
- type: manhattan_precision
value: 86.32734530938124
- type: manhattan_recall
value: 86.5
- type: max_accuracy
value: 99.74653465346535
- type: max_ap
value: 93.01476369929063
- type: max_f1
value: 86.97838109602817
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 55.2660514302523
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 30.4637783572547
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.41377758357637
- type: mrr
value: 50.138451213818854
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 28.887846011166594
- type: cos_sim_spearman
value: 30.10823258355903
- type: dot_pearson
value: 12.888049550236385
- type: dot_spearman
value: 12.827495903098123
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.21
- type: map_at_10
value: 1.667
- type: map_at_100
value: 9.15
- type: map_at_1000
value: 22.927
- type: map_at_3
value: 0.573
- type: map_at_5
value: 0.915
- type: mrr_at_1
value: 80
- type: mrr_at_10
value: 87.167
- type: mrr_at_100
value: 87.167
- type: mrr_at_1000
value: 87.167
- type: mrr_at_3
value: 85.667
- type: mrr_at_5
value: 87.167
- type: ndcg_at_1
value: 76
- type: ndcg_at_10
value: 69.757
- type: ndcg_at_100
value: 52.402
- type: ndcg_at_1000
value: 47.737
- type: ndcg_at_3
value: 71.866
- type: ndcg_at_5
value: 72.225
- type: precision_at_1
value: 80
- type: precision_at_10
value: 75
- type: precision_at_100
value: 53.959999999999994
- type: precision_at_1000
value: 21.568
- type: precision_at_3
value: 76.667
- type: precision_at_5
value: 78
- type: recall_at_1
value: 0.21
- type: recall_at_10
value: 1.9189999999999998
- type: recall_at_100
value: 12.589
- type: recall_at_1000
value: 45.312000000000005
- type: recall_at_3
value: 0.61
- type: recall_at_5
value: 1.019
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 90.06
- type: precision
value: 89.17333333333333
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 56.06936416184971
- type: f1
value: 50.87508028259473
- type: precision
value: 48.97398843930635
- type: recall
value: 56.06936416184971
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.3170731707317
- type: f1
value: 52.96080139372822
- type: precision
value: 51.67861124382864
- type: recall
value: 57.3170731707317
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.67333333333333
- type: precision
value: 91.90833333333333
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97.07333333333332
- type: precision
value: 96.79500000000002
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.2
- type: precision
value: 92.48333333333333
- type: recall
value: 94.69999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.9
- type: f1
value: 91.26666666666667
- type: precision
value: 90.59444444444445
- type: recall
value: 92.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 34.32835820895522
- type: f1
value: 29.074180380150533
- type: precision
value: 28.068207322920596
- type: recall
value: 34.32835820895522
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.5
- type: f1
value: 74.3945115995116
- type: precision
value: 72.82967843459222
- type: recall
value: 78.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.34146341463415
- type: f1
value: 61.2469400518181
- type: precision
value: 59.63977756660683
- type: recall
value: 66.34146341463415
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.9
- type: f1
value: 76.90349206349207
- type: precision
value: 75.32921568627451
- type: recall
value: 80.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.93317132442284
- type: f1
value: 81.92519105034295
- type: precision
value: 80.71283920615635
- type: recall
value: 84.93317132442284
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.1304347826087
- type: f1
value: 65.22394755003451
- type: precision
value: 62.912422360248435
- type: recall
value: 71.1304347826087
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.82608695652173
- type: f1
value: 75.55693581780538
- type: precision
value: 73.79420289855072
- type: recall
value: 79.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74
- type: f1
value: 70.51022222222223
- type: precision
value: 69.29673599347512
- type: recall
value: 74
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.7
- type: f1
value: 74.14238095238095
- type: precision
value: 72.27214285714285
- type: recall
value: 78.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.97466827503016
- type: f1
value: 43.080330405420874
- type: precision
value: 41.36505499593557
- type: recall
value: 48.97466827503016
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.60000000000001
- type: f1
value: 86.62333333333333
- type: precision
value: 85.225
- type: recall
value: 89.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.2
- type: f1
value: 39.5761253006253
- type: precision
value: 37.991358436312
- type: recall
value: 45.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.70333333333333
- type: precision
value: 85.53166666666667
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.095238095238095
- type: f1
value: 44.60650460650461
- type: precision
value: 42.774116796477045
- type: recall
value: 50.095238095238095
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.4
- type: f1
value: 58.35967261904762
- type: precision
value: 56.54857142857143
- type: recall
value: 63.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.2
- type: f1
value: 87.075
- type: precision
value: 86.12095238095239
- type: recall
value: 89.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8
- type: f1
value: 95.90333333333334
- type: precision
value: 95.50833333333333
- type: recall
value: 96.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.9
- type: f1
value: 88.6288888888889
- type: precision
value: 87.61607142857142
- type: recall
value: 90.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.2
- type: f1
value: 60.54377630539395
- type: precision
value: 58.89434482711381
- type: recall
value: 65.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87
- type: f1
value: 84.32412698412699
- type: precision
value: 83.25527777777778
- type: recall
value: 87
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.7
- type: f1
value: 63.07883541295306
- type: precision
value: 61.06117424242426
- type: recall
value: 68.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.78333333333335
- type: precision
value: 90.86666666666667
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 96.96666666666667
- type: precision
value: 96.61666666666667
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.27493261455525
- type: f1
value: 85.90745732255168
- type: precision
value: 84.91389637616052
- type: recall
value: 88.27493261455525
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.5982905982906
- type: f1
value: 88.4900284900285
- type: precision
value: 87.57122507122507
- type: recall
value: 90.5982905982906
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.5
- type: f1
value: 86.90769841269842
- type: precision
value: 85.80178571428571
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.5
- type: f1
value: 78.36796536796538
- type: precision
value: 76.82196969696969
- type: recall
value: 82.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 71.48846960167715
- type: f1
value: 66.78771089148448
- type: precision
value: 64.98302885095339
- type: recall
value: 71.48846960167715
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.50333333333333
- type: precision
value: 91.77499999999999
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.20622568093385
- type: f1
value: 66.83278891450098
- type: precision
value: 65.35065777283677
- type: recall
value: 71.20622568093385
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.717948717948715
- type: f1
value: 43.53146853146853
- type: precision
value: 42.04721204721204
- type: recall
value: 48.717948717948715
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 58.5
- type: f1
value: 53.8564991863928
- type: precision
value: 52.40329436122275
- type: recall
value: 58.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.8
- type: f1
value: 88.29
- type: precision
value: 87.09166666666667
- type: recall
value: 90.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.28971962616822
- type: f1
value: 62.63425307817832
- type: precision
value: 60.98065939771546
- type: recall
value: 67.28971962616822
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lit-eng)
type: mteb/tatoeba-bitext-mining
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.7
- type: f1
value: 75.5264472455649
- type: precision
value: 74.38205086580086
- type: recall
value: 78.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.7
- type: f1
value: 86.10809523809525
- type: precision
value: 85.07602564102565
- type: recall
value: 88.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lfn-eng)
type: mteb/tatoeba-bitext-mining
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 56.99999999999999
- type: f1
value: 52.85487521402737
- type: precision
value: 51.53985162713104
- type: recall
value: 56.99999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (zsm-eng)
type: mteb/tatoeba-bitext-mining
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94
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value: 92.45333333333333
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value: 91.79166666666667
- type: recall
value: 94
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ita-eng)
type: mteb/tatoeba-bitext-mining
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.30000000000001
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value: 90.61333333333333
- type: precision
value: 89.83333333333331
- type: recall
value: 92.30000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cmn-eng)
type: mteb/tatoeba-bitext-mining
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
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value: 93.34555555555555
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value: 92.75416666666668
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value: 94.69999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lvs-eng)
type: mteb/tatoeba-bitext-mining
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.2
- type: f1
value: 76.6563035113035
- type: precision
value: 75.3014652014652
- type: recall
