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damgomz/ft_8_18e6_x8 | damgomz | "2024-07-13T07:17:44Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-20T16:01:00Z" | ---
language: en
tags:
- text-classification
pipeline_tag: text-classification
widget:
- text: GEPS Techno is the pioneer of hybridization of renewable energies at sea.
We imagine, design and commercialize innovative off-grid systems that aim to generate
power at sea, stabilize and collect data. The success of our low power platforms
WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity
platform.
---
## Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|--------------------------|---------------------------------|
| Duration (in seconds) | 77352.77055954933 |
| Emissions (Co2eq in kg) | 0.0468073450853753 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 0.9131904317042898 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.080574989101539 |
| Consumed energy (kWh) | 0.9937654208058287 |
| Country name | Switzerland |
| Cloud provider | nan |
| Cloud region | nan |
| CPU count | 2 |
| CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz |
| GPU count | nan |
| GPU model | nan |
## Environmental Impact (for one core)
| Metric | Value |
|--------------------------|---------------------------------|
| CPU energy (kWh) | 0.14890408332713248 |
| Emissions (Co2eq in kg) | 0.03029650180249015 |
## Note
12 juillet 2024
## My Config
| Config | Value |
|--------------------------|-----------------|
| checkpoint | damgomz/fp_bs32_lr1e4_x8 |
| model_name | ft_8_18e6_x8 |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 1.8e-05 |
| batch_size | 8 |
| weight_decay | 0.0 |
| warm_up_prop | 0.0 |
| drop_out_prob | 0.1 |
| packing_length | 100 |
| train_test_split | 0.2 |
| num_steps | 29328 |
## Training and Testing steps
Epoch | Train Loss | Test Loss | F-beta Score
---|---|---|---
| 0 | 0.000000 | 0.697937 | 0.331425 |
| 1 | 0.263267 | 0.220389 | 0.904429 |
| 2 | 0.163043 | 0.210519 | 0.942222 |
| 3 | 0.101605 | 0.267899 | 0.921057 |
| 4 | 0.056810 | 0.332097 | 0.902358 |
| 5 | 0.036866 | 0.327206 | 0.931372 |
| 6 | 0.028792 | 0.383207 | 0.917303 |
|
LarryAIDraw/KasumiMiwa003 | LarryAIDraw | "2023-10-08T09:53:22Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2023-10-08T09:49:46Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/157587/kasumi-miwa-jujutsu-kaisen-lora |
versae/stt_nn-NO_conformer_transducer_large | versae | "2022-11-07T17:57:43Z" | 4 | 0 | nemo | [
"nemo",
"region:us"
] | null | "2022-11-07T17:51:45Z" | Colab → https://colab.research.google.com/drive/1ggqsd5tu6cKf22EiKckbUNTJOwMMqKAh?usp=sharing |
farooqkhan2840503/gemma-Code-Instruct-Finetune-test | farooqkhan2840503 | "2024-03-01T03:54:35Z" | 115 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-01T03:50:46Z" | ---
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.
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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
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<!-- Relevant interpretability work for the model goes here -->
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<!-- 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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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|
trl-lib/OpenHermes-2-Mistral-7B-sigmoid-beta-0.9-steps-200 | trl-lib | "2023-12-20T14:55:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"en",
"arxiv:1910.09700",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | null | "2023-12-20T14:54:52Z" | ---
library_name: peft
base_model: teknium/OpenHermes-2.5-Mistral-7B
model-index:
- name: OpenHermes-2-Mistral-7B-sigmoid-beta-0.9-steps-200
results: []
license: apache-2.0
language:
- en
---
# 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]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
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<!-- 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
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[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
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[More Information Needed]
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#### 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]
## 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:**
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**APA:**
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## Glossary [optional]
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### Framework versions
- PEFT 0.7.1 |
Bharatdeep-H/stella_finetuned_en_dataset_to_mine_negatives_from | Bharatdeep-H | "2025-03-01T09:12:40Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"new",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:164619",
"loss:TripletLoss",
"custom_code",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:NovaSearch/stella_en_400M_v5",
"base_model:finetune:NovaSearch/stella_en_400M_v5",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2025-03-01T09:09:51Z" | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:164619
- loss:TripletLoss
base_model: NovaSearch/stella_en_400M_v5
widget:
- source_sentence: 'Casado''s house bookshelf is so authentic Like your college degrees.
TURNER amazon.es Now Contil Hello Choose your address All Best Sellers Amazon
Basics Deals Latest News ELECTRONICS Mobile phones and telephony Photography and
video camera Electronics Photography and camcorders Accessories Photo studio [USER]
UNDE Audio and H TV, video and Home Cinema photographic backgrounds Funds library
for suckers that they want to pretend in a video call Visit the Store Price: €19.99
Y Of 586 FREE returns'
sentences:
- Quotes show Democrats supported riots “when BLM was BURNING down cities and killing
people in the streets!”
- This is how C5N lies with fake news fakenews cases of COVID Plaza de Mayo Argerich
- Pablo Casado has a fake library to appear on video calls
- source_sentence: 'Kendall Jenner. #BlackLivesMatter BLACK LIVES MATTER 79 GRID WILD'
sentences:
- Photo shows basketball legend Kobe Bryant’s body
- Kendall Jenner posted a photoshopped picture holding a "Black Lives Matter" sign
- Video shows the arrest of US military officer by Russian forces in 2022.
- source_sentence: 4 severe level one, fatigue Headache loss of smell -Cough Fever
- hoarseness Chest pain -Fatigue
sentences:
- THE INGREDIENTS OF THE VACCINES REVEALED
- The CROWN VIRUS from Wuhan. can be cured* with a bowl of freshly boiled garlic
water
- There are 6 "types" of COVID-19
- source_sentence: 'HUEN Airport Entebbe International Airport (IATA: EBB, ICAO: HUEN)
is the principal international airport of Uganda. ... It is the only international
airport of Uganda. Built: 1972-1973 (main terminal building) Location: Entebbe,
Uganda Hub for: Eagle Air; Uganda Airlines'
sentences:
- Uganda’s new police spokesman shoots catapult at journalist
- Uganda’s Entebbe Airport changes name to HUEN Airport
- Tweets from the Israeli prime minister’s official Twitter account show the country
was responsible for the Beirut explosion
- source_sentence: 'day I was acquitted 12/12/12 i hocus45th GP SERVICES USA CDC CENTERS
FOR DISH CONTROL AND P EXCLUSIVE: Per the CDC There Are Nearly Twice As Many Vaccine
Related Deaths SO FAR in 2021 (1,755) Than All the Vaccine Deaths this Past Decade
(994) For information about vaccines. visit who.int.'
sentences:
- New Zealand PM links booster dose to six months of freedom
- Side effects of the first published vaccine According to Pfizer documents, 1,200
deaths.
- “Thousands of COVID Vaccine Injuries and 13 U.S. Deaths Reported in December Alone”;
“In December, 3,916 COVID vaccine-related adverse events, including 13 deaths,
were reported to VAERS”
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on NovaSearch/stella_en_400M_v5
results:
- task:
type: triplet
name: Triplet
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.9713522050783623
name: Cosine Accuracy
---
# SentenceTransformer based on NovaSearch/stella_en_400M_v5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NovaSearch/stella_en_400M_v5](https://huggingface.co/NovaSearch/stella_en_400M_v5) on the csv dataset. It maps sentences & paragraphs to a 1024-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:** [NovaSearch/stella_en_400M_v5](https://huggingface.co/NovaSearch/stella_en_400M_v5) <!-- at revision 32b4baf84d02a1b1beb2df8952e875232e8ebe1d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **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: NewModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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): Dense({'in_features': 1024, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## 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("Bharatdeep-H/stella_finetuned_en_dataset_to_mine_negatives_from")
# Run inference
sentences = [
'day I was acquitted 12/12/12 i hocus45th GP SERVICES USA CDC CENTERS FOR DISH CONTROL AND P EXCLUSIVE: Per the CDC There Are Nearly Twice As Many Vaccine Related Deaths SO FAR in 2021 (1,755) Than All the Vaccine Deaths this Past Decade (994) For information about vaccines. visit who.int.',
'“Thousands of COVID Vaccine Injuries and 13 U.S. Deaths Reported in December Alone”; “In December, 3,916 COVID vaccine-related adverse events, including 13 deaths, were reported to VAERS”',
'Side effects of the first published vaccine According to Pfizer documents, 1,200 deaths.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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
#### Triplet
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9714** |
<!--
## 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
#### csv
* Dataset: csv
* Size: 164,619 training samples
* Columns: <code>query</code>, <code>answer</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 83.66 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.17 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.69 tokens</li><li>max: 120 tokens</li></ul> |
* Samples:
| query | answer | negative |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>YEAST EXTRACK C DDees.co NO AFTER 40 YEARS OF RESEARCH, THERE IS A VACCINE AGAINST AIDS AFTER 76 YEARS OF RESEARCH, NO THERE IS A VACCINE AGAINST INFLUENZA AFTER 100 YEARS OF RESEARCH, NO THERE IS A VACCINE AGAINST CANCER BUT AFTER ONLY 6 MONTHS THERE IS A VACCINE AGAINST A "VIRUS" THAT APPEARED "FROM SUDDEN" AND THOSE WHO</code> | <code>After years of research there is no vaccine against AIDS, cancer and influenza, but in 6 months there is a vaccine against covid-19</code> | <code>Chronology of decline in vaccine effectiveness shows that immunizers are not effective</code> |
| <code>Donald J. Trump [USER] Trump We have reports of NIGERIA supporting Iran ACTIVELY and PASSIVELY....just got of the phone with secretary Pompeo....the USA MUST and WILL respond to terrorist nations IMMEDIATELY! 12:11 AM - 08 Jan 2020 244,391 Retweets 319,284 Likes 167K [244K Follow 319K</code> | <code>US President Donald Trump threatens to attack Nigeria</code> | <code>Vice President of the United States Mike Pence unfollowed President Donald Trump on Twitter and put a picture of Joe Biden and Kamala Harris on the profile cover</code> |
| <code>4 k 40 years worth of research... *no vaccine for HIV cancer MA At least 100 years of research...no vaccine for Ongoing research... no vaccine for the common cold Less than a year for a Covid vaccine? I think I'll pass on that shot!! PIC COLLAGE</code> | <code>Vaccines for HIV, cold and cancer should deter you from getting the Covid-19 vaccine</code> | <code>Anticovid vaccines cause deterioration of the immune system and AIDS</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Evaluation Dataset
#### csv
* Dataset: csv
* Size: 164,619 evaluation samples
* Columns: <code>query</code>, <code>answer</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 73.2 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.27 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.3 tokens</li><li>max: 179 tokens</li></ul> |
* Samples:
| query | answer | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|
| <code>--- Nora Al Riyadh Tik Tok Replying to [USER]</code> | <code>A restaurant in Riyadh in one of the malls, the opening of the meal is free, of course, Corona in the farthest corner is surprising</code> | <code>Video of Rakhi Sawant wearing a hijab to support protestors in Karnataka</code> |
| <code>SQUID FOR BRAZIL BELTER SALT Corumbau pasil Milk</code> | <code>Lula expelled from the city of Itanagrà in Bahia in May 2021. The Army had to provide security.</code> | <code>All workers in Gardenia Philippines bread factory COVID-19 positive in July 2020</code> |
| <code>I just ran out of words William Barr, Attorney General of the America literally most important person of all the American court system just publicly denounced that there has been electoral fraud 2rad10 TM [USER].6h US Attorney General William Barr denounces Vote-by-mail fraud. OM BLITZER [USER] THE WITH THE WITH SITUATION WOLFOOTION WOLF S BLITZE ROOM OUTZER TH HE WITH DERNIE CNN EXCLUSIVE Jimmy Carter & James Baker WOLF ONE-ON-ONE WITH ATTORNEY GENERAL WILLIAM BARR CAN WELL DEEST</code> | <code>The US attorney general denounces that there has been electoral fraud</code> | <code>Hillary Clinton appeared before the US justice on June 2, 2020</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 2
- `learning_rate`: 3e-05
- `max_steps`: 4000
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.2
- `bf16`: True
- `batch_sampler`: no_duplicates
#### 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`: 2
- `per_device_eval_batch_size`: 8
- `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`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3.0
- `max_steps`: 4000
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `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`: True
- `fp16`: False
- `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}
- `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
- `dispatch_batches`: None
- `split_batches`: 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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:---------------:|
| 0.0016 | 100 | 1.6958 | - | - |
| 0.0032 | 200 | 1.3647 | - | - |
| 0.0049 | 300 | 1.1698 | - | - |
| 0.0065 | 400 | 0.8551 | - | - |
| 0.0081 | 500 | 0.8275 | - | - |
| 0.0097 | 600 | 0.8878 | - | - |
| 0.0113 | 700 | 0.9717 | - | - |
| 0.0130 | 800 | 1.0219 | - | - |
| 0.0146 | 900 | 0.9074 | - | - |
| 0.0162 | 1000 | 0.903 | 0.8201 | 0.9452 |
| 0.0178 | 1100 | 0.9236 | - | - |
| 0.0194 | 1200 | 0.7935 | - | - |
| 0.0211 | 1300 | 1.0483 | - | - |
| 0.0227 | 1400 | 1.0878 | - | - |
| 0.0243 | 1500 | 0.9258 | - | - |
| 0.0259 | 1600 | 1.011 | - | - |
| 0.0275 | 1700 | 0.7785 | - | - |
| 0.0292 | 1800 | 0.7643 | - | - |
| 0.0308 | 1900 | 0.9918 | - | - |
| 0.0324 | 2000 | 0.7941 | 0.7678 | 0.9387 |
| 0.0340 | 2100 | 1.106 | - | - |
| 0.0356 | 2200 | 0.7571 | - | - |
| 0.0373 | 2300 | 0.6687 | - | - |
| 0.0389 | 2400 | 0.6914 | - | - |
| 0.0405 | 2500 | 0.5925 | - | - |
| 0.0421 | 2600 | 0.8085 | - | - |
| 0.0437 | 2700 | 0.5775 | - | - |
| 0.0454 | 2800 | 0.5051 | - | - |
| 0.0470 | 2900 | 0.6894 | - | - |
| 0.0486 | 3000 | 0.4202 | 0.4875 | 0.9667 |
| 0.0502 | 3100 | 0.4704 | - | - |
| 0.0518 | 3200 | 0.4511 | - | - |
| 0.0535 | 3300 | 0.3991 | - | - |
| 0.0551 | 3400 | 0.4166 | - | - |
| 0.0567 | 3500 | 0.3402 | - | - |
| 0.0583 | 3600 | 0.6621 | - | - |
| 0.0599 | 3700 | 0.5999 | - | - |
| 0.0616 | 3800 | 0.443 | - | - |
| 0.0632 | 3900 | 0.6503 | - | - |
| 0.0648 | 4000 | 0.42 | 0.4156 | 0.9714 |
### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 3.3.1
- Transformers: 4.49.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
## 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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ying-zh/Reinforce-Pixelcopter-PLE-v0 | ying-zh | "2023-05-24T18:27:51Z" | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-05-23T14:37:35Z" | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 32.10 +/- 22.58
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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
|
google/metricx-23-qe-xxl-v2p0 | google | "2025-01-07T21:10:24Z" | 945 | 6 | transformers | [
"transformers",
"pytorch",
"mt5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-02-07T16:34:57Z" | ---
license: apache-2.0
---
# MetricX-23
*This is not an officially supported Google product.*
**GitHub repository: [https://github.com/google-research/metricx](https://github.com/google-research/metricx)**
This repository contains the MetricX-23 models,
a family of models for automatic evaluation of translations that were proposed
in the WMT'23 Metrics Shared Task submission
[MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.63/).