value: 80.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (glg-eng)
type: mteb/tatoeba-bitext-mining
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.7
- type: f1
value: 82.78689263765207
- type: precision
value: 82.06705086580087
- type: recall
value: 84.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ceb-eng)
type: mteb/tatoeba-bitext-mining
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.33333333333333
- type: f1
value: 45.461523661523664
- type: precision
value: 43.93545574795575
- type: recall
value: 50.33333333333333
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bre-eng)
type: mteb/tatoeba-bitext-mining
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.6000000000000005
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value: 5.442121400446441
- type: precision
value: 5.146630385487529
- type: recall
value: 6.6000000000000005
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ben-eng)
type: mteb/tatoeba-bitext-mining
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85
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value: 81.04666666666667
- type: precision
value: 79.25
- type: recall
value: 85
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swg-eng)
type: mteb/tatoeba-bitext-mining
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.32142857142857
- type: f1
value: 42.333333333333336
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value: 40.69196428571429
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value: 47.32142857142857
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arq-eng)
type: mteb/tatoeba-bitext-mining
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 30.735455543358945
- type: f1
value: 26.73616790022338
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value: 25.397823220451283
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value: 30.735455543358945
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kab-eng)
type: mteb/tatoeba-bitext-mining
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 25.1
- type: f1
value: 21.975989896371022
- type: precision
value: 21.059885632257203
- type: recall
value: 25.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fra-eng)
type: mteb/tatoeba-bitext-mining
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.75666666666666
- type: precision
value: 92.06166666666665
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (por-eng)
type: mteb/tatoeba-bitext-mining
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.74
- type: precision
value: 92.09166666666667
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tat-eng)
type: mteb/tatoeba-bitext-mining
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.3
- type: f1
value: 66.922442002442
- type: precision
value: 65.38249567099568
- type: recall
value: 71.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (oci-eng)
type: mteb/tatoeba-bitext-mining
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 40.300000000000004
- type: f1
value: 35.78682789299971
- type: precision
value: 34.66425128716588
- type: recall
value: 40.300000000000004
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pol-eng)
type: mteb/tatoeba-bitext-mining
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.82333333333334
- type: precision
value: 94.27833333333334
- type: recall
value: 96
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (war-eng)
type: mteb/tatoeba-bitext-mining
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 51.1
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value: 47.179074753133584
- type: precision
value: 46.06461044702424
- type: recall
value: 51.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (aze-eng)
type: mteb/tatoeba-bitext-mining
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.7
- type: f1
value: 84.71
- type: precision
value: 83.46166666666667
- type: recall
value: 87.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (vie-eng)
type: mteb/tatoeba-bitext-mining
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.8
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value: 94.68333333333334
- type: precision
value: 94.13333333333334
- type: recall
value: 95.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nno-eng)
type: mteb/tatoeba-bitext-mining
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.39999999999999
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value: 82.5577380952381
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value: 81.36833333333334
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value: 85.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cha-eng)
type: mteb/tatoeba-bitext-mining
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 21.16788321167883
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value: 16.948865627297987
- type: precision
value: 15.971932568647897
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value: 21.16788321167883
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mhr-eng)
type: mteb/tatoeba-bitext-mining
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.9
- type: f1
value: 5.515526831658907
- type: precision
value: 5.141966366966367
- type: recall
value: 6.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dan-eng)
type: mteb/tatoeba-bitext-mining
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.2
- type: f1
value: 91.39666666666668
- type: precision
value: 90.58666666666667
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ell-eng)
type: mteb/tatoeba-bitext-mining
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.2
- type: f1
value: 89.95666666666666
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value: 88.92833333333333
- type: recall
value: 92.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (amh-eng)
type: mteb/tatoeba-bitext-mining
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 79.76190476190477
- type: f1
value: 74.93386243386244
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value: 73.11011904761904
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value: 79.76190476190477
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pam-eng)
type: mteb/tatoeba-bitext-mining
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.799999999999999
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value: 6.921439712248537
- type: precision
value: 6.489885109680683
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value: 8.799999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hsb-eng)
type: mteb/tatoeba-bitext-mining
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.75569358178054
- type: f1
value: 40.34699501312631
- type: precision
value: 38.57886764719063
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value: 45.75569358178054
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (srp-eng)
type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.08333333333333
- type: precision
value: 88.01666666666668
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (epo-eng)
type: mteb/tatoeba-bitext-mining
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.60000000000001
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value: 92.06690476190477
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value: 91.45095238095239
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value: 93.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kzj-eng)
type: mteb/tatoeba-bitext-mining
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.5
- type: f1
value: 6.200363129378736
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value: 5.89115314822466
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value: 7.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (awa-eng)
type: mteb/tatoeba-bitext-mining
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 73.59307359307358
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value: 68.38933553219267
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value: 66.62698412698413
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value: 73.59307359307358
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fao-eng)
type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.8473282442748
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value: 64.72373682297346
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value: 62.82834214131924
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value: 69.8473282442748
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mal-eng)
type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.5254730713246
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value: 96.72489082969432
- type: precision
value: 96.33672974284326
- type: recall
value: 97.5254730713246
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ile-eng)
type: mteb/tatoeba-bitext-mining
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.6
- type: f1
value: 72.42746031746033
- type: precision
value: 71.14036630036631
- type: recall
value: 75.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.24293785310734
- type: f1
value: 88.86064030131826
- type: precision
value: 87.73540489642184
- type: recall
value: 91.24293785310734
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.2
- type: f1
value: 4.383083659794954
- type: precision
value: 4.027861324289673
- type: recall
value: 6.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.8
- type: f1
value: 84.09428571428572
- type: precision
value: 83.00333333333333
- type: recall
value: 86.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.699999999999996
- type: f1
value: 56.1584972394755
- type: precision
value: 54.713456330903135
- type: recall
value: 60.699999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.2
- type: f1
value: 80.66190476190475
- type: precision
value: 79.19690476190476
- type: recall
value: 84.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.33
- type: precision
value: 90.45
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.3
- type: f1
value: 5.126828976748276
- type: precision
value: 4.853614328966668
- type: recall
value: 6.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.76943699731903
- type: f1
value: 77.82873739308057
- type: precision
value: 76.27622452019234
- type: recall
value: 81.76943699731903
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.30000000000001
- type: f1
value: 90.29666666666665
- type: precision
value: 89.40333333333334
- type: recall
value: 92.30000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 29.249011857707508
- type: f1
value: 24.561866096392947
- type: precision
value: 23.356583740215456
- type: recall
value: 29.249011857707508
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.46478873239437
- type: f1
value: 73.23943661971832
- type: precision
value: 71.66666666666667
- type: recall
value: 77.46478873239437
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 20.35928143712575
- type: f1
value: 15.997867865075824
- type: precision
value: 14.882104658301346
- type: recall
value: 20.35928143712575
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.2
- type: f1
value: 90.25999999999999
- type: precision
value: 89.45333333333335
- type: recall
value: 92.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 23.15270935960591
- type: f1
value: 19.65673625772148
- type: precision
value: 18.793705293464992
- type: recall
value: 23.15270935960591
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.154929577464785
- type: f1
value: 52.3868463305083
- type: precision
value: 50.14938113529662
- type: recall
value: 59.154929577464785
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.51282051282051
- type: f1
value: 66.8089133089133
- type: precision
value: 65.37645687645687
- type: recall
value: 70.51282051282051
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93
- type: precision
value: 92.23333333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.62212943632568
- type: f1
value: 34.3278276962583
- type: precision
value: 33.07646935732408
- type: recall
value: 38.62212943632568
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 28.1
- type: f1
value: 23.579609223054604
- type: precision
value: 22.39622774921555
- type: recall
value: 28.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.27361563517914
- type: f1
value: 85.12486427795874
- type: precision
value: 83.71335504885994
- type: recall
value: 88.27361563517914
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.6
- type: f1
value: 86.39928571428571
- type: precision
value: 85.4947557997558
- type: recall
value: 88.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.5
- type: f1
value: 83.77952380952381
- type: precision
value: 82.67602564102565
- type: recall
value: 86.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.52755905511812
- type: f1
value: 75.3055868016498
- type: precision
value: 73.81889763779527
- type: recall
value: 79.52755905511812
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.9
- type: f1
value: 73.76261904761905
- type: precision
value: 72.11670995670995
- type: recall
value: 77.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 53.8781163434903
- type: f1
value: 47.25804051288816
- type: precision
value: 45.0603482390186
- type: recall
value: 53.8781163434903
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.10000000000001
- type: f1
value: 88.88
- type: precision
value: 87.96333333333334
- type: recall
value: 91.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.46153846153847
- type: f1
value: 34.43978243978244
- type: precision
value: 33.429487179487175
- type: recall
value: 38.46153846153847
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.9
- type: f1
value: 86.19888888888887
- type: precision
value: 85.07440476190476
- type: recall
value: 88.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.9
- type: f1
value: 82.58857142857143
- type: precision
value: 81.15666666666667
- type: recall
value: 85.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.8
- type: f1
value: 83.36999999999999
- type: precision
value: 81.86833333333333
- type: recall
value: 86.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.51415094339622
- type: f1
value: 63.195000099481234
- type: precision
value: 61.394033442972116
- type: recall
value: 68.51415094339622
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.5
- type: f1
value: 86.14603174603175
- type: precision
value: 85.1162037037037
- type: recall
value: 88.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.62043795620438
- type: f1
value: 94.40389294403892
- type: precision
value: 93.7956204379562
- type: recall
value: 95.62043795620438
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.8
- type: f1
value: 78.6532178932179
- type: precision
value: 77.46348795840176
- type: recall
value: 81.8
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.603
- type: map_at_10
value: 8.5
- type: map_at_100
value: 12.985
- type: map_at_1000
value: 14.466999999999999
- type: map_at_3
value: 4.859999999999999
- type: map_at_5
value: 5.817
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 42.331
- type: mrr_at_100
value: 43.592999999999996
- type: mrr_at_1000
value: 43.592999999999996
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 39.966
- type: ndcg_at_1
value: 26.531
- type: ndcg_at_10
value: 21.353
- type: ndcg_at_100
value: 31.087999999999997
- type: ndcg_at_1000
value: 43.163000000000004
- type: ndcg_at_3
value: 22.999
- type: ndcg_at_5
value: 21.451
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 19.387999999999998
- type: precision_at_100
value: 6.265
- type: precision_at_1000
value: 1.4160000000000001
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 21.224
- type: recall_at_1
value: 2.603
- type: recall_at_10
value: 14.474
- type: recall_at_100
value: 40.287
- type: recall_at_1000
value: 76.606
- type: recall_at_3
value: 5.978
- type: recall_at_5
value: 7.819
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.7848
- type: ap
value: 13.661023167088224
- type: f1
value: 53.61686134460943
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.28183361629882
- type: f1
value: 61.55481034919965
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 35.972128420092396
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.59933241938367
- type: cos_sim_ap
value: 72.20760361208136
- type: cos_sim_f1
value: 66.4447731755424
- type: cos_sim_precision
value: 62.35539102267469
- type: cos_sim_recall
value: 71.10817941952506
- type: dot_accuracy
value: 78.98313166835548
- type: dot_ap
value: 44.492521645493795
- type: dot_f1
value: 45.814889336016094
- type: dot_precision
value: 37.02439024390244
- type: dot_recall
value: 60.07915567282321
- type: euclidean_accuracy
value: 85.3907134767837
- type: euclidean_ap
value: 71.53847289080343
- type: euclidean_f1
value: 65.95952206778834
- type: euclidean_precision
value: 61.31006346328196
- type: euclidean_recall
value: 71.37203166226914
- type: manhattan_accuracy
value: 85.40859510043511
- type: manhattan_ap
value: 71.49664104395515
- type: manhattan_f1
value: 65.98569969356485
- type: manhattan_precision
value: 63.928748144482924
- type: manhattan_recall
value: 68.17941952506597
- type: max_accuracy
value: 85.59933241938367
- type: max_ap
value: 72.20760361208136
- type: max_f1
value: 66.4447731755424
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.83261536073273
- type: cos_sim_ap
value: 85.48178133644264
- type: cos_sim_f1
value: 77.87816307403935
- type: cos_sim_precision
value: 75.88953021114926
- type: cos_sim_recall
value: 79.97382198952879
- type: dot_accuracy
value: 79.76287499514883
- type: dot_ap
value: 59.17438838475084
- type: dot_f1
value: 56.34566667855996
- type: dot_precision
value: 52.50349092359864
- type: dot_recall
value: 60.794579611949494
- type: euclidean_accuracy
value: 88.76857996662397
- type: euclidean_ap
value: 85.22764834359887
- type: euclidean_f1
value: 77.65379751543554
- type: euclidean_precision
value: 75.11152683839401
- type: euclidean_recall
value: 80.37419156144134
- type: manhattan_accuracy
value: 88.6987231730508
- type: manhattan_ap
value: 85.18907981724007
- type: manhattan_f1
value: 77.51967028849757
- type: manhattan_precision
value: 75.49992701795358
- type: manhattan_recall
value: 79.65044656606098
- type: max_accuracy
value: 88.83261536073273
- type: max_ap
value: 85.48178133644264
- type: max_f1
value: 77.87816307403935
---
# jeremyys/multilingual-e5-base-Q5_0-GGUF
This model was converted to GGUF format from [`intfloat/multilingual-e5-base`](https://huggingface.co/intfloat/multilingual-e5-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-base) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jeremyys/multilingual-e5-base-Q5_0-GGUF --hf-file multilingual-e5-base-q5_0.gguf -c 2048
```
|
Daemontatox/Manticore-32B | Daemontatox | 2025-06-16T02:07:16Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"reasoning",
"mathematics",
"coding",
"conversational",
"en",
"dataset:open-thoughts/OpenThoughts2-1M",
"dataset:open-r1/OpenR1-Math-220k",
"dataset:nvidia/OpenMathReasoning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-20T07:46:44Z | ---
base_model: qwen/qwen3-32b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- reasoning
- mathematics
- coding
license: apache-2.0
language:
- en
datasets:
- open-thoughts/OpenThoughts2-1M
- open-r1/OpenR1-Math-220k
- nvidia/OpenMathReasoning
pipeline_tag: text-generation
library_name: transformers
inference: true
new_version: Daemontatox/Manticore-32B
---
<div align="center">
# Manticore-32B
**A powerful reasoning-focused language model optimized for multi-step problem solving**
[](https://opensource.org/licenses/Apache-2.0)
[](https://huggingface.co/spaces/Daemontatox/Manticore32)
[](https://huggingface.co/Daemontatox/Manticore-32B)
</div>
## 📋 Table of Contents
- [Model Overview](#model-overview)
- [Key Capabilities](#key-capabilities)
- [Training Details](#training-details)
- [Dataset Information](#dataset-information)
- [Usage Guide](#usage-guide)
- [Quick Start](#quick-start)
- [Advanced Usage](#advanced-usage)
- [Benchmarks](#benchmarks)
- [Limitations](#limitations)
- [Acknowledgments](#acknowledgments)
- [Citation](#citation)
## 🔍 Model Overview
**Manticore-32B** is a specialized fine-tuned version of Qwen3-32B, engineered to excel at complex reasoning tasks through intensive training on high-quality synthetic data. Developed by [Daemontatox](https://huggingface.co/Daemontatox), this model combines the raw power of Qwen3 with targeted optimization for step-by-step problem solving across multiple domains.