The models were trained in [T5X](https://github.com/google-research/t5x) and
then converted for use in PyTorch.
## Available Models
There are 6 models available on HuggingFace that vary in the number of
parameters and whether or not the model is reference-based or reference-free
(also known as quality estimation, or QE):
* [MetricX-23-XXL](https://huggingface.co/google/metricx-23-xxl-v2p0)
* [MetricX-23-XL](https://huggingface.co/google/metricx-23-xl-v2p0)
* [MetricX-23-Large](https://huggingface.co/google/metricx-23-large-v2p0)
* [MetricX-23-QE-XXL](https://huggingface.co/google/metricx-23-qe-xxl-v2p0)
* [MetricX-23-QE-XL](https://huggingface.co/google/metricx-23-qe-xl-v2p0)
* [MetricX-23-QE-Large](https://huggingface.co/google/metricx-23-qe-large-v2p0)
We recommend using the XXL model versions for the best agreement with human
judgments of translation quality, the Large versions for best speed, and the
XL for an intermediate use case.
## Changes to the WMT'23 Submission
These models available here are most similar to the primary submission to the WMT'23 Metrics
Shared Task. They are initialized with [mT5](https://aclanthology.org/2021.naacl-main.41/)
then fine-tuned on a combination of direct assessment and MQM data. However,
we made some changes that make these models different from the WMT'23 submissions.
First, the models are trained to regress the actual MQM score rather than a
normalized score between 0 and 1. **That means the output from the MetricX-23
models is a score in the range [0, 25] where lower is better (i.e., it predicts
an error score).**
Second, these models were trained with a larger variety of synthetic data that
makes them more robust to translation edge cases like over- and undertranslation,
described in more detail in the following section.
### Synthetic Data
In order for our MetricX models to learn to identify certain types of bad
translations that are not sufficiently (or at all) represented in the regular
training data, we created synthetic examples and mixed them in during training.
The synthetic training data was generated from the DA datasets ranging from
WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have
the candidate translation manipulated so as to turn it into a bad translation
with a specific issue commonly unrecognized by learned metrics.
The table below provides an overview of the various failure modes that we
considered, including brief descriptions of how we prepared the synthetic data
to address them.
| Failure mode | Synthetic example description |
| ----------- | ----------- |
| Undertranslation | Candidate translation with an arbitrary sentence removed (if multi-sentence); alternatively, candidate with a certain proportion of words removed from the end. |
| Overtranslation | Candidate translation duplicated (with space in between). |
| Fluent but unrelated translation | Arbitrary reference of a similar length from the dataset. |
| Gibberish | Text of a similar length as the reference, generated by sampling words from the reference translation vocabulary (built from all references in the data). |
| Missing punctuation | Reference translation with the end punctuation removed (11 punctuation symbols considered). |
| Latin instead of Chinese/Japanese or Hindi/Bengali punctuation | Candidate translation with the language-specific punctuation symbol at the end replaced with the Latin equivalent (e.g., "." instead of "。" or "।"); alternatively, the punctuation symbol is replaced with the Latin equivalent in the reference, keeping the correct one in the candidate. |
| Reference-matching translation | Reference translation copied as the candidate translation (unlike the rest of the synthetic data, these examples are meant to train the metric to predict a perfect score for candidates matching the reference). |
Examples from the first 4 categories were assigned a label corresponding to the
worst score on the given rating scale (e.g., 25 when mixed with MQM training
data), whereas the reference-matching translation examples are assigned the best
score (e.g., 0 when used with MQM data). The missing/incorrect punctuation
examples were labeled with a score slightly worse than perfect.
Note that some of the synthetic datasets are only meaningful in the
reference-based scenario, and we thus excluded them when training a QE variant
of MetricX. These are the Latin-vs-special punctuation and the
reference-matching translation examples.
Most of the synthetic training sets were created using stratified sampling
across target languages, taking 500 examples per target language. One exception
is the missing punctuation set, which used a stratified sample across different
punctuation symbols instead.
When training MetricX, a small proportion of the synthetic examples was mixed
with the regular training examples. During the first-stage fine-tuning on DA
data, each synthetic training set constituted between 0.1% and 1% of all
training examples, whereas in the second-stage fine-tuning on MQM data we used
an even smaller proportion, around 0.05%.
As for evaluating the effect of the synthetic training data on the model's
performance, the DEMETR challenge set - which we originally used to evaluate the
models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We
therefore created a new DEMETR-style test set based on the WMT22 DA data, with
examples constructed analogically to the synthetic training examples, as
described above. This test set helped us determine the right proportions of
synthetic data for fine-tuning in order to make MetricX robust for the failure
modes in consideration, without sacrificing the system- and segment-level
correlations with human ratings.
## Usage
The code for using MetricX models can be found at [https://github.com/google-research/metricx](https://github.com/google-research/metricx).
The repository contains example prediction scripts, described below.
The `metricx23/predict.py` script contains an example for how to run inference
on the models.
### Reference-Based
Example usage for a reference-based model:
```bash
python -m metricx23.predict \
--tokenizer google/mt5-xl \
--model_name_or_path google/metricx-23-xl-v2p0 \
--max_input_length 1024 \
--batch_size 1 \
--input_file input.jsonl \
--output_file output.jsonl
```
`input.jsonl` is expected to have 1 serialized JSON object per line with
`"reference"` and `"hypothesis"` fields. The output jsonl will be parallel
to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score.
Note that the model was trained with a maximum input length of 1024 tokens, so
significantly increasing that value may lead to unpredictable behavior.
### Reference-Free
Example usage for a reference-free model:
```bash
python -m metricx23.predict \
--tokenizer google/mt5-xl \
--model_name_or_path google/metricx-23-qe-xl-v2p0 \
--max_input_length 1024 \
--batch_size 1 \
--input_file input.jsonl \
--output_file output.jsonl \
--qe
```
`input.jsonl` is expected to have 1 serialized JSON object per line with
`"source"` and `"hypothesis"` fields. The output jsonl will be parallel
to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score.
## Meta-Evaluation
The `metricx23/evaluate.py` script contains code to calculate various correlations
between the MetricX-23 scores and MQM ratings of translation quality using the
[MT Metrics Eval](https://github.com/google-research/mt-metrics-eval) library.
Example usage:
```bash
python -m metricx23.evaluate \
--dataset wmt22 \
--lp en-de \
--input_file input.jsonl \
--output_file output.json
```
`input.jsonl` is expected to have one JSON object serialized per line.
Each JSON object is expected to contain 4 fields:
* `"system_id"`: The name of the system that generated the translation.
* `"segment_id"`: The 0-based index of the corresponding segment in the MT
Metrics Eval data.
* `"label"`: The ground-truth translation quality score (with higher is better).
* `"prediction"`: The model predicted translation quality score (with lower is
better; the script negates the scores so higher is better).
The script will calculate the 4 agreement/correlations that were used in the
WMT'23 Shared Task. Below are the results for the MetricX-23 models on the
WMT'22 Metrics Shared Task data:
English-German:
| Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc |
| ----------- | ----------- | ----------- | ----------- | ----------- |
| MetricX-23-XXL | 0.795 | 0.835 | 0.546 | 0.619 |
| MetricX-23-XL | 0.756 | 0.813 | 0.540 | 0.605 |
| MetricX-23-Large | 0.769 | 0.759 | 0.507 | 0.595 |
| MetricX-23-QE-XXL | 0.769 | 0.830 | 0.490 | 0.606 |
| MetricX-23-QE-XL | 0.718 | 0.684 | 0.421 | 0.594 |
| MetricX-23-QE-Large | 0.744 | 0.671 | 0.387 | 0.579 |
English-Russian:
| Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc |
| ----------- | ----------- | ----------- | ----------- | ----------- |
| MetricX-23-XXL | 0.905 | 0.943 | 0.477 | 0.609 |
| MetricX-23-XL | 0.876 | 0.906 | 0.498 | 0.589 |
| MetricX-23-Large | 0.876 | 0.841 | 0.474 | 0.569 |
| MetricX-23-QE-XXL | 0.895 | 0.940 | 0.470 | 0.602 |
| MetricX-23-QE-XL | 0.848 | 0.861 | 0.415 | 0.570 |
| MetricX-23-QE-Large | 0.819 | 0.778 | 0.411 | 0.551 |
Chinese-English:
| Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc |
| ----------- | ----------- | ----------- | ----------- | ----------- |
| MetricX-23-XXL | 0.868 | 0.919 | 0.605 | 0.551 |
| MetricX-23-XL | 0.868 | 0.924 | 0.584 | 0.543 |
| MetricX-23-Large | 0.857 | 0.919 | 0.555 | 0.539 |
| MetricX-23-QE-XXL | 0.857 | 0.928 | 0.573 | 0.544 |
| MetricX-23-QE-XL | 0.802 | 0.879 | 0.546 | 0.529 |
| MetricX-23-QE-Large | 0.758 | 0.904 | 0.522 | 0.529 |
The `metricx23/evaluate_wmt23.py` script re-calculates the average correlation
score that was used to rank submissions from the
[WMT'23 Shared Task](https://www2.statmt.org/wmt23/pdf/2023.wmt-1.51.pdf).