**Base Model:** [unsloth/qwen3-32b-unsloth](https://huggingface.co/unsloth/qwen3-32b-unsloth)
## 🌟 Key Capabilities
Manticore-32B demonstrates exceptional performance in:
- **Mathematical Reasoning**: Complex problem solving with detailed step-by-step explanations
- **Logical Deduction**: Ability to handle intricate puzzles and logical problems
- **Code Generation**: Writing efficient, well-documented code across multiple languages
- **Chain-of-Thought Reasoning**: Breaking down complex problems into manageable steps
- **Multi-step Problem Solving**: Maintaining coherence across extended reasoning chains
## ⚙️ Training Details
- **Framework**: Fine-tuned using TRL + LoRA with Unsloth acceleration techniques
- **Optimization**: Quantized for efficient inference with 4-bit precision (BNB-4bit)
- **Training Process**:
- Custom fine-tuning across ~1 million samples
- Specific focus on multi-step reasoning tasks
- Progressive learning rate scheduling for optimal convergence
- **Hardware**: Single-node A100 80GB GPU setup
- **Training Objective**: Enhance multi-domain reasoning capabilities while maintaining computational efficiency
## 📊 Dataset Information
The model was trained on a carefully curated combination of high-quality reasoning datasets:
| Dataset | Size | Focus Area | Content Type |
|---------|------|------------|--------------|
| [OpenThoughts2-1M](https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M) | ~1.1M examples | General reasoning | Multi-turn conversations, step-by-step solutions |
| [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) | 220K examples | Mathematical reasoning | Problem statements with detailed solutions |
| [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | Supplementary | Advanced mathematics | University-level math problems |
These datasets were processed and filtered using [Curator Viewer](https://curator.bespokelabs.ai/) to ensure the highest quality training examples.
## 🚀 Usage Guide
### Quick Start
```python
from transformers import pipeline
# Initialize the pipeline with the model
pipe = pipeline("text-generation",
model="Daemontatox/Manticore-32B",
torch_dtype="auto")
# Basic chat format
messages = [
{"role": "user", "content": "Can you solve this math problem step by step? If a rectangle has a perimeter of 30 meters and a length that is twice its width, what are the dimensions of the rectangle?"}
]
# Generate response
response = pipe(messages,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95)
print(response[0]["generated_text"])
```
### Advanced Usage
For more control over generation parameters and to utilize advanced features:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Manticore-32B")
model = AutoModelForCausalLM.from_pretrained(
"Daemontatox/Manticore-32B",
torch_dtype=torch.bfloat16,
device_map="auto",
load_in_4bit=True
)
# Format messages in chat template
messages = [
{"role": "system", "content": "You are Manticore-32B, an AI assistant specialized in reasoning and problem-solving. Always show your work step-by-step when tackling problems."},
{"role": "user", "content": "Write a recursive function in Python to calculate the nth Fibonacci number with memoization."}
]
# Create prompt using chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False)
# Generate with more control
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs.input_ids,
max_new_tokens=1024,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
# Decode and print result
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)
```
#### Using with Unsloth for Even Faster Inference
```python
from unsloth import FastLanguageModel
import torch
# Load with Unsloth for optimized inference
model, tokenizer = FastLanguageModel.from_pretrained(
"Daemontatox/Manticore-32B",
dtype=torch.bfloat16,
load_in_4bit=True,
token="your_huggingface_token" # Optional
)
# Create prompt
messages = [
{"role": "user", "content": "Explain the concept of computational complexity and give examples of O(1), O(n), and O(n²) algorithms."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False)
# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs.input_ids,
max_new_tokens=768,
temperature=0.7
)
# Decode
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)
```
## 📈 Benchmarks
Manticore-32B demonstrates strong performance across multiple reasoning benchmarks:
| Benchmark | Score | Base Model Score | Improvement |
|-----------|-------|------------------|-------------|
| GSM8K | 78.2% | 71.5% | +6.7% |
| MATH | 42.5% | 37.8% | +4.7% |
| HumanEval | 75.6% | 71.3% | +4.3% |
| BBH | 69.3% | 64.8% | +4.5% |
*Note: These benchmarks reflect zero-shot performance with temperature=0.0*
## ⚠️ Limitations
Despite its strengths, users should be aware of the following limitations:
- **Language Support**: Primarily optimized for English; performance degrades significantly for other languages
- **Factual Accuracy**: While reasoning skills are enhanced, the model may still hallucinate factual information
- **Domain Knowledge**: Specialized knowledge outside common domains may be limited or incorrect
- **Context Window**: Default context window is inherited from Qwen3-32B (128K tokens)
- **Bias**: Inherits potential biases from base model and synthetic training data
## 🙏 Acknowledgments
This model builds upon the exceptional work of:
- [Qwen Team](https://huggingface.co/qwen) for the base Qwen3-32B model
- [Unsloth](https://github.com/unsloth/unsloth) for optimization techniques
- [OpenThoughts Team](https://huggingface.co/open-thoughts) for their invaluable dataset
## 📄 Citation
If you use this model in your research or applications, please cite:
```bibtex
@misc{daemontatox2025manticore,
author = {Daemontatox},
title = {Manticore-32B: A Fine-tuned Language Model for Advanced Reasoning},
year = {2025},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/Daemontatox/Manticore-32B}}
}
``` |
Daemontatox/Grifflet-2 | Daemontatox | 2025-06-16T02:05:58Z | 25 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"dataset:cognitivecomputations/dolphin-r1",
"dataset:open-thoughts/OpenThoughts2-1M",
"dataset:open-r1/Mixture-of-Thoughts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-05T17:04:10Z | ---
base_model: qwen/qwen3-8b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
datasets:
- cognitivecomputations/dolphin-r1
- open-thoughts/OpenThoughts2-1M
- open-r1/Mixture-of-Thoughts
library_name: transformers
new_version: qwen/qwen3-8b
---
## **Model Description**
### **Purpose**
"Daemontatox/Grifflet-2" is a state-of-the-art language model designed to excel in hybrid tasks that combine conversational abilities with reasoning capabilities. The model has been meticulously fine-tuned using advanced techniques to ensure it performs well both when engaging in dynamic, human-like conversations and when tackling complex, multi-step reasoning problems.
### **Training Approach**
The model was trained using a unique **hybrid training regimen**, which blends datasets focused on both **chatting** and **reasoning**. This dual-pronged approach ensures the model can seamlessly transition between casual conversation and more structured, logical thinking tasks.
Key features of the training methodology include:
- **Efficiency**: Training time was reduced by a factor of 2x using Unsloth, an open-source library optimized for faster fine-tuning.
- **Hybrid Dataset Combination**: By combining diverse datasets from multiple sources, the model benefits from exposure to a wide variety of conversational patterns and reasoning challenges.
- **Advanced Fine-Tuning**: Leveraging Hugging Face’s TRL (Transformer Reinforcement Learning) library, the model underwent supervised fine-tuning followed by reinforcement learning steps to refine its outputs.
---
## **Technical Details**
### **Base Model Architecture**
- **Base Model:** Qwen3-8B
- **Architecture:** Transformer-based architecture with 8 billion parameters.
- **Language:** English (`en`)
- **Library Used:** [Transformers](https://huggingface.co/transformers) by Hugging Face
### **Fine-Tuning Datasets**
The model leverages a combination of high-quality datasets to achieve its hybrid capabilities:
1. **CognitiveComputations/Dolphin-R1**: A dataset designed to enhance reasoning and problem-solving skills through structured prompts and complex scenarios.
2. **Open-Thoughts/OpenThoughts2-1M**: A large-scale dataset containing millions of examples of human-like dialogue, enabling the model to generate natural, fluent conversations.
3. **Open-R1/Mixture-of-Thoughts**: A specialized dataset focused on mixing logical reasoning with conversational context, helping the model bridge the gap between chat and reasoning.
### **Training Methodology**
- **Preprocessing:** Data augmentation techniques were applied to increase diversity within the datasets, ensuring robustness across different contexts.
- **Optimization:** Fine-tuning was conducted using mixed precision training (FP16) for computational efficiency.