Example usage:
```bash
python -m metricx23.evaluate_wmt23 \
--en_de predictions_ende.jsonl \
--he_en predictions_heen.jsonl \
--zh_en predictions_zhen.jsonl \
--output_file output.json
```
Each of the 3 input files is expected to be in the same format as described
above. Each file should correspond to running inference on each of the language
pairs from the WMT'23 dataset.
The results for each of the models is the following:
| Model | Average Correlation |
| ----------- | ----------- |
| MetricX-23-XXL | 0.812 |
| MetricX-23-XL | 0.813 |
| MetricX-23-Large | 0.794 |
| MetricX-23-QE-XXL | 0.797 |
| MetricX-23-QE-XL | 0.767 |
| MetricX-23-QE-Large | 0.762 |
## Citation
If you use MetricX-23 in your research, please cite the following publication:
```bibtex
@inproceedings{juraska-etal-2023-metricx,
title = {{MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task}},
author = "Juraska, Juraj and
Finkelstein, Mara and
Deutsch, Daniel and
Siddhant, Aditya and
Mirzazadeh, Mehdi and
Freitag, Markus",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.63",
doi = "10.18653/v1/2023.wmt-1.63",
pages = "756--767",
}
``` |
robiulawaldev/758c31a0-f3c8-433d-9a8f-82c05f8afe75 | robiulawaldev | "2025-03-01T05:01:32Z" | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:unsloth/codegemma-7b",
"base_model:adapter:unsloth/codegemma-7b",
"region:us"
] | null | "2025-03-01T05:01:15Z" | ---
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/codegemma-7b
model-index:
- name: robiulawaldev/758c31a0-f3c8-433d-9a8f-82c05f8afe75
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. -->
# robiulawaldev/758c31a0-f3c8-433d-9a8f-82c05f8afe75
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0172
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
LucasMagnana/Pictalk_distil | LucasMagnana | "2024-04-19T18:35:09Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-01-25T11:45:06Z" | ---
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]
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|
aidonuts/ancient-disco-31-ep1 | aidonuts | "2024-02-28T02:44:45Z" | 92 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-02-28T02:42:16Z" | ---
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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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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GrennKren/Arcee-Blitz-4bit | GrennKren | "2025-02-21T04:51:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2025-02-21T04:47:58Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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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[More Information Needed]
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shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5 | shidowake | "2024-04-02T14:10:23Z" | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-02T14:04:16Z" | ---
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. -->
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[More Information Needed]
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|
jiayueyuan/filter-class | jiayueyuan | "2023-11-10T07:32:00Z" | 0 | 1 | null | [
"biology",
"zh",
"license:apache-2.0",
"region:us"
] | null | "2023-11-10T07:30:19Z" | ---
license: apache-2.0
language:
- zh
tags:
- biology
--- |
TTNVXX/BokehOrNot | TTNVXX | "2024-03-05T11:52:00Z" | 7 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"dataset:BokehOrNot/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-03-05T11:51:28Z" |
---
tags:
- autotrain
- image-classification
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
datasets:
- BokehOrNot/autotrain-data
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metricsg
loss: 0.3941328525543213
f1_macro: 0.8130457113507962
f1_micro: 0.8355263157894737
f1_weighted: 0.8288865461033169
precision_macro: 0.8533012943450432
precision_micro: 0.8355263157894737
precision_weighted: 0.8434833671575431
recall_macro: 0.8000841750841751
recall_micro: 0.8355263157894737
recall_weighted: 0.8355263157894737
accuracy: 0.8355263157894737
|
Sunbird/translate-nllb-1.3b-salt | Sunbird | "2024-11-06T23:01:34Z" | 5,450 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"m2m_100",
"text2text-generation",
"dataset:Sunbird/salt",
"base_model:facebook/nllb-200-1.3B",
"base_model:finetune:facebook/nllb-200-1.3B",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-04-25T16:43:55Z" | ---
base_model: facebook/nllb-200-1.3B
model-index:
- name: translate-nllb-1.3b-salt
results: []
datasets:
- Sunbird/salt
---
<!-- 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. -->
# Model details
This machine translation model can convert single sentences from and to any combination of the following languages:
| ISO 693-3 | Language name |
| --- | --- |
| eng | English |
| ach | Acholi |
| lgg | Lugbara |
| lug | Luganda |
| nyn | Runyankole |
| teo | Ateso |
It was trained on the [SALT](http://huggingface.co/datasets/Sunbird/salt) dataset and a variety of
additional external data resources, including back-translated news articles, FLORES-200, MT560 and LAFAND-MT.
The base model was [facebok/nllb-200-1.3B](https://huggingface.co/facebook/nllb-200-1.3B),
with tokens adapted to add support for languages not originally included.
# Usage example
```python
tokenizer = transformers.NllbTokenizer.from_pretrained(
'Sunbird/translate-nllb-1.3b-salt')
model = transformers.M2M100ForConditionalGeneration.from_pretrained(
'Sunbird/translate-nllb-1.3b-salt')
text = 'Where is the hospital?'
source_language = 'eng'
target_language = 'lug'
language_tokens = {
'eng': 256047,
'ach': 256111,
'lgg': 256008,
'lug': 256110,
'nyn': 256002,
'teo': 256006,
}
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
inputs = tokenizer(text, return_tensors="pt").to(device)
inputs['input_ids'][0][0] = language_tokens[source_language]
translated_tokens = model.to(device).generate(
**inputs,
forced_bos_token_id=language_tokens[target_language],
max_length=100,
num_beams=5,
)
result = tokenizer.batch_decode(
translated_tokens, skip_special_tokens=True)[0]
# Eddwaliro liri ludda wa?
```
# Evaluation metrics
Results on salt-dev:
| Source language | Target language | BLEU |
| --- | --- | --- |
| ach | eng | 28.371 |
| lgg | eng | 30.45 |
| lug | eng | 41.978 |
| nyn | eng |32.296 |
| teo | eng | 30.422 |
| eng | ach | 20.972 |
| eng | lgg | 22.362 |
| eng | lug | 30.359 |
| eng | nyn | 15.305 |
| eng | teo | 21.391 | |
jvadlamudi2/convnext-tiny-224-jvadlamudi2 | jvadlamudi2 | "2023-07-24T18:05:38Z" | 193 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:facebook/convnext-tiny-224",
"base_model:finetune:facebook/convnext-tiny-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2023-07-24T17:51:37Z" | ---
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-jvadlamudi2
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. -->
# convnext-tiny-224-jvadlamudi2
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5780
- Accuracy: 0.7946
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 0.5882 | 0.8036 |
| 0.6213 | 2.0 | 14 | 0.5821 | 0.7857 |
| 0.6123 | 3.0 | 21 | 0.5780 | 0.7946 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|
r1char9/rubert-tiny2-ru-go-emotions | r1char9 | "2024-06-14T06:58:31Z" | 110 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"sentiment-analysis",
"multi-label-classification",
"sentiment analysis",
"rubert",
"sentiment",
"tiny",
"russian",
"multilabel",
"classification",
"emotion-classification",
"emotion-recognition",
"emotion",
"ru",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-02-13T16:58:39Z" | ---
license: mit
language:
- ru
pipeline_tag: text-classification
tags:
- sentiment-analysis
- multi-label-classification
- sentiment analysis
- rubert
- sentiment
- bert
- tiny
- russian
- multilabel
- classification
- emotion-classification
- emotion-recognition
- emotion
---
Модель [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) была fine-tuned для задачи __emotion classification__, предназначенная для __Russian__ текст.
Выполняет задачу __multi-label classification__ с слудующимим категориями:
```yaml
0: admiration
1: amusement
2: anger
3: annoyance
4: approval
5: caring
6: confusion
7: curiosity
8: desire
9: disappointment
10: disapproval
11: disgust
12: embarrassment
13: excitement
14: fear
15: gratitude
16: grief
17: joy
18: love
19: nervousness
20: optimism
21: pride
22: realization
23: relief
24: remorse
25: sadness
26: surprise
27: neutral
```
Категории для русского языка:
```yaml
admiration: восхищение
amusement: веселье
anger: злость
annoyance: раздражение
approval: одобрение
caring: забота
confusion: непонимание
curiosity: любопытство
desire: желание
disappointment: разочарование
disapproval: неодобрение
disgust: отвращение
embarrassment: смущение
excitement: возбуждение
fear: страх
gratitude: признательность
grief: горе
joy: радость
love: любовь
nervousness: нервозность
optimism: оптимизм
pride: гордость
realization: осознание
relief: облегчение
remorse: раскаяние
sadness: грусть
surprise: удивление
neutral: нейтральность
```
## Usage
```python
from transformers import pipeline
model = pipeline(model="r1char9/rubert-tiny2-ru-go-emotions")
model("Привет, ты мне нравишься!")
# [{'label': 'love', 'score': 0.5955629944801331}]
``` |
Triangle104/ADELIE-DPO-Q6_K-GGUF | Triangle104 | "2024-11-24T22:34:32Z" | 7 | 1 | null | [
"gguf",
"text-generation-inference",
"Information Extraction",
"IE",
"Named Entity Recogniton",
"Event Extraction",
"Relation Extraction",
"LLaMA",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:ACE05",
"dataset:conll2003",
"dataset:conll2012_ontonotesv5",
"dataset:rams",
"dataset:tacred",
"dataset:fewrel",
"dataset:maven",
"base_model:THU-KEG/ADELIE-DPO",
"base_model:quantized:THU-KEG/ADELIE-DPO",
"license:llama2",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-24T22:33:38Z" | ---
license: llama2
datasets:
- ACE05
- conll2003
- conll2012_ontonotesv5
- rams
- tacred
- fewrel
- maven
language:
- en
metrics:
- f1
pipeline_tag: text-generation
tags:
- text-generation-inference
- Information Extraction
- IE
- Named Entity Recogniton
- Event Extraction
- Relation Extraction
- LLaMA
- llama-cpp
- gguf-my-repo
base_model: THU-KEG/ADELIE-DPO
---
# Triangle104/ADELIE-DPO-Q6_K-GGUF
This model was converted to GGUF format from [`THU-KEG/ADELIE-DPO`](https://huggingface.co/THU-KEG/ADELIE-DPO) 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/THU-KEG/ADELIE-DPO) for more details on the model.
---
Model details:
-
We introduce ADELIE (Aligning large language moDELs on Information Extraction), an aligned LLM that effectively solves various IE tasks, including closed IE, open IE, and on-demand IE. We first collect and construct a high-quality alignment corpus IEInstruct for IE. Then we train ADELIESFT using instruction tuning on IEInstruct. We further train ADELIESFT with direct preference optimization (DPO) objective, resulting in ADELIEDPO. Extensive experiments on various held-out IE datasets demonstrate that our models (ADELIESFT and ADELIEDPO) achieve state-of-the-art (SoTA) performance among open-source models. We further explore the general capabilities of ADELIE, and experimental results reveal that their general capabilities do not exhibit a noticeable decline.
📖 Paper: ADELIE: Aligning Large Language Models on Information Extraction
🐧 Github: THU/ADELIE
Model Description
-
Developed by: Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
Model type: Text Generation
Language(s) (NLP): English
License: LLaMA2 License for the base model.
Finetuned from model [optional]: LLaMA2-7B
---
## 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 Triangle104/ADELIE-DPO-Q6_K-GGUF --hf-file adelie-dpo-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/ADELIE-DPO-Q6_K-GGUF --hf-file adelie-dpo-q6_k.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 Triangle104/ADELIE-DPO-Q6_K-GGUF --hf-file adelie-dpo-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/ADELIE-DPO-Q6_K-GGUF --hf-file adelie-dpo-q6_k.gguf -c 2048
```
|
DevQuasar/llama3.2_3b_chat_brainstorm-v3.2.3 | DevQuasar | "2025-02-01T23:04:38Z" | 5 | 0 | null | [
"safetensors",
"llama",
"license:llama3.2",
"region:us"
] | null | "2024-11-09T14:54:21Z" | ---
license: llama3.2
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
'Make knowledge free for everyone'
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
TechxGenus/CursorCore-QW2.5-1.5B-SR | TechxGenus | "2024-10-10T06:43:22Z" | 130 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"conversational",
"arxiv:2410.07002",
"base_model:Qwen/Qwen2.5-Coder-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Coder-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-08T04:06:35Z" | ---
tags:
- code
base_model:
- Qwen/Qwen2.5-Coder-1.5B
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
---
# CursorCore: Assist Programming through Aligning Anything
<p align="center">
<a href="http://arxiv.org/abs/2410.07002">[📄arXiv]</a> |
<a href="https://hf.co/papers/2410.07002">[🤗HF Paper]</a> |
<a href="https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2">[🤖Models]</a> |
<a href="https://github.com/TechxGenus/CursorCore">[🛠️Code]</a> |
<a href="https://github.com/TechxGenus/CursorWeb">[Web]</a> |
<a href="https://discord.gg/Z5Tev8fV">[Discord]</a>
</p>
<hr>
- [CursorCore: Assist Programming through Aligning Anything](#cursorcore-assist-programming-through-aligning-anything)
- [Introduction](#introduction)
- [Models](#models)
- [Usage](#usage)
- [1) Normal chat](#1-normal-chat)
- [2) Assistant-Conversation](#2-assistant-conversation)
- [3) Web Demo](#3-web-demo)
- [Future Work](#future-work)
- [Citation](#citation)
- [Contribution](#contribution)
<hr>
## Introduction
CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read [our paper](http://arxiv.org/abs/2410.07002) to learn more.
<p align="center">
<img width="100%" alt="conversation" src="https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/conversation.png">
</p>
## Models
Our models have been open-sourced on Hugging Face. You can access our models here: [CursorCore-Series](https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2"). We also provide pre-quantized weights for GPTQ and AWQ here: [CursorCore-Quantization](https://huggingface.co/collections/TechxGenus/cursorcore-quantization-67066431f29f252494ee8cf3)
## Usage
Here are some examples of how to use our model:
### 1) Normal chat
Script:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-9B",
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "user", "content": "Hi!"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512)
print(tokenizer.decode(outputs[0]))
````
Output:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>user
Hi!<|im_end|>
<|im_start|>assistant
Hello! I'm an AI language model and I can help you with any programming questions you might have. What specific problem or task are you trying to solve?<|im_end|>
````
### 2) Assistant-Conversation
In our work, we introduce a new framework of AI-assisted programming task. It is designed for aligning anything during programming process, used for the implementation of features like Tab and Inline Chat.
Script 1:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval.utils import prepare_input_for_wf
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-9B",
torch_dtype=torch.bfloat16,
device_map="auto"
)
sample = {
"history": [
{
"type": "code",
"lang": "python",
"code": """def quick_sort(arr):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
}
],
"current": {
"type": "code",
"lang": "python",
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
},
"user": ""
}
prompt = tokenizer.apply_chat_template(
prepare_input_for_wf(sample),
tokenize=False,
chat_template="assistant-conversation",
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0]))
````
Output 1:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>history
```python
def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>current
```python
def quick_sort(array):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>assistant
<|next_start|>```python
def quick_sort(array):
if len(array) <= 1:
return array
pivot = array[len(array) // 2]
left = [x for x in array if x < pivot]
middle = [x for x in array if x == pivot]
right = [x for x in array if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|next_end|>
The user has provided a revised code change that corrects the parameter name from `arr` to `array` in the `quick_sort` function. This change ensures consistency in the function definition and avoids potential confusion or errors.
To implement this, we will:
1. Update the parameter name in the function definition from `arr` to `array`.
2. Ensure that all references to `arr` within the function are updated to `array`.
This will make the function definition and internal references consistent, improving code readability and maintainability.<|im_end|>
````
Script 2:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval.utils import prepare_input_for_wf
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-9B",
torch_dtype=torch.bfloat16,
device_map="auto"
)
sample = {
"history": [],
"current": {
"type": "code",
"lang": "python",
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
},
"user": "Add Docstring."
}
prompt = tokenizer.apply_chat_template(
prepare_input_for_wf(sample),
tokenize=False,
chat_template="assistant-conversation",
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0]))
````
Output 2:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>current
```python
def quick_sort(array):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>user
Add Docstring.<|im_end|>
<|im_start|>assistant
<|next_start|>```python
def quick_sort(array):
"""
This is an implementation of the quick sort algorithm.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|next_end|><|im_end|>
````
For models in Locate-and-Change (LC) and Search-and-Replace (SR) formats, the output examples are as follows:
Script for LC:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval.utils import prepare_input_for_lc
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-LC")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-1.5B-LC",
torch_dtype=torch.bfloat16,
device_map="auto"
)
sample = {
"history": [],
"current": {
"type": "code",
"lang": "python",
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
},
"user": "Add Docstring."
}
prompt = tokenizer.apply_chat_template(
prepare_input_for_lc(sample),
tokenize=False,
chat_template="assistant-conversation",
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0]))
````
Output for LC:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>current
```python
1 def quick_sort(array):
2 if len(arr) <= 1:
3 return arr
4 pivot = arr[len(arr) // 2]
5 left = [x for x in arr if x < pivot]
6 middle = [x for x in arr if x == pivot]
7 right = [x for x in arr if x > pivot]
8 return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>user
Add Docstring.<|im_end|>
<|im_start|>assistant
<|next_start|>1,1
```
'''This function will sort an array using quick sort algorithm'''
```<|next_end|>
To enhance the readability and maintainability of the code, we should add a docstring to the `quick_sort` function. A docstring provides a clear description of what the function does, which is particularly useful for other developers who might use or modify this code in the future.
The docstring will be added immediately after the function definition, explaining that the function uses the quick sort algorithm to sort an array. This will make the code more self-explanatory and easier to understand.
Here's the plan:
1. Add a docstring at the beginning of the `quick_sort` function.
2. Ensure the docstring is clear and concise, describing the purpose of the function.
This modification will improve the code's documentation without altering its functionality.<|im_end|>
````
Script for SR:
````python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from eval.utils import prepare_input_for_sr
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-SR")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CursorCore-Yi-1.5B-SR",
torch_dtype=torch.bfloat16,
device_map="auto"
)
sample = {
"history": [],
"current": {
"type": "code",
"lang": "python",
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
},
"user": "Add Docstring."
}
prompt = tokenizer.apply_chat_template(
prepare_input_for_sr(sample),
tokenize=False,
chat_template="assistant-conversation",
add_generation_prompt=True
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0]))
````
Output for SR:
````txt
<|im_start|>system
You are a helpful programming assistant.<|im_end|>
<|im_start|>current
```python
def quick_sort(array):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
```<|im_end|>
<|im_start|>user
Add Docstring.<|im_end|>
<|im_start|>assistant
<|next_start|>```python
def quick_sort(array):
<|search_and_replace|>
def quick_sort(array):
"""
This function implements quick sort algorithm
"""
```<|next_end|><|im_end|>
````
### 3) Web Demo
We create a web demo for CursorCore. Please visit [CursorWeb](https://github.com/TechxGenus/CursorWeb) for more details.
## Future Work
CursorCore is still in a very early stage, and lots of work is needed to achieve a better user experience. For example:
- Repository-level editing support
- Better and faster editing formats
- Better user interface and presentation
- ...
## Citation
```bibtex
@article{jiang2024cursorcore,
title = {CursorCore: Assist Programming through Aligning Anything},
author = {Hao Jiang and Qi Liu and Rui Li and Shengyu Ye and Shijin Wang},
year = {2024},
journal = {arXiv preprint arXiv: 2410.07002}
}
```
## Contribution
Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.
|
VPTQ-community/Meta-Llama-3.3-70B-Instruct-v8-k65536-0-woft | VPTQ-community | "2025-02-25T17:19:44Z" | 26 | 0 | null | [
"safetensors",
"llama",
"VPTQ",
"Quantized",
"Quantization",
"arxiv:2409.17066",
"base_model:meta-llama/Llama-3.3-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.3-70B-Instruct",
"license:llama3.3",
"vptq",
"region:us"
] | null | "2024-12-15T15:39:57Z" |
---
license: llama3.3
base_model:
- meta-llama/Llama-3.3-70B-Instruct
base_model_relation: quantized
tags:
- VPTQ
- Quantized
- Quantization
---
**Disclaimer**:
The model is reproduced based on the paper *VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models* [github](https://github.com/microsoft/vptq) and [arXiv](https://arxiv.org/abs/2409.17066)
The model itself is sourced from a community release.
It is intended only for experimental purposes.
Users are responsible for any consequences arising from the use of this model.
|
Nhat1904/test_trainer_XLNET_3ep_5e-5 | Nhat1904 | "2022-12-06T03:10:16Z" | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlnet",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-12-06T01:30:37Z" | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer_XLNET_3ep_5e-5
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. -->
# test_trainer_XLNET_3ep_5e-5
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5405
- Accuracy: 0.8773
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7984 | 1.0 | 1125 | 0.6647 | 0.7923 |
| 0.5126 | 2.0 | 2250 | 0.4625 | 0.862 |
| 0.409 | 3.0 | 3375 | 0.5405 | 0.8773 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
SanteriVtj/ppo-SnowballTarget | SanteriVtj | "2025-02-28T18:22:37Z" | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | "2025-02-28T18:22:34Z" | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: SanteriVtj/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
EMahdi/whisper-large-v3-turbo-ar-finetune | EMahdi | "2024-12-04T12:50:33Z" | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"dataset:EMahdi/WhisperFinetune",
"base_model:openai/whisper-large-v3-turbo",
"base_model:finetune:openai/whisper-large-v3-turbo",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-12-04T10:48:45Z" | ---
library_name: transformers
language:
- ar
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- EMahdi/WhisperFinetune
metrics:
- wer
model-index:
- name: Whisper Large V3 Turbo Finetune Ar - EMahdi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: EMahdi/WhisperFinetune Sudanese Corpus
type: EMahdi/WhisperFinetune
args: 'config: sudanese_corpus, split: test'
metrics:
- name: Wer
type: wer
value: 42.80180761781795
---
<!-- 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 Large V3 Turbo Finetune Ar - EMahdi
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the EMahdi/WhisperFinetune Sudanese Corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8721
- Wer: 42.8018
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.2464 | 1.0 | 89 | 0.9025 | 71.2072 |
| 0.7343 | 2.0 | 178 | 0.7835 | 55.7779 |
| 0.5441 | 3.0 | 267 | 0.7463 | 56.3105 |
| 0.4076 | 4.0 | 356 | 0.7532 | 47.5468 |
| 0.325 | 5.0 | 445 | 0.7811 | 51.4526 |
| 0.2635 | 6.0 | 534 | 0.8050 | 62.1369 |
| 0.1866 | 7.0 | 623 | 0.8226 | 45.7715 |
| 0.1171 | 8.0 | 712 | 0.8406 | 45.4810 |
| 0.0679 | 9.0 | 801 | 0.8664 | 43.5119 |
| 0.0399 | 10.0 | 890 | 0.8721 | 42.8018 |
### Framework versions
- Transformers 4.45.0
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
in-diretta-sapna-shah-video-leak/sapna.shah.viral.video.official.tutorial | in-diretta-sapna-shah-video-leak | "2025-03-29T18:42:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-03-29T18:41:32Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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<meta name="twitter:site" content="@huggingface" />
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<title>Hugging Face - The AI community building the future.</title>
<style>
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margin: 0;
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
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color: rgb(209, 213, 219);
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color: rgb(156, 163, 175);
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// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
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document.documentElement.classList.remove("dark");
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</body>
</html> |
DarijaM/XLM-R-Large-Tweet-base | DarijaM | "2025-01-10T22:47:32Z" | 10 | 0 | null | [
"safetensors",
"xlm-roberta",
"license:mit",
"region:us"
] | null | "2025-01-10T16:45:31Z" | ---
license: mit
---
# **XLM-R-Large-Tweet-Base**
**XLM-R-Large-Tweet-Base** is an additionally pretrained version of the [XLM-RoBERTa large-sized model]( https://huggingface.co/FacebookAI/xlm-roberta-large), tailored specifically for the social media domain. The model has been pretrained using 37,200 COVID-19 vaccination-related tweets in the Serbian language (approximately 1.3 million tokens), leveraging the unique linguistic features and informal writing styles prevalent on social media platforms.