- **Evaluation:** Rigorous evaluation metrics, including BLEU, ROUGE, and custom benchmarks for reasoning accuracy, were used to validate performance.
---
## **Capabilities**
### **Chatting Abilities**
- **Natural Language Understanding:** The model excels at understanding nuanced conversational inputs, making it ideal for applications such as virtual assistants, customer support bots, and interactive storytelling.
- **Contextual Awareness:** It maintains coherence over long conversations and adapts dynamically to changing topics or tones.
- **Engagement:** Designed to produce engaging, empathetic responses that mimic human interaction.
### **Reasoning Abilities**
- **Logical Deduction:** Capable of solving puzzles, answering analytical questions, and performing step-by-step reasoning tasks.
- **Multi-Step Problem Solving:** Handles complex queries requiring sequential logic, such as mathematical computations, algorithmic reasoning, and decision-making under constraints.
- **Knowledge Integration:** Combines factual knowledge with reasoning to provide accurate and insightful answers.
---
## **Intended Use Cases**
### **Primary Applications**
1. **Conversational AI Systems:** Deploy the model in chatbots, virtual assistants, or any system requiring natural, fluid dialogue.
2. **Educational Tools:** Use the model to create tutoring systems capable of explaining concepts, guiding students through problems, and providing feedback.
3. **Problem-Solving Assistants:** Leverage its reasoning abilities for applications like coding assistance, scientific research, or business analytics.
### **Secondary Applications**
- Content generation (e.g., writing essays, articles, or creative pieces).
- Knowledge base querying for industries like healthcare, law, or finance.
- Game development (e.g., creating intelligent NPCs with reasoning capabilities).
---
## **Limitations**
While "Daemontatox/Grifflet-2" demonstrates impressive versatility, users should be aware of the following limitations:
- **Bias Inheritance:** Like all models trained on large datasets, it may inherit biases present in the source material. Careful monitoring is recommended for sensitive use cases.
- **Domain-Specific Expertise:** While the model performs well across general domains, highly specialized fields might require additional fine-tuning.
- **Resource Intensity:** As a large language model, it demands significant computational resources for inference, especially in real-time applications.
---
## **Ethical Considerations**
- **Fair Use Policy:** The model must not be used for malicious purposes, including but not limited to generating harmful content, misinformation, or discriminatory outputs.
- **Transparency:** Users are encouraged to disclose when they are interacting with an AI system powered by this model.
- **Data Privacy:** Ensure compliance with data protection regulations (e.g., GDPR) when deploying the model in environments handling personal information.
---
## **How to Use**
### **Installation**
To use "Daemontatox/Grifflet-2," install the necessary libraries and load the model via Hugging Face's `transformers` library:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Grifflet-2")
model = AutoModelForCausalLM.from_pretrained("Daemontatox/Grifflet-2")
# Generate text
input_text = "Explain the concept of gravity."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### **Hardware Requirements**
- Recommended: GPU with at least 24GB VRAM (e.g., NVIDIA A100 or similar).
- Minimum: CPU with sufficient RAM for smaller batch sizes.
---
## **Acknowledgments**
- **Unsloth Team:** For their contribution to accelerating the fine-tuning process.
- **Hugging Face Community:** For providing the foundational tools and libraries that made this project possible.
- **Dataset Contributors:** Special thanks to the creators of Dolphin-R1, OpenThoughts2-1M, and Mixture-of-Thoughts for their invaluable contributions.
---
## **Contact Information**
For inquiries, feedback, or collaboration opportunities, please reach out to the developer:
- **Developer:** Daemontatox
- **Email:** [[email protected]](mailto:[email protected])
- **GitHub:** [https://github.com/Daemontatox](https://github.com/Daemontatox)
--- |
CriteriaPO/qwen2.5-3b-dpo-finegrained-no-tools | CriteriaPO | 2025-06-16T02:05:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:CriteriaPO/qwen2.5-3b-sft-10",
"base_model:finetune:CriteriaPO/qwen2.5-3b-sft-10",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-09T23:27:00Z | ---
base_model: CriteriaPO/qwen2.5-3b-sft-10
library_name: transformers
model_name: qwen2.5-3b-dpo-finegrained-no-tools
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for qwen2.5-3b-dpo-finegrained-no-tools
This model is a fine-tuned version of [CriteriaPO/qwen2.5-3b-sft-10](https://huggingface.co/CriteriaPO/qwen2.5-3b-sft-10).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="CriteriaPO/qwen2.5-3b-dpo-finegrained-no-tools", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bborges/CriteriaPreferences/runs/auibjmrb)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.1.2+cu121
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
New-tutorial-Sophie-Rain-Viral-Videos/FULL.VIDEO.Sophie.Rain.Spiderman.Viral.Video.Tutorial.Official | New-tutorial-Sophie-Rain-Viral-Videos | 2025-06-16T02:04:56Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T02:04:24Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
CelestialWandererOfTheVoid/test | CelestialWandererOfTheVoid | 2025-06-16T02:04:32Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T02:04:31Z | # Audio Enhancement API
This repository contains a FastAPI-based audio enhancement model API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Makes necessary preparations (downloading checkpoints, etc.) and initializes model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
## Setup and Usage
1. Install the requirements:
```
pip install -r app/requirements.txt
```
2. Run the API:
```
python -m app.app
```
3. Access the API documentation at `http://localhost:8000/docs`
|
BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175 | BootesVoid | 2025-06-16T02:02:57Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-16T02:02:51Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: EMILYY
---
# Cmbyblijq03Bvrdqs0Ce71Ulk_Cmbyfdvbe03I5Rdqsnp2Il175
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `EMILYY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "EMILYY",
"lora_weights": "https://huggingface.co/BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175', weight_name='lora.safetensors')
image = pipeline('EMILYY').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbyblijq03bvrdqs0ce71ulk_cmbyfdvbe03i5rdqsnp2il175/discussions) to add images that show off what you’ve made with this LoRA.
|
rmdhirr/suja-lorab-ep5-suja-5000 | rmdhirr | 2025-06-16T02:02:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:rmdhirr/merged-suja-latest",
"base_model:adapter:rmdhirr/merged-suja-latest",
"region:us"
] | null | 2025-06-16T02:01:24Z | ---
base_model: rmdhirr/merged-suja-latest
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
michelescotto/trainer_output | michelescotto | 2025-06-16T02:02:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"base_model:finetune:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-16T02:01:21Z | ---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: trainer_output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trainer_output
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1910
- Accuracy: 0.948
- F1 Macro: 0.8547
- Kappa Score: 0.7094
- Accuracy Balanced: 0.8568
- Precision Macro: 0.8526
- Recall Macro: 0.8568
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.8e-05
- train_batch_size: 16
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.15_epoch2 | MinaMila | 2025-06-16T02:01:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:59:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF | JohnRoger | 2025-06-16T02:00:51Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Huihui-MoE-12B-A4B-abliterated",
"base_model:quantized:huihui-ai/Huihui-MoE-12B-A4B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:59:54Z | ---
license: apache-2.0
base_model: huihui-ai/Huihui-MoE-12B-A4B-abliterated
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- moe
- llama-cpp
- gguf-my-repo
extra_gated_prompt: '**Usage Warnings**
“**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering
has been significantly reduced, potentially generating sensitive, controversial,
or inappropriate content. Users should exercise caution and rigorously review generated
outputs.
“**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s
outputs may be inappropriate for public settings, underage users, or applications
requiring high security.
“**Legal and Ethical Responsibilities**“: Users must ensure their usage complies
with local laws and ethical standards. Generated content may carry legal or ethical
risks, and users are solely responsible for any consequences.
“**Research and Experimental Use**“: It is recommended to use this model for research,
testing, or controlled environments, avoiding direct use in production or public-facing
commercial applications.
“**Monitoring and Review Recommendations**“: Users are strongly advised to monitor
model outputs in real-time and conduct manual reviews when necessary to prevent
the dissemination of inappropriate content.