Its fine-tuned version for the **five-class sentiment analysis task** is available as [XLM-R-Large-Tweet](https://huggingface.co/DarijaM/XLM-R-Large-Tweet). |
John6666/epicrealism-xl-v9unflux-sdxl | John6666 | "2024-12-23T06:36:38Z" | 5,578 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"photo",
"photography",
"photorealism",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-10-11T13:48:37Z" | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- photo
- photography
- photorealism
---
Original model is [here](https://civitai.com/models/277058?modelVersionId=931522).
This model created by [epinikion](https://civitai.com/user/epinikion).
|
Primeness/primeh6v5c4 | Primeness | "2025-02-05T09:13:24Z" | 22 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-05T07:01:07Z" | ---
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] |
IlyaGusev/mt0_xxl_ru_turbo_alpaca_lora | IlyaGusev | "2023-03-31T18:41:13Z" | 0 | 1 | null | [
"text2text-generation",
"ru",
"dataset:IlyaGusev/ru_turbo_alpaca",
"region:us"
] | text2text-generation | "2023-03-28T21:38:27Z" | ---
datasets:
- IlyaGusev/ru_turbo_alpaca
language:
- ru
pipeline_tag: text2text-generation
inference: false
--- |
oregapam/ioniclora1 | oregapam | "2025-03-26T22:24:08Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-03-26T19:10:41Z" | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ioniclora1
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
---
# ioniclora1
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `ioniclora1` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
GeneralAwareness/VintagePhotos | GeneralAwareness | "2022-12-29T03:26:13Z" | 0 | 6 | null | [
"stable-diffusion",
"v2",
"text-to-image",
"image-to-image",
"Embedding",
"en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | text-to-image | "2022-12-29T03:22:22Z" | ---
license: cc-by-nc-sa-4.0
language:
- en
thumbnail: "https://huggingface.co/GeneralAwareness/VintagePhotos/resolve/main/00122-2365281862-color%20photo%20emma%20stone%20in%20the%20style%20of%20Vint.png"
tags:
- stable-diffusion
- v2
- text-to-image
- image-to-image
- Embedding
---
Textual Inversion Embedding by General Awareness For SD 2.x trained on 768x768 images from various sources.
Install by downloading the .pt embedding, and put it in the \embeddings folder.
The two embeddings are a one two punch as Vint-3000 is more 1880s style of photography (some seeds will be different) while the Vint is more for 1940s onward though both can be used for anything you can dream of.
Use keyword: vint, or vint-3000 depending on the embedding, and effect you are trying to achieve.
color photo morgan freeman in the style of Vint-3000
color photo morgan freeman in the style of Vint
color photo emma stone in the style of Vint
color photo emma stone in the style of Vint-3000
color photo post apocalyptic city in the style of Vint-3000
color photo post apocalyptic city in the style of Vint
 |
husnu/electra-small-turkish-uncased-discriminator | husnu | "2022-01-16T19:01:47Z" | 11 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"endpoints_compatible",
"region:us"
] | question-answering | "2022-03-02T23:29:05Z" | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: ft_electra-small-turkish-uncased-discriminator_lr-2e-1_epochs-1
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. -->
This model is a fine-tuned version of [loodos/electra-small-turkish-uncased-discriminator](https://huggingface.co/loodos/electra-small-turkish-uncased-discriminator) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 5.9506
## 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: 0.2
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.951 | 1.0 | 5818 | 5.9506 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
imdatta0/llama_2_13b_Magiccoder_evol_10k_reverse | imdatta0 | "2024-06-10T17:34:07Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"unsloth",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:adapter:meta-llama/Llama-2-13b-hf",
"license:llama2",
"region:us"
] | null | "2024-06-10T13:59:29Z" | ---
license: llama2
library_name: peft
tags:
- unsloth
- generated_from_trainer
base_model: meta-llama/Llama-2-13b-hf
model-index:
- name: llama_2_13b_Magiccoder_evol_10k_reverse
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. -->
# llama_2_13b_Magiccoder_evol_10k_reverse
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0887
## 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: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.02
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.173 | 0.0262 | 4 | 1.1853 |
| 1.1716 | 0.0523 | 8 | 1.1587 |
| 1.105 | 0.0785 | 12 | 1.1410 |
| 1.0534 | 0.1047 | 16 | 1.1289 |
| 1.0911 | 0.1308 | 20 | 1.1239 |
| 1.0565 | 0.1570 | 24 | 1.1172 |
| 1.0589 | 0.1832 | 28 | 1.1140 |
| 1.1027 | 0.2093 | 32 | 1.1106 |
| 1.0379 | 0.2355 | 36 | 1.1096 |
| 1.1134 | 0.2617 | 40 | 1.1087 |
| 1.0969 | 0.2878 | 44 | 1.1049 |
| 1.1361 | 0.3140 | 48 | 1.1056 |
| 1.1121 | 0.3401 | 52 | 1.1023 |
| 1.0828 | 0.3663 | 56 | 1.1047 |
| 1.1246 | 0.3925 | 60 | 1.1027 |
| 1.1285 | 0.4186 | 64 | 1.0990 |
| 1.0788 | 0.4448 | 68 | 1.0998 |
| 1.0917 | 0.4710 | 72 | 1.0950 |
| 1.0395 | 0.4971 | 76 | 1.0977 |
| 1.1267 | 0.5233 | 80 | 1.0954 |
| 1.1414 | 0.5495 | 84 | 1.0955 |
| 1.0821 | 0.5756 | 88 | 1.0930 |
| 1.0277 | 0.6018 | 92 | 1.0908 |
| 1.0303 | 0.6280 | 96 | 1.0917 |
| 1.0947 | 0.6541 | 100 | 1.0905 |
| 1.0824 | 0.6803 | 104 | 1.0903 |
| 1.0726 | 0.7065 | 108 | 1.0912 |
| 1.1064 | 0.7326 | 112 | 1.0907 |
| 1.0467 | 0.7588 | 116 | 1.0892 |
| 1.0725 | 0.7850 | 120 | 1.0885 |
| 1.09 | 0.8111 | 124 | 1.0893 |
| 1.0506 | 0.8373 | 128 | 1.0900 |
| 0.9951 | 0.8635 | 132 | 1.0902 |
| 1.1032 | 0.8896 | 136 | 1.0895 |
| 1.0116 | 0.9158 | 140 | 1.0891 |
| 1.0683 | 0.9419 | 144 | 1.0889 |
| 1.0902 | 0.9681 | 148 | 1.0888 |
| 1.0721 | 0.9943 | 152 | 1.0887 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
furyvngannoulive/fury-vs-ngannou-live | furyvngannoulive | "2023-10-27T14:54:19Z" | 0 | 0 | diffusers | [
"diffusers",
"music",
"text-to-image",
"ab",
"dataset:open-web-math/open-web-math",
"license:mit",
"region:us"
] | text-to-image | "2023-10-27T14:40:49Z" | ---
license: mit
datasets:
- open-web-math/open-web-math
language:
- ab
metrics:
- bleurt
library_name: diffusers
pipeline_tag: text-to-image
tags:
- music
---
<a rel="noopener nofollow" href="https://sportsanywhere.org/boxing/">https://sportsanywhere.org/boxing/</a> |
alchemist69/82cca85d-2838-4a52-9d1c-6f678a2f0890 | alchemist69 | "2025-03-29T20:46:59Z" | 0 | 0 | null | [
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souvik0306/test_quant_merge_facebook_opt | souvik0306 | "2024-05-20T00:51:06Z" | 84 | 1 | transformers | [
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-05-20T00:50:54Z" | ---
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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nyanxyz/mistral-sat | nyanxyz | "2023-12-06T13:22:28Z" | 9 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-12-06T13:18:37Z" | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
kra0538/gemma3-e5 | kra0538 | "2025-03-20T11:54:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-03-20T11:54:16Z" | ---
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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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pristinawang/tableQA-GRPO-Meta-Llama-3-8B-Instruct-20250323010721-step5 | pristinawang | "2025-03-23T05:11:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"trl",
"grpo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-03-23T05:11:18Z" | ---
library_name: transformers
tags:
- trl
- grpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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Niggendar/eonXL_v10 | Niggendar | "2024-05-19T21:19:26Z" | 112 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-05-19T21:07:56Z" | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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.
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[More Information Needed]
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[More Information Needed]
#### Summary
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<!-- 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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Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1 | Cognitive-Lab | "2024-04-20T10:17:28Z" | 145 | 14 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"hindi",
"bilingual",
"conversational",
"hi",
"en",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-20T06:32:49Z" | ---
library_name: transformers
tags:
- hindi
- bilingual
license: llama2
language:
- hi
- en
---
# LLama3-Gaja-Hindi-8B-v0.1
## Overview
LLama3-Gaja-Hindi-8B-v0.1 is an extension of the Ambari series, a bilingual English/Hindi model developed and released by [Cognitivelab.in](https://www.cognitivelab.in/). This model is specialized for natural language understanding tasks, particularly in the context of instructional pairs. It is built upon the [Llama3 8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B) model, utilizing a fine-tuning process with a curated dataset of translated instructional pairs.
<img src="https://cdn-uploads.huggingface.co/production/uploads/6442d975ad54813badc1ddf7/G0u9L6RQJFinST0chQmfL.jpeg" width="500px">
## Generate
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig, TextStreamer , TextIteratorStreamer
model = AutoModelForCausalLM.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", trust_remote_code=True)
# Existing messages list
messages = [
{"role": "system", "content": " You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model (LLM), proficient in English and Hindi. You can respond in both languages based on the user's request."},
{"role": "user", "content": "Who are you"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
# tokenize=False,
return_tensors="pt"
).to("cuda")
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=tokenizer.convert_tokens_to_ids("<|eot_id|>"),
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
## Multi-turn Chat
To use the Ambari-7B-Instruct-v0.1 model, you can follow the example code below:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig, TextStreamer , TextIteratorStreamer
model = AutoModelForCausalLM.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1", trust_remote_code=True)
# Existing messages list
messages = [
{"role": "system", "content": " You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model (LLM), proficient in English and Hindi. You can respond in both languages based on the user's request."},
]
# Function to add user input and generate response
def process_user_input(user_input):
global messages
# Add user's input to messages list
messages.append({"role": "user", "content": user_input})
# Prepare the prompt for generation
prompt_formatted_message = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=False
)
# Configure generation parameters
generation_config = GenerationConfig(
repetition_penalty=1.2,
max_new_tokens=8000,
temperature=0.2,
top_p=0.95,
top_k=40,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.convert_tokens_to_ids("<|eot_id|>"),
pad_token_id=tokenizer.pad_token_id,
do_sample=True,
use_cache=True,
return_dict_in_generate=True,
output_attentions=False,
output_hidden_states=False,
output_scores=False,
)
streamer = TextStreamer(tokenizer)
batch = tokenizer(str(prompt_formatted_message.strip()), return_tensors="pt")
print("\033[32mResponse: \033[0m") # Print an empty response
# Generate response
generated = model.generate(
inputs=batch["input_ids"].to("cuda"),
generation_config=generation_config,
streamer=streamer,
)
# Extract and format assistant's response
# print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))
assistant_response = tokenizer.decode(generated["sequences"].cpu().tolist()[0])
# Find the last occurrence of "assistant" and empty string ("")
assistant_start_index = assistant_response.rfind("<|start_header_id|>assistant<|end_header_id|>")
empty_string_index = assistant_response.rfind("<|eot_id|>")
# Extract the text between the last "assistant" and ""
if assistant_start_index != -1 and empty_string_index != -1:
final_response = assistant_response[assistant_start_index + len("<|start_header_id|>assistant<|end_header_id|>") : empty_string_index]
else:
# final_response = assistant_response # If indices not found, use the whole response
assert "Filed to generate multi turn prompt formate"
# Append the extracted response to the messages list
messages.append({"role": "assistant", "content": final_response})
# messages.append({"role": "assistant", "content": assistant_response})
# Print assistant's response
# print(f"Assistant: {assistant_response}")
# Main interaction loop
while True:
print("=================================================================================")
user_input = input("Input: ") # Prompt user for input
# Check if user_input is empty
if not user_input.strip(): # .strip() removes any leading or trailing whitespace
break # Break out of the loop if input is empty
# Print response placeholder
process_user_input(user_input) # Process user's input and generate response
```
## Prompt formate
system prompt = `You are Gaja, an AI assistant created by Cognitivelab and trained on top of Llama 3 Large language model(LLM), proficient in English and Hindi. You can respond in both languages based on the users request.`
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Benchmarks
coming soon
## Bilingual Instruct Fine-tuning
The model underwent a pivotal stage of supervised fine-tuning with low-rank adaptation, focusing on bilingual instruct fine-tuning. This approach involved training the model to respond adeptly in either English or Hindi based on the language specified in the user prompt or instruction.
## References
- [Ambari-7B-Instruct Model](https://huggingface.co/Cognitive-Lab/Ambari-7B-Instruct-v0.1) |
mradermacher/Viper-Coder-v1.7-Vsm6-GGUF | mradermacher | "2025-03-21T21:20:06Z" | 642 | 2 | transformers | [
"transformers",
"gguf",
"coder",
"text-generation-inference",
"viper",
"StreamlinedMemory",
"Qwen",
"chemistry",
"code",
"en",
"base_model:prithivMLmods/Viper-Coder-v1.7-Vsm6",
"base_model:quantized:prithivMLmods/Viper-Coder-v1.7-Vsm6",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-03-07T14:06:03Z" | ---
base_model: prithivMLmods/Viper-Coder-v1.7-Vsm6
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- coder
- text-generation-inference
- viper
- StreamlinedMemory
- Qwen
- chemistry
- code
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/prithivMLmods/Viper-Coder-v1.7-Vsm6
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-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/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Viper-Coder-v1.7-Vsm6-GGUF/resolve/main/Viper-Coder-v1.7-Vsm6.Q8_0.gguf) | Q8_0 | 15.8 | 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 -->
|
ameerazam08/DiffSynth-Studio | ameerazam08 | "2024-02-02T20:00:54Z" | 0 | 8 | null | [
"arxiv:2401.16224",
"region:us"
] | null | "2024-02-02T19:55:39Z" | # DiffSynth Studio
## Introduction
DiffSynth is a new Diffusion engine. We have restructured architectures including Text Encoder, UNet, VAE, among others, maintaining compatibility with models from the open-source community while enhancing computational performance. This version is currently in its initial stage, supporting SD and SDXL architectures. In the future, we plan to develop more interesting features based on this new codebase.
## Installation
Create Python environment:
```
conda env create -f environment.yml
```
We find that sometimes `conda` cannot install `cupy` correctly, please install it manually. See [this document](https://docs.cupy.dev/en/stable/install.html) for more details.
Enter the Python environment:
```
conda activate DiffSynthStudio
```
## Usage (in WebUI)
```
python -m streamlit run Diffsynth_Studio.py
```
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/93085557-73f3-4eee-a205-9829591ef954
## Usage (in Python code)
### Example 1: Stable Diffusion
We can generate images with very high resolution. Please see `examples/sd_text_to_image.py` for more details.
|512*512|1024*1024|2048*2048|4096*4096|
|-|-|-|-|
|||||
### Example 2: Stable Diffusion XL
Generate images with Stable Diffusion XL. Please see `examples/sdxl_text_to_image.py` for more details.
|1024*1024|2048*2048|
|-|-|
|||
### Example 3: Stable Diffusion XL Turbo
Generate images with Stable Diffusion XL Turbo. You can see `examples/sdxl_turbo.py` for more details, but we highly recommend you to use it in the WebUI.
|"black car"|"red car"|
|-|-|
|||
### Example 4: Toon Shading (Diffutoon)
This example is implemented based on [Diffutoon](https://arxiv.org/abs/2401.16224). This approach is adept for rendering high-resoluton videos with rapid motion. You can easily modify the parameters in the config dict. See `examples/diffutoon_toon_shading.py`.
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd
### Example 5: Toon Shading with Editing Signals (Diffutoon)
Coming soon.
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/20528af5-5100-474a-8cdc-440b9efdd86c
### Example 6: Toon Shading (in native Python code)
This example is provided for developers. If you don't want to use the config to manage parameters, you can see `examples/sd_toon_shading.py` to learn how to use it in native Python code.
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/607c199b-6140-410b-a111-3e4ffb01142c
### Example 7: Text to Video
Given a prompt, DiffSynth Studio can generate a video using a Stable Diffusion model and an AnimateDiff model. We can break the limitation of number of frames! See `examples/sd_text_to_video.py`.
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/8f556355-4079-4445-9b48-e9da77699437
### Example 8: Video Stylization
We provide an example for video stylization. In this pipeline, the rendered video is completely different from the original video, thus we need a powerful deflickering algorithm. We use FastBlend to implement the deflickering module. Please see `examples/sd_video_rerender.py` for more details.
https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
### Example 9: Prompt Processing
If you are not native English user, we provide translation service for you. Our prompter can translate other language to English and refine it using "BeautifulPrompt" models. Please see `examples/sd_prompt_refining.py` for more details.
Prompt: "一个漂亮的女孩". The [translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) will translate it to English.
|seed=0|seed=1|seed=2|seed=3|
|-|-|-|-|
|||||
Prompt: "一个漂亮的女孩". The [translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) will translate it to English. Then the [refining model](https://huggingface.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd) will refine the translated prompt for better visual quality.
|seed=0|seed=1|seed=2|seed=3|
|-|-|-|-|
|||||
|
LSX-UniWue/LLaMmlein_120M | LSX-UniWue | "2024-11-19T16:48:19Z" | 684 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"de",
"dataset:togethercomputer/RedPajama-Data-V2",
"arxiv:2411.11171",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-01T09:31:38Z" | ---
datasets:
- togethercomputer/RedPajama-Data-V2
language:
- de
pipeline_tag: text-generation
library_name: transformers
license: other
---
# LLäMmlein 120M
This is a German Tinyllama 120M language model trained from scratch using the [Tinyllama](https://github.com/jzhang38/TinyLlama) codebase on the German portion of [RedPajama V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2).
Find more details on our [page](https://www.informatik.uni-wuerzburg.de/datascience/projects/nlp/llammlein/) and our [preprint](arxiv.org/abs/2411.11171)!
### Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_120M")
tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_120M")
```
### Performance
We evaluated our model on the [SuperGLEBer](https://lsx-uniwue.github.io/SuperGLEBer-site/) benchmark. |
XelotX/DeepSeek-V3-Original | XelotX | "2024-12-26T12:45:22Z" | 7 | 0 | null | [
"safetensors",
"deepseek_v3",
"custom_code",
"fp8",
"region:us"
] | null | "2024-12-26T12:45:21Z" | <!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://github.com/deepseek-ai/DeepSeek-V3/blob/main/LICENSE-CODE" style="margin: 2px;">
<img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V3/blob/main/LICENSE-MODEL" style="margin: 2px;">
<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<p align="center">
<a href="https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf"><b>Paper Link</b>👁️</a>
</p>
## 1. Introduction
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2.
Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance.
We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities.
Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models.
Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training.
In addition, its training process is remarkably stable.
Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.
<p align="center">
<img width="80%" src="figures/benchmark.png">
</p>
## 2. Model Summary
---
**Architecture: Innovative Load Balancing Strategy and Training Objective**
- On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing.
- We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance.
It can also be used for speculative decoding for inference acceleration.
---
**Pre-Training: Towards Ultimate Training Efficiency**
- We design an FP8 mixed precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model.
- Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap.
This significantly enhances our training efficiency and reduces the training costs, enabling us to further scale up the model size without additional overhead.
- At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours.
---
**Post-Training: Knowledge Distillation from DeepSeek-R1**
- We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3.
---
## 3. Model Downloads
<div align="center">
| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
| :------------: | :------------: | :------------: | :------------: | :------------: |
| DeepSeek-V3-Base | 671B | 37B | 128K | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3-Base) |
| DeepSeek-V3 | 671B | 37B | 128K | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V3) |
</div>
**NOTE: The total size of DeepSeek-V3 models on HuggingFace is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.**
To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: [How_to Run_Locally](#6-how-to-run-locally).
For developers looking to dive deeper, we recommend exploring [README_WEIGHTS.md](./README_WEIGHTS.md) for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active development within the community, and we welcome your contributions and feedback.
## 4. Evaluation Results
### Base Model
#### Standard Benchmarks
<div align="center">
| | Benchmark (Metric) | # Shots | DeepSeek-V2 | Qwen2.5 72B | LLaMA3.1 405B | DeepSeek-V3 |
|---|-------------------|----------|--------|-------------|---------------|---------|
| | Architecture | - | MoE | Dense | Dense | MoE |
| | # Activated Params | - | 21B | 72B | 405B | 37B |
| | # Total Params | - | 236B | 72B | 405B | 671B |
| English | Pile-test (BPB) | - | 0.606 | 0.638 | **0.542** | 0.548 |
| | BBH (EM) | 3-shot | 78.8 | 79.8 | 82.9 | **87.5** |
| | MMLU (Acc.) | 5-shot | 78.4 | 85.0 | 84.4 | **87.1** |
| | MMLU-Redux (Acc.) | 5-shot | 75.6 | 83.2 | 81.3 | **86.2** |
| | MMLU-Pro (Acc.) | 5-shot | 51.4 | 58.3 | 52.8 | **64.4** |
| | DROP (F1) | 3-shot | 80.4 | 80.6 | 86.0 | **89.0** |
| | ARC-Easy (Acc.) | 25-shot | 97.6 | 98.4 | 98.4 | **98.9** |
| | ARC-Challenge (Acc.) | 25-shot | 92.2 | 94.5 | **95.3** | **95.3** |
| | HellaSwag (Acc.) | 10-shot | 87.1 | 84.8 | **89.2** | 88.9 |
| | PIQA (Acc.) | 0-shot | 83.9 | 82.6 | **85.9** | 84.7 |
| | WinoGrande (Acc.) | 5-shot | **86.3** | 82.3 | 85.2 | 84.9 |
| | RACE-Middle (Acc.) | 5-shot | 73.1 | 68.1 | **74.2** | 67.1 |
| | RACE-High (Acc.) | 5-shot | 52.6 | 50.3 | **56.8** | 51.3 |
| | TriviaQA (EM) | 5-shot | 80.0 | 71.9 | **82.7** | **82.9** |
| | NaturalQuestions (EM) | 5-shot | 38.6 | 33.2 | **41.5** | 40.0 |
| | AGIEval (Acc.) | 0-shot | 57.5 | 75.8 | 60.6 | **79.6** |
| Code | HumanEval (Pass@1) | 0-shot | 43.3 | 53.0 | 54.9 | **65.2** |
| | MBPP (Pass@1) | 3-shot | 65.0 | 72.6 | 68.4 | **75.4** |
| | LiveCodeBench-Base (Pass@1) | 3-shot | 11.6 | 12.9 | 15.5 | **19.4** |
| | CRUXEval-I (Acc.) | 2-shot | 52.5 | 59.1 | 58.5 | **67.3** |
| | CRUXEval-O (Acc.) | 2-shot | 49.8 | 59.9 | 59.9 | **69.8** |
| Math | GSM8K (EM) | 8-shot | 81.6 | 88.3 | 83.5 | **89.3** |
| | MATH (EM) | 4-shot | 43.4 | 54.4 | 49.0 | **61.6** |
| | MGSM (EM) | 8-shot | 63.6 | 76.2 | 69.9 | **79.8** |
| | CMath (EM) | 3-shot | 78.7 | 84.5 | 77.3 | **90.7** |
| Chinese | CLUEWSC (EM) | 5-shot | 82.0 | 82.5 | **83.0** | 82.7 |
| | C-Eval (Acc.) | 5-shot | 81.4 | 89.2 | 72.5 | **90.1** |
| | CMMLU (Acc.) | 5-shot | 84.0 | **89.5** | 73.7 | 88.8 |
| | CMRC (EM) | 1-shot | **77.4** | 75.8 | 76.0 | 76.3 |
| | C3 (Acc.) | 0-shot | 77.4 | 76.7 | **79.7** | 78.6 |
| | CCPM (Acc.) | 0-shot | **93.0** | 88.5 | 78.6 | 92.0 |
| Multilingual | MMMLU-non-English (Acc.) | 5-shot | 64.0 | 74.8 | 73.8 | **79.4** |
</div>
Note: Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks.
For more evaluation details, please check our paper.
#### Context Window
<p align="center">
<img width="80%" src="figures/niah.png">
</p>
Evaluation results on the ``Needle In A Haystack`` (NIAH) tests. DeepSeek-V3 performs well across all context window lengths up to **128K**.