“**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone
rigorous safety optimization. huihui.ai bears no responsibility for any consequences
arising from its use.'
---
# JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF
This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-12B-A4B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo JohnRoger/Huihui-MoE-12B-A4B-abliterated-Q8_0-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q8_0.gguf -c 2048
```
|
Enzogbs/Reinforce-1 | Enzogbs | 2025-06-16T02:00:18Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-16T02:00:06Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
CriteriaPO/qwen2.5-3b-dpo-coarse-no-tools | CriteriaPO | 2025-06-16T01:59:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:CriteriaPO/qwen2.5-3b-sft-10",
"base_model:finetune:CriteriaPO/qwen2.5-3b-sft-10",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-09T23:23:50Z | ---
base_model: CriteriaPO/qwen2.5-3b-sft-10
library_name: transformers
model_name: qwen2.5-3b-dpo-coarse-no-tools
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for qwen2.5-3b-dpo-coarse-no-tools
This model is a fine-tuned version of [CriteriaPO/qwen2.5-3b-sft-10](https://huggingface.co/CriteriaPO/qwen2.5-3b-sft-10).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="CriteriaPO/qwen2.5-3b-dpo-coarse-no-tools", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bborges/CriteriaPreferences/runs/neggxd33)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.1.2+cu121
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi | BootesVoid | 2025-06-16T01:58:16Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-16T01:58:15Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: PINEAPPLE
---
# Cmby5Pvyq02Vnrdqsdgmevjip_Cmbyeg1Bt03H4Rdqsprxcmrzi
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `PINEAPPLE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "PINEAPPLE",
"lora_weights": "https://huggingface.co/BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi', weight_name='lora.safetensors')
image = pipeline('PINEAPPLE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmby5pvyq02vnrdqsdgmevjip_cmbyeg1bt03h4rdqsprxcmrzi/discussions) to add images that show off what you’ve made with this LoRA.
|
johngreendr1/8e330f4b-e810-43e2-ab04-84939525ab65 | johngreendr1 | 2025-06-16T01:57:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-4B-Base",
"base_model:adapter:Qwen/Qwen3-4B-Base",
"region:us"
] | null | 2025-06-15T23:22:45Z | ---
base_model: Qwen/Qwen3-4B-Base
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1 |
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.15_0.75_epoch1 | MinaMila | 2025-06-16T01:56:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:54:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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wavespeed/Wan2.1-VACE-14B-bf16 | wavespeed | 2025-06-16T01:56:21Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"vace",
"video generation",
"video-to-video editing",
"refernce-to-video",
"image-to-video",
"en",
"zh",
"arxiv:2503.20314",
"arxiv:2503.07598",
"arxiv:2309.14509",
"arxiv:2310.01889",
"license:apache-2.0",
"region:us"
] | image-to-video | 2025-06-16T01:48:57Z | ---
license: apache-2.0
language:
- en
- zh
tags:
- video generation
- video-to-video editing
- refernce-to-video
pipeline_tag: image-to-video
---
# Wan2.1
<p align="center">
<img src="assets/logo.png" width="400"/>
<p>
<p align="center">
💜 <a href="https://wan.video"><b>Wan</b></a>    ｜    🖥️ <a href="https://github.com/Wan-Video/Wan2.1">GitHub</a>    |   🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>   |    📑 <a href="https://arxiv.org/abs/2503.20314">Technical Report</a>    |    📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a>    |   💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>   |    📖 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>  
<br>
-----
[**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be>
In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
- 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
- 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
- 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
- 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
- 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
## Video Demos
<div align="center">
<video width="80%" controls>
<source src="https://cloud.video.taobao.com/vod/Jth64Y7wNoPcJki_Bo1ZJTDBvNjsgjlVKsNs05Fqfps.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div>
## 🔥 Latest News!!
* May 14, 2025: 👋 We introduce **Wan2.1** [VACE](https://github.com/ali-vilab/VACE), an all-in-one model for video creation and editing, along with its [inference code](#run-vace), [weights](#model-download), and [technical report](https://arxiv.org/abs/2503.07598)!
* Apr 17, 2025: 👋 We introduce **Wan2.1** [FLF2V](#run-first-last-frame-to-video-generation) with its inference code and weights!
* Mar 21, 2025: 👋 We are excited to announce the release of the **Wan2.1** [technical report](https://files.alicdn.com/tpsservice/5c9de1c74de03972b7aa657e5a54756b.pdf). We welcome discussions and feedback!
* Mar 3, 2025: 👋 **Wan2.1**'s T2V and I2V have been integrated into Diffusers ([T2V](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanPipeline) | [I2V](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan#diffusers.WanImageToVideoPipeline)). Feel free to give it a try!
* Feb 27, 2025: 👋 **Wan2.1** has been integrated into [ComfyUI](https://comfyanonymous.github.io/ComfyUI_examples/wan/). Enjoy!
* Feb 25, 2025: 👋 We've released the inference code and weights of **Wan2.1**.
## Community Works
If your work has improved **Wan2.1** and you would like more people to see it, please inform us.
- [Phantom](https://github.com/Phantom-video/Phantom) has developed a unified video generation framework for single and multi-subject references based on **Wan2.1-T2V-1.3B**. Please refer to [their examples](https://github.com/Phantom-video/Phantom).
- [UniAnimate-DiT](https://github.com/ali-vilab/UniAnimate-DiT), based on **Wan2.1-14B-I2V**, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it!
- [CFG-Zero](https://github.com/WeichenFan/CFG-Zero-star) enhances **Wan2.1** (covering both T2V and I2V models) from the perspective of CFG.
- [TeaCache](https://github.com/ali-vilab/TeaCache) now supports **Wan2.1** acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try!
- [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) provides more support for **Wan2.1**, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to [their examples](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo).
## 📑 Todo List
- Wan2.1 Text-to-Video
- [x] Multi-GPU Inference code of the 14B and 1.3B models
- [x] Checkpoints of the 14B and 1.3B models
- [x] Gradio demo
- [x] ComfyUI integration
- [x] Diffusers integration
- [ ] Diffusers + Multi-GPU Inference
- Wan2.1 Image-to-Video
- [x] Multi-GPU Inference code of the 14B model
- [x] Checkpoints of the 14B model
- [x] Gradio demo
- [x] ComfyUI integration
- [x] Diffusers integration
- [ ] Diffusers + Multi-GPU Inference
- Wan2.1 First-Last-Frame-to-Video
- [x] Multi-GPU Inference code of the 14B model
- [x] Checkpoints of the 14B model
- [x] Gradio demo
- [ ] ComfyUI integration
- [ ] Diffusers integration
- [ ] Diffusers + Multi-GPU Inference
- Wan2.1 VACE
- [x] Multi-GPU Inference code of the 14B and 1.3B models
- [x] Checkpoints of the 14B and 1.3B models
- [x] Gradio demo
- [x] ComfyUI integration
- [ ] Diffusers integration
- [ ] Diffusers + Multi-GPU Inference
## Quickstart
#### Installation
Clone the repo:
```sh
git clone https://github.com/Wan-Video/Wan2.1.git
cd Wan2.1
```
Install dependencies:
```sh
# Ensure torch >= 2.4.0
pip install -r requirements.txt
```
#### Model Download
| Models | Download Link | Notes |
|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------|
| T2V-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | Supports both 480P and 720P
| I2V-14B-720P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | Supports 720P
| I2V-14B-480P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | Supports 480P
| T2V-1.3B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | Supports 480P
| FLF2V-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | Supports 720P
| VACE-1.3B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | Supports 480P
| VACE-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | Supports both 480P and 720P
> 💡Note:
> * The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.
> * For the first-last frame to video generation, we train our model primarily on Chinese text-video pairs. Therefore, we recommend using Chinese prompt to achieve better results.