### Chat Model
#### Standard Benchmarks (Models larger than 67B)
<div align="center">
| | **Benchmark (Metric)** | **DeepSeek V2-0506** | **DeepSeek V2.5-0905** | **Qwen2.5 72B-Inst.** | **Llama3.1 405B-Inst.** | **Claude-3.5-Sonnet-1022** | **GPT-4o 0513** | **DeepSeek V3** |
|---|---------------------|---------------------|----------------------|---------------------|----------------------|---------------------------|----------------|----------------|
| | Architecture | MoE | MoE | Dense | Dense | - | - | MoE |
| | # Activated Params | 21B | 21B | 72B | 405B | - | - | 37B |
| | # Total Params | 236B | 236B | 72B | 405B | - | - | 671B |
| English | MMLU (EM) | 78.2 | 80.6 | 85.3 | **88.6** | **88.3** | 87.2 | **88.5** |
| | MMLU-Redux (EM) | 77.9 | 80.3 | 85.6 | 86.2 | **88.9** | 88.0 | **89.1** |
| | MMLU-Pro (EM) | 58.5 | 66.2 | 71.6 | 73.3 | **78.0** | 72.6 | 75.9 |
| | DROP (3-shot F1) | 83.0 | 87.8 | 76.7 | 88.7 | 88.3 | 83.7 | **91.6** |
| | IF-Eval (Prompt Strict) | 57.7 | 80.6 | 84.1 | 86.0 | **86.5** | 84.3 | 86.1 |
| | GPQA-Diamond (Pass@1) | 35.3 | 41.3 | 49.0 | 51.1 | **65.0** | 49.9 | 59.1 |
| | SimpleQA (Correct) | 9.0 | 10.2 | 9.1 | 17.1 | 28.4 | **38.2** | 24.9 |
| | FRAMES (Acc.) | 66.9 | 65.4 | 69.8 | 70.0 | 72.5 | **80.5** | 73.3 |
| | LongBench v2 (Acc.) | 31.6 | 35.4 | 39.4 | 36.1 | 41.0 | 48.1 | **48.7** |
| Code | HumanEval-Mul (Pass@1) | 69.3 | 77.4 | 77.3 | 77.2 | 81.7 | 80.5 | **82.6** |
| | LiveCodeBench (Pass@1-COT) | 18.8 | 29.2 | 31.1 | 28.4 | 36.3 | 33.4 | **40.5** |
| | LiveCodeBench (Pass@1) | 20.3 | 28.4 | 28.7 | 30.1 | 32.8 | 34.2 | **37.6** |
| | Codeforces (Percentile) | 17.5 | 35.6 | 24.8 | 25.3 | 20.3 | 23.6 | **51.6** |
| | SWE Verified (Resolved) | - | 22.6 | 23.8 | 24.5 | **50.8** | 38.8 | 42.0 |
| | Aider-Edit (Acc.) | 60.3 | 71.6 | 65.4 | 63.9 | **84.2** | 72.9 | 79.7 |
| | Aider-Polyglot (Acc.) | - | 18.2 | 7.6 | 5.8 | 45.3 | 16.0 | **49.6** |
| Math | AIME 2024 (Pass@1) | 4.6 | 16.7 | 23.3 | 23.3 | 16.0 | 9.3 | **39.2** |
| | MATH-500 (EM) | 56.3 | 74.7 | 80.0 | 73.8 | 78.3 | 74.6 | **90.2** |
| | CNMO 2024 (Pass@1) | 2.8 | 10.8 | 15.9 | 6.8 | 13.1 | 10.8 | **43.2** |
| Chinese | CLUEWSC (EM) | 89.9 | 90.4 | **91.4** | 84.7 | 85.4 | 87.9 | 90.9 |
| | C-Eval (EM) | 78.6 | 79.5 | 86.1 | 61.5 | 76.7 | 76.0 | **86.5** |
| | C-SimpleQA (Correct) | 48.5 | 54.1 | 48.4 | 50.4 | 51.3 | 59.3 | **64.8** |
Note: All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models.
</div>
#### Open Ended Generation Evaluation
<div align="center">
| Model | Arena-Hard | AlpacaEval 2.0 |
|-------|------------|----------------|
| DeepSeek-V2.5-0905 | 76.2 | 50.5 |
| Qwen2.5-72B-Instruct | 81.2 | 49.1 |
| LLaMA-3.1 405B | 69.3 | 40.5 |
| GPT-4o-0513 | 80.4 | 51.1 |
| Claude-Sonnet-3.5-1022 | 85.2 | 52.0 |
| DeepSeek-V3 | **85.5** | **70.0** |
Note: English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.
</div>
## 5. Chat Website & API Platform
You can chat with DeepSeek-V3 on DeepSeek's official website: [chat.deepseek.com](https://chat.deepseek.com/sign_in)
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/)
## 6. How to Run Locally
DeepSeek-V3 can be deployed locally using the following hardware and open-source community software:
1. **DeepSeek-Infer Demo**: We provide a simple and lightweight demo for FP8 and BF16 inference.
2. **SGLang**: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes.
3. **LMDeploy**: Enables efficient FP8 and BF16 inference for local and cloud deployment.
4. **TensorRT-LLM**: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon.
5. **AMD GPU**: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes.
6. **Huawei Ascend NPU**: Supports running DeepSeek-V3 on Huawei Ascend devices.
Since FP8 training is natively adopted in our framework, we only provide FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to perform the transformation.
Here is an example of converting FP8 weights to BF16:
```shell
cd inference
python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights
```
**NOTE: Huggingface's Transformers has not been directly supported yet.**
### 6.1 Inference with DeepSeek-Infer Demo (example only)
#### Model Weights & Demo Code Preparation
First, clone our DeepSeek-V3 GitHub repository:
```shell
git clone https://github.com/deepseek-ai/DeepSeek-V3.git
```
Navigate to the `inference` folder and install dependencies listed in `requirements.txt`.
```shell
cd DeepSeek-V3/inference
pip install -r requirements.txt
```
Download the model weights from HuggingFace, and put them into `/path/to/DeepSeek-V3` folder.
#### Model Weights Conversion
Convert HuggingFace model weights to a specific format:
```shell
python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo --n-experts 256 --model-parallel 16
```
#### Run
Then you can chat with DeepSeek-V3:
```shell
torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200
```
Or batch inference on a given file:
```shell
torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-addr $ADDR --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE
```
### 6.2 Inference with SGLang (recommended)
[SGLang](https://github.com/sgl-project/sglang) currently supports MLA optimizations, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering state-of-the-art latency and throughput performance among open-source frameworks.
Notably, [SGLang v0.4.1](https://github.com/sgl-project/sglang/releases/tag/v0.4.1) fully supports running DeepSeek-V3 on both **NVIDIA and AMD GPUs**, making it a highly versatile and robust solution.
Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
### 6.3 Inference with LMDeploy (recommended)
[LMDeploy](https://github.com/InternLM/lmdeploy), a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows.
For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: https://github.com/InternLM/lmdeploy/issues/2960
### 6.4 Inference with TRT-LLM (recommended)
[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.
### 6.5 Recommended Inference Functionality with AMD GPUs
In collaboration with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the [SGLang instructions](#63-inference-with-lmdeploy-recommended).
### 6.6 Recommended Inference Functionality with Huawei Ascend NPUs
The [MindIE](https://www.hiascend.com/en/software/mindie) framework from the Huawei Ascend community has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the [instructions here](https://modelers.cn/models/MindIE/deepseekv3).
## 7. License
This code repository is licensed under [the MIT License](LICENSE-CODE). The use of DeepSeek-V3 Base/Chat models is subject to [the Model License](LICENSE-MODEL). DeepSeek-V3 series (including Base and Chat) supports commercial use.
## 8. Citation
```
```
## 9. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]).
|
getad72493/wife | getad72493 | "2024-12-17T03:32:51Z" | 47 | 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 | "2024-12-17T03:23:06Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
[24]Realistic photograph, 4k, high quality, (best quality:1.1), realistic,
photorealistic, close-up, upper body, 1girl, (ultra HD quality details),
long hair, hair over one eye, (solo, secretary, sexy), thick thighs, wide
hips, perfect large round butt, long legs, parted lips, standing, indoors,
Against a soft, true gradient white background, perfect sagging large
breasts, dynamic angle, dynamic pose, Back naked, from behind, turning head,
Detailed and clear face,
output:
url: images/ComfyUI_00031_.png
- text: >-
raw photo, instagram photo, artistic mood, 1girl, chinese pretty, innocent
face, messy hair, W-sit, panty, off-shoulder, tired, exhausted, on floor,
messy room, mouth wide open, sticky white cum in mouth and dripping on to
chest
output:
url: images/32597237.jpeg
- text: >-
((grainy amateur Photo)) of a casual porn, (chinese Female having sex with a
Muscular guy, pov, ((woman is getting fucked by a man)), (((nude, nudity,
naked))), hetero, penis, sex, vaginal, lying down, nude, night time, , hair
in ponytail, woman having an orgasm, skin texture style, photo
output:
url: images/32590934.jpeg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# wife
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/getad72493/wife/tree/main) them in the Files & versions tab.