Download models using huggingface-cli:
``` sh
pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B
```
Download models using modelscope-cli:
``` sh
pip install modelscope
modelscope download Wan-AI/Wan2.1-T2V-14B --local_dir ./Wan2.1-T2V-14B
```
#### Run Text-to-Video Generation
This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:
<table>
<thead>
<tr>
<th rowspan="2">Task</th>
<th colspan="2">Resolution</th>
<th rowspan="2">Model</th>
</tr>
<tr>
<th>480P</th>
<th>720P</th>
</tr>
</thead>
<tbody>
<tr>
<td>t2v-14B</td>
<td style="color: green;">✔️</td>
<td style="color: green;">✔️</td>
<td>Wan2.1-T2V-14B</td>
</tr>
<tr>
<td>t2v-1.3B</td>
<td style="color: green;">✔️</td>
<td style="color: red;">❌</td>
<td>Wan2.1-T2V-1.3B</td>
</tr>
</tbody>
</table>
##### (1) Without Prompt Extension
To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step.
- Single-GPU inference
``` sh
python generate.py --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
```
If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True` and `--t5_cpu` options to reduce GPU memory usage. For example, on an RTX 4090 GPU:
``` sh
python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
```
> 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance.
- Multi-GPU inference using FSDP + xDiT USP
We use FSDP and [xDiT](https://github.com/xdit-project/xDiT) USP to accelerate inference.
* Ulysess Strategy
If you want to use [`Ulysses`](https://arxiv.org/abs/2309.14509) strategy, you should set `--ulysses_size $GPU_NUMS`. Note that the `num_heads` should be divisible by `ulysses_size` if you wish to use `Ulysess` strategy. For the 1.3B model, the `num_heads` is `12` which can't be divided by 8 (as most multi-GPU machines have 8 GPUs). Therefore, it is recommended to use `Ring Strategy` instead.
* Ring Strategy
If you want to use [`Ring`](https://arxiv.org/pdf/2310.01889) strategy, you should set `--ring_size $GPU_NUMS`. Note that the `sequence length` should be divisible by `ring_size` when using the `Ring` strategy.
Of course, you can also combine the use of `Ulysses` and `Ring` strategies.
``` sh
pip install "xfuser>=0.4.1"
torchrun --nproc_per_node=8 generate.py --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
```
##### (2) Using Prompt Extension
Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension:
- Use the Dashscope API for extension.
- Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) | [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)).
- Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1).
- Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks.
- You can modify the model used for extension with the parameter `--prompt_extend_model`. For example:
```sh
DASH_API_KEY=your_key python generate.py --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'zh'
```
- Using a local model for extension.
- By default, the Qwen model on HuggingFace is used for this extension. Users can choose Qwen models or other models based on the available GPU memory size.
- For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`.
- For image-to-video or first-last-frame-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`.
- Larger models generally provide better extension results but require more GPU memory.
- You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example:
``` sh
python generate.py --task t2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-T2V-14B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'zh'
```
##### (3) Running with Diffusers
You can easily inference **Wan2.1**-T2V using Diffusers with the following command:
``` python
import torch
from diffusers.utils import export_to_video
from diffusers import AutoencoderKLWan, WanPipeline
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
# Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
flow_shift = 5.0 # 5.0 for 720P, 3.0 for 480P
scheduler = UniPCMultistepScheduler(prediction_type='flow_prediction', use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
pipe.scheduler = scheduler
pipe.to("cuda")
prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=720,
width=1280,
num_frames=81,
guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)
```
> 💡Note: Please note that this example does not integrate Prompt Extension and distributed inference. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers.
##### (4) Running local gradio
``` sh
cd gradio
# if one uses dashscope’s API for prompt extension
DASH_API_KEY=your_key python t2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-14B
# if one uses a local model for prompt extension
python t2v_14B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-14B
```
#### Run Image-to-Video Generation
Similar to Text-to-Video, Image-to-Video is also divided into processes with and without the prompt extension step. The specific parameters and their corresponding settings are as follows:
<table>
<thead>
<tr>
<th rowspan="2">Task</th>
<th colspan="2">Resolution</th>
<th rowspan="2">Model</th>
</tr>
<tr>
<th>480P</th>
<th>720P</th>
</tr>
</thead>
<tbody>
<tr>
<td>i2v-14B</td>
<td style="color: green;">❌</td>
<td style="color: green;">✔️</td>
<td>Wan2.1-I2V-14B-720P</td>
</tr>
<tr>
<td>i2v-14B</td>
<td style="color: green;">✔️</td>
<td style="color: red;">❌</td>
<td>Wan2.1-T2V-14B-480P</td>
</tr>
</tbody>
</table>
##### (1) Without Prompt Extension
- Single-GPU inference
```sh
python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
```
> 💡For the Image-to-Video task, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
- Multi-GPU inference using FSDP + xDiT USP
```sh
pip install "xfuser>=0.4.1"
torchrun --nproc_per_node=8 generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
```
##### (2) Using Prompt Extension
The process of prompt extension can be referenced [here](#2-using-prompt-extention).
Run with local prompt extension using `Qwen/Qwen2.5-VL-7B-Instruct`:
```
python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
```
Run with remote prompt extension using `dashscope`:
```
DASH_API_KEY=your_key python generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --use_prompt_extend --prompt_extend_method 'dashscope' --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
```
##### (3) Running with Diffusers
You can easily inference **Wan2.1**-I2V using Diffusers with the following command:
``` python
import torch
import numpy as np
from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
from diffusers.utils import export_to_video, load_image
from transformers import CLIPVisionModel
# Available models: Wan-AI/Wan2.1-I2V-14B-480P-Diffusers, Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
model_id = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"
image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16)
pipe.to("cuda")
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
)
max_area = 720 * 1280
aspect_ratio = image.height / image.width
mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
image = image.resize((width, height))
prompt = (
"An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
"the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
)
negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
output = pipe(
image=image,
prompt=prompt,
negative_prompt=negative_prompt,
height=height, width=width,
num_frames=81,
guidance_scale=5.0
).frames[0]
export_to_video(output, "output.mp4", fps=16)
```
> 💡Note: Please note that this example does not integrate Prompt Extension and distributed inference. We will soon update with the integrated prompt extension and multi-GPU version of Diffusers.
##### (4) Running local gradio
```sh
cd gradio
# if one only uses 480P model in gradio
DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P
# if one only uses 720P model in gradio
DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
# if one uses both 480P and 720P models in gradio
DASH_API_KEY=your_key python i2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_480p ./Wan2.1-I2V-14B-480P --ckpt_dir_720p ./Wan2.1-I2V-14B-720P
```
#### Run First-Last-Frame-to-Video Generation
First-Last-Frame-to-Video is also divided into processes with and without the prompt extension step. Currently, only 720P is supported. The specific parameters and corresponding settings are as follows:
<table>
<thead>
<tr>
<th rowspan="2">Task</th>
<th colspan="2">Resolution</th>
<th rowspan="2">Model</th>
</tr>
<tr>
<th>480P</th>
<th>720P</th>
</tr>
</thead>
<tbody>
<tr>
<td>flf2v-14B</td>
<td style="color: green;">❌</td>
<td style="color: green;">✔️</td>
<td>Wan2.1-FLF2V-14B-720P</td>
</tr>
</tbody>
</table>
##### (1) Without Prompt Extension
- Single-GPU inference
```sh
python generate.py --task flf2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
```
> 💡Similar to Image-to-Video, the `size` parameter represents the area of the generated video, with the aspect ratio following that of the original input image.
- Multi-GPU inference using FSDP + xDiT USP
```sh
pip install "xfuser>=0.4.1"
torchrun --nproc_per_node=8 generate.py --task flf2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
```
##### (2) Using Prompt Extension
The process of prompt extension can be referenced [here](#2-using-prompt-extention).
Run with local prompt extension using `Qwen/Qwen2.5-VL-7B-Instruct`:
```
python generate.py --task flf2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --use_prompt_extend --prompt_extend_model Qwen/Qwen2.5-VL-7B-Instruct --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
```
Run with remote prompt extension using `dashscope`:
```
DASH_API_KEY=your_key python generate.py --task flf2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-FLF2V-14B-720P --first_frame examples/flf2v_input_first_frame.png --last_frame examples/flf2v_input_last_frame.png --use_prompt_extend --prompt_extend_method 'dashscope' --prompt "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird’s feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
```
##### (3) Running local gradio
```sh
cd gradio
# use 720P model in gradio
DASH_API_KEY=your_key python flf2v_14B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir_720p ./Wan2.1-FLF2V-14B-720P
```
#### Run VACE
[VACE](https://github.com/ali-vilab/VACE) now supports two models (1.3B and 14B) and two main resolutions (480P and 720P).
The input supports any resolution, but to achieve optimal results, the video size should fall within a specific range.
The parameters and configurations for these models are as follows:
<table>
<thead>
<tr>
<th rowspan="2">Task</th>
<th colspan="2">Resolution</th>
<th rowspan="2">Model</th>
</tr>
<tr>
<th>480P(~81x480x832)</th>
<th>720P(~81x720x1280)</th>
</tr>
</thead>
<tbody>
<tr>
<td>VACE</td>
<td style="color: green; text-align: center; vertical-align: middle;">✔️</td>
<td style="color: green; text-align: center; vertical-align: middle;">✔️</td>
<td>Wan2.1-VACE-14B</td>
</tr>
<tr>
<td>VACE</td>
<td style="color: green; text-align: center; vertical-align: middle;">✔️</td>
<td style="color: red; text-align: center; vertical-align: middle;">❌</td>
<td>Wan2.1-VACE-1.3B</td>
</tr>
</tbody>
</table>
In VACE, users can input text prompt and optional video, mask, and image for video generation or editing. Detailed instructions for using VACE can be found in the [User Guide](https://github.com/ali-vilab/VACE/blob/main/UserGuide.md).
The execution process is as follows:
##### (1) Preprocessing
User-collected materials needs to be preprocessed into VACE-recognizable inputs, including `src_video`, `src_mask`, `src_ref_images`, and `prompt`.
For R2V (Reference-to-Video Generation), you may skip this preprocessing, but for V2V (Video-to-Video Editing) and MV2V (Masked Video-to-Video Editing) tasks, additional preprocessing is required to obtain video with conditions such as depth, pose or masked regions.
For more details, please refer to [vace_preproccess](https://github.com/ali-vilab/VACE/blob/main/vace/vace_preproccess.py).
##### (2) cli inference
- Single-GPU inference
```sh
python generate.py --task vace-1.3B --size 832*480 --ckpt_dir ./Wan2.1-VACE-1.3B --src_ref_images examples/girl.png,examples/snake.png --prompt "在一个欢乐而充满节日气氛的场景中，穿着鲜艳红色春服的小女孩正与她的可爱卡通蛇嬉戏。她的春服上绣着金色吉祥图案，散发着喜庆的气息，脸上洋溢着灿烂的笑容。蛇身呈现出亮眼的绿色，形状圆润，宽大的眼睛让它显得既友善又幽默。小女孩欢快地用手轻轻抚摸着蛇的头部，共同享受着这温馨的时刻。周围五彩斑斓的灯笼和彩带装饰着环境，阳光透过洒在她们身上，营造出一个充满友爱与幸福的新年氛围。"
```
- Multi-GPU inference using FSDP + xDiT USP
```sh
torchrun --nproc_per_node=8 generate.py --task vace-14B --size 1280*720 --ckpt_dir ./Wan2.1-VACE-14B --dit_fsdp --t5_fsdp --ulysses_size 8 --src_ref_images examples/girl.png,examples/snake.png --prompt "在一个欢乐而充满节日气氛的场景中，穿着鲜艳红色春服的小女孩正与她的可爱卡通蛇嬉戏。她的春服上绣着金色吉祥图案，散发着喜庆的气息，脸上洋溢着灿烂的笑容。蛇身呈现出亮眼的绿色，形状圆润，宽大的眼睛让它显得既友善又幽默。小女孩欢快地用手轻轻抚摸着蛇的头部，共同享受着这温馨的时刻。周围五彩斑斓的灯笼和彩带装饰着环境，阳光透过洒在她们身上，营造出一个充满友爱与幸福的新年氛围。"
```
##### (3) Running local gradio
- Single-GPU inference
```sh
python gradio/vace.py --ckpt_dir ./Wan2.1-VACE-1.3B
```
- Multi-GPU inference using FSDP + xDiT USP
```sh
python gradio/vace.py --mp --ulysses_size 8 --ckpt_dir ./Wan2.1-VACE-14B/
```
#### Run Text-to-Image Generation
Wan2.1 is a unified model for both image and video generation. Since it was trained on both types of data, it can also generate images. The command for generating images is similar to video generation, as follows:
##### (1) Without Prompt Extension
- Single-GPU inference
```sh
python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人'
```
- Multi-GPU inference using FSDP + xDiT USP
```sh
torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B --size 1024*1024 --prompt '一个朴素端庄的美人' --ckpt_dir ./Wan2.1-T2V-14B
```
##### (2) With Prompt Extention
- Single-GPU inference
```sh
python generate.py --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人' --use_prompt_extend
```
- Multi-GPU inference using FSDP + xDiT USP
```sh
torchrun --nproc_per_node=8 generate.py --dit_fsdp --t5_fsdp --ulysses_size 8 --base_seed 0 --frame_num 1 --task t2i-14B --size 1024*1024 --ckpt_dir ./Wan2.1-T2V-14B --prompt '一个朴素端庄的美人' --use_prompt_extend
```
## Manual Evaluation
##### (1) Text-to-Video Evaluation
Through manual evaluation, the results generated after prompt extension are superior to those from both closed-source and open-source models.
<div align="center">
<img src="assets/t2v_res.jpg" alt="" style="width: 80%;" />
</div>
##### (2) Image-to-Video Evaluation
We also conducted extensive manual evaluations to evaluate the performance of the Image-to-Video model, and the results are presented in the table below. The results clearly indicate that **Wan2.1** outperforms both closed-source and open-source models.
<div align="center">
<img src="assets/i2v_res.png" alt="" style="width: 80%;" />
</div>
## Computational Efficiency on Different GPUs
We test the computational efficiency of different **Wan2.1** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
<div align="center">
<img src="assets/comp_effic.png" alt="" style="width: 80%;" />
</div>
> The parameter settings for the tests presented in this table are as follows:
> (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
> (2) For the 14B model on 1 GPU, use `--offload_model True`;
> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
> 💡Note: T2V-14B is slower than I2V-14B because the former samples 50 steps while the latter uses 40 steps.
-------
## Introduction of Wan2.1
**Wan2.1** is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
##### (1) 3D Variational Autoencoders
We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
<div align="center">
<img src="assets/video_vae_res.jpg" alt="" style="width: 80%;" />
</div>
##### (2) Video Diffusion DiT
**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
<div align="center">
<img src="assets/video_dit_arch.jpg" alt="" style="width: 80%;" />
</div>
| Model | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
| 1.3B | 1536 | 16 | 16 | 8960 | 256 | 12 | 30 |
| 14B | 5120 | 16 | 16 | 13824 | 256 | 40 | 40 |
##### Data
We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
##### Comparisons to SOTA
We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performance. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. We then compute the total score by performing a weighted calculation on the scores of each dimension, utilizing weights derived from human preferences in the matching process. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
## Citation
If you find our work helpful, please cite us.
```
@article{wan2025,
title={Wan: Open and Advanced Large-Scale Video Generative Models},
author={Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
journal = {arXiv preprint arXiv:2503.20314},
year={2025}
}
```
## License Agreement
The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
## Acknowledgements
We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
## Contact Us
If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)! |
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