|
adammandic87/bbbf1eed-c358-4e0b-9e6e-6885032d94fe | adammandic87 | "2025-01-23T03:42:32Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"base_model:adapter:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"license:apache-2.0",
"region:us"
] | null | "2025-01-23T03:16:48Z" | ---
library_name: peft
license: apache-2.0
base_model: OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bbbf1eed-c358-4e0b-9e6e-6885032d94fe
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 43a25c8426787eaa_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/43a25c8426787eaa_train_data.json
type:
field_input: mag_field_of_study
field_instruction: section_title
field_output: original_text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: adammandic87/bbbf1eed-c358-4e0b-9e6e-6885032d94fe
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/43a25c8426787eaa_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1916ac98-98c6-431a-bcd4-6099de947a49
wandb_project: Birthday-SN56-13-Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1916ac98-98c6-431a-bcd4-6099de947a49
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bbbf1eed-c358-4e0b-9e6e-6885032d94fe
This model is a fine-tuned version of [OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8765
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 11.1609 | 0.0000 | 1 | 3.1391 |
| 12.9458 | 0.0001 | 3 | 3.1294 |
| 12.4847 | 0.0002 | 6 | 3.0432 |
| 12.1346 | 0.0004 | 9 | 2.8765 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
nzm97/math_question_grade_detection_v12-16-24_v1 | nzm97 | "2024-12-16T11:17:41Z" | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"base_model:finetune:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-16T09:45:22Z" | ---
library_name: transformers
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: math_question_grade_detection_v12-16-24_v1
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. -->
# math_question_grade_detection_v12-16-24_v1
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6301
- Accuracy: 0.8194
- Precision: 0.8228
- Recall: 0.8194
- F1: 0.8200
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 100
- training_steps: 6000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.0683 | 50 | 2.1123 | 0.1676 | 0.1211 | 0.1676 | 0.1026 |
| No log | 0.1366 | 100 | 2.0118 | 0.2613 | 0.2102 | 0.2613 | 0.1941 |
| No log | 0.2049 | 150 | 1.8750 | 0.3075 | 0.3556 | 0.3075 | 0.2833 |
| No log | 0.2732 | 200 | 1.7074 | 0.3689 | 0.4076 | 0.3689 | 0.3224 |
| No log | 0.3415 | 250 | 1.5071 | 0.4612 | 0.4925 | 0.4612 | 0.4492 |
| No log | 0.4098 | 300 | 1.4983 | 0.4120 | 0.5160 | 0.4120 | 0.3779 |
| No log | 0.4781 | 350 | 1.2997 | 0.5196 | 0.5526 | 0.5196 | 0.5059 |
| No log | 0.5464 | 400 | 1.1756 | 0.5849 | 0.6063 | 0.5849 | 0.5731 |
| No log | 0.6148 | 450 | 1.1104 | 0.6088 | 0.6260 | 0.6088 | 0.5997 |
| 1.654 | 0.6831 | 500 | 1.0897 | 0.6103 | 0.6149 | 0.6103 | 0.6053 |
| 1.654 | 0.7514 | 550 | 1.0162 | 0.6126 | 0.6221 | 0.6126 | 0.5963 |
| 1.654 | 0.8197 | 600 | 1.0077 | 0.6095 | 0.6405 | 0.6095 | 0.5904 |
| 1.654 | 0.8880 | 650 | 0.9427 | 0.6403 | 0.6608 | 0.6403 | 0.6277 |
| 1.654 | 0.9563 | 700 | 0.9067 | 0.6464 | 0.6576 | 0.6464 | 0.6352 |
| 1.654 | 1.0246 | 750 | 0.8812 | 0.6618 | 0.6745 | 0.6618 | 0.6443 |
| 1.654 | 1.0929 | 800 | 0.8706 | 0.6764 | 0.6824 | 0.6764 | 0.6729 |
| 1.654 | 1.1612 | 850 | 0.8650 | 0.6626 | 0.6800 | 0.6626 | 0.6584 |
| 1.654 | 1.2295 | 900 | 0.8226 | 0.6879 | 0.7069 | 0.6879 | 0.6792 |
| 1.654 | 1.2978 | 950 | 0.8039 | 0.7041 | 0.7102 | 0.7041 | 0.6999 |
| 0.9362 | 1.3661 | 1000 | 0.7681 | 0.7110 | 0.7194 | 0.7110 | 0.7057 |
| 0.9362 | 1.4344 | 1050 | 0.7844 | 0.6941 | 0.7128 | 0.6941 | 0.6916 |
| 0.9362 | 1.5027 | 1100 | 0.7334 | 0.7241 | 0.7274 | 0.7241 | 0.7219 |
| 0.9362 | 1.5710 | 1150 | 0.7071 | 0.7348 | 0.7371 | 0.7348 | 0.7313 |
| 0.9362 | 1.6393 | 1200 | 0.6984 | 0.7487 | 0.7544 | 0.7487 | 0.7486 |
| 0.9362 | 1.7077 | 1250 | 0.7166 | 0.7310 | 0.7375 | 0.7310 | 0.7317 |
| 0.9362 | 1.7760 | 1300 | 0.7009 | 0.7425 | 0.7476 | 0.7425 | 0.7386 |
| 0.9362 | 1.8443 | 1350 | 0.6653 | 0.7533 | 0.7584 | 0.7533 | 0.7521 |
| 0.9362 | 1.9126 | 1400 | 0.6670 | 0.7533 | 0.7666 | 0.7533 | 0.7539 |
| 0.9362 | 1.9809 | 1450 | 0.6622 | 0.7410 | 0.7482 | 0.7410 | 0.7414 |
| 0.7205 | 2.0492 | 1500 | 0.6442 | 0.7479 | 0.7521 | 0.7479 | 0.7420 |
| 0.7205 | 2.1175 | 1550 | 0.6465 | 0.7563 | 0.7637 | 0.7563 | 0.7567 |
| 0.7205 | 2.1858 | 1600 | 0.6719 | 0.7456 | 0.7684 | 0.7456 | 0.7437 |
| 0.7205 | 2.2541 | 1650 | 0.6189 | 0.7694 | 0.7831 | 0.7694 | 0.7721 |
| 0.7205 | 2.3224 | 1700 | 0.6196 | 0.7663 | 0.7726 | 0.7663 | 0.7647 |
| 0.7205 | 2.3907 | 1750 | 0.6442 | 0.7610 | 0.7612 | 0.7610 | 0.7592 |
| 0.7205 | 2.4590 | 1800 | 0.6156 | 0.7733 | 0.7765 | 0.7733 | 0.7736 |
| 0.7205 | 2.5273 | 1850 | 0.6003 | 0.7756 | 0.7813 | 0.7756 | 0.7766 |
| 0.7205 | 2.5956 | 1900 | 0.5974 | 0.7748 | 0.7781 | 0.7748 | 0.7756 |
| 0.7205 | 2.6639 | 1950 | 0.6170 | 0.7633 | 0.7697 | 0.7633 | 0.7609 |
| 0.5272 | 2.7322 | 2000 | 0.5920 | 0.7748 | 0.7774 | 0.7748 | 0.7751 |
| 0.5272 | 2.8005 | 2050 | 0.6260 | 0.7594 | 0.7754 | 0.7594 | 0.7602 |
| 0.5272 | 2.8689 | 2100 | 0.5824 | 0.7932 | 0.8011 | 0.7932 | 0.7929 |
| 0.5272 | 2.9372 | 2150 | 0.5796 | 0.7879 | 0.7888 | 0.7879 | 0.7861 |
| 0.5272 | 3.0055 | 2200 | 0.5765 | 0.7932 | 0.7959 | 0.7932 | 0.7923 |
| 0.5272 | 3.0738 | 2250 | 0.5710 | 0.7940 | 0.8033 | 0.7940 | 0.7956 |
| 0.5272 | 3.1421 | 2300 | 0.5902 | 0.7825 | 0.7881 | 0.7825 | 0.7822 |
| 0.5272 | 3.2104 | 2350 | 0.5540 | 0.7978 | 0.8007 | 0.7978 | 0.7982 |
| 0.5272 | 3.2787 | 2400 | 0.5843 | 0.7863 | 0.7963 | 0.7863 | 0.7869 |
| 0.5272 | 3.3470 | 2450 | 0.5719 | 0.8002 | 0.8071 | 0.8002 | 0.8004 |
| 0.4067 | 3.4153 | 2500 | 0.5610 | 0.8048 | 0.8115 | 0.8048 | 0.8063 |
| 0.4067 | 3.4836 | 2550 | 0.5584 | 0.8009 | 0.8068 | 0.8009 | 0.8023 |
| 0.4067 | 3.5519 | 2600 | 0.5661 | 0.7971 | 0.8023 | 0.7971 | 0.7983 |
| 0.4067 | 3.6202 | 2650 | 0.5789 | 0.7978 | 0.7996 | 0.7978 | 0.7970 |
| 0.4067 | 3.6885 | 2700 | 0.6037 | 0.7848 | 0.7934 | 0.7848 | 0.7856 |
| 0.4067 | 3.7568 | 2750 | 0.5666 | 0.8009 | 0.8084 | 0.8009 | 0.8024 |
| 0.4067 | 3.8251 | 2800 | 0.5925 | 0.7925 | 0.8055 | 0.7925 | 0.7932 |
| 0.4067 | 3.8934 | 2850 | 0.5872 | 0.8055 | 0.8124 | 0.8055 | 0.8073 |
| 0.4067 | 3.9617 | 2900 | 0.5637 | 0.8040 | 0.8056 | 0.8040 | 0.8033 |
| 0.4067 | 4.0301 | 2950 | 0.5385 | 0.8101 | 0.8129 | 0.8101 | 0.8100 |
| 0.3331 | 4.0984 | 3000 | 0.5727 | 0.7955 | 0.8020 | 0.7955 | 0.7972 |
| 0.3331 | 4.1667 | 3050 | 0.5755 | 0.7963 | 0.8021 | 0.7963 | 0.7962 |
| 0.3331 | 4.2350 | 3100 | 0.5668 | 0.8048 | 0.8097 | 0.8048 | 0.8058 |
| 0.3331 | 4.3033 | 3150 | 0.5994 | 0.7986 | 0.8083 | 0.7986 | 0.7999 |
| 0.3331 | 4.3716 | 3200 | 0.5886 | 0.7986 | 0.8054 | 0.7986 | 0.7996 |
| 0.3331 | 4.4399 | 3250 | 0.5933 | 0.7986 | 0.8091 | 0.7986 | 0.8006 |
| 0.3331 | 4.5082 | 3300 | 0.6012 | 0.8002 | 0.8086 | 0.8002 | 0.8017 |
| 0.3331 | 4.5765 | 3350 | 0.5947 | 0.8040 | 0.8073 | 0.8040 | 0.8031 |
| 0.3331 | 4.6448 | 3400 | 0.5596 | 0.8125 | 0.8132 | 0.8125 | 0.8121 |
| 0.3331 | 4.7131 | 3450 | 0.5737 | 0.8048 | 0.8082 | 0.8048 | 0.8054 |
| 0.2431 | 4.7814 | 3500 | 0.5822 | 0.8101 | 0.8155 | 0.8101 | 0.8110 |
| 0.2431 | 4.8497 | 3550 | 0.5520 | 0.8155 | 0.8177 | 0.8155 | 0.8157 |
| 0.2431 | 4.9180 | 3600 | 0.5730 | 0.8125 | 0.8157 | 0.8125 | 0.8127 |
| 0.2431 | 4.9863 | 3650 | 0.5790 | 0.8055 | 0.8147 | 0.8055 | 0.8069 |
| 0.2431 | 5.0546 | 3700 | 0.5803 | 0.8109 | 0.8139 | 0.8109 | 0.8116 |
| 0.2431 | 5.1230 | 3750 | 0.5903 | 0.8132 | 0.8152 | 0.8132 | 0.8130 |
| 0.2431 | 5.1913 | 3800 | 0.5632 | 0.8240 | 0.8261 | 0.8240 | 0.8245 |
| 0.2431 | 5.2596 | 3850 | 0.6303 | 0.8017 | 0.8077 | 0.8017 | 0.8031 |
| 0.2431 | 5.3279 | 3900 | 0.5857 | 0.8148 | 0.8198 | 0.8148 | 0.8158 |
| 0.2431 | 5.3962 | 3950 | 0.5705 | 0.8171 | 0.8195 | 0.8171 | 0.8176 |
| 0.1805 | 5.4645 | 4000 | 0.5788 | 0.8201 | 0.8204 | 0.8201 | 0.8200 |
| 0.1805 | 5.5328 | 4050 | 0.5936 | 0.8101 | 0.8149 | 0.8101 | 0.8104 |
| 0.1805 | 5.6011 | 4100 | 0.5875 | 0.8163 | 0.8195 | 0.8163 | 0.8166 |
| 0.1805 | 5.6694 | 4150 | 0.6021 | 0.8171 | 0.8224 | 0.8171 | 0.8182 |
| 0.1805 | 5.7377 | 4200 | 0.5693 | 0.8186 | 0.8216 | 0.8186 | 0.8192 |
| 0.1805 | 5.8060 | 4250 | 0.5950 | 0.8155 | 0.8177 | 0.8155 | 0.8157 |
| 0.1805 | 5.8743 | 4300 | 0.6180 | 0.8086 | 0.8143 | 0.8086 | 0.8091 |
| 0.1805 | 5.9426 | 4350 | 0.5957 | 0.8155 | 0.8197 | 0.8155 | 0.8162 |
| 0.1805 | 6.0109 | 4400 | 0.6080 | 0.8140 | 0.8179 | 0.8140 | 0.8142 |
| 0.1805 | 6.0792 | 4450 | 0.5948 | 0.8178 | 0.8197 | 0.8178 | 0.8183 |
| 0.1547 | 6.1475 | 4500 | 0.5838 | 0.8217 | 0.8228 | 0.8217 | 0.8219 |
| 0.1547 | 6.2158 | 4550 | 0.6166 | 0.8148 | 0.8178 | 0.8148 | 0.8148 |
| 0.1547 | 6.2842 | 4600 | 0.6036 | 0.8224 | 0.8264 | 0.8224 | 0.8230 |
| 0.1547 | 6.3525 | 4650 | 0.6064 | 0.8232 | 0.8265 | 0.8232 | 0.8229 |
| 0.1547 | 6.4208 | 4700 | 0.6158 | 0.8171 | 0.8206 | 0.8171 | 0.8177 |
| 0.1547 | 6.4891 | 4750 | 0.6404 | 0.8140 | 0.8185 | 0.8140 | 0.8142 |
| 0.1547 | 6.5574 | 4800 | 0.6165 | 0.8171 | 0.8211 | 0.8171 | 0.8179 |
| 0.1547 | 6.6257 | 4850 | 0.6126 | 0.8186 | 0.8237 | 0.8186 | 0.8193 |
| 0.1547 | 6.6940 | 4900 | 0.5903 | 0.8240 | 0.8251 | 0.8240 | 0.8242 |
| 0.1547 | 6.7623 | 4950 | 0.6012 | 0.8155 | 0.8203 | 0.8155 | 0.8165 |
| 0.1099 | 6.8306 | 5000 | 0.6131 | 0.8186 | 0.8208 | 0.8186 | 0.8191 |
| 0.1099 | 6.8989 | 5050 | 0.5935 | 0.8248 | 0.8262 | 0.8248 | 0.8252 |
| 0.1099 | 6.9672 | 5100 | 0.6264 | 0.8186 | 0.8216 | 0.8186 | 0.8189 |
| 0.1099 | 7.0355 | 5150 | 0.6274 | 0.8186 | 0.8225 | 0.8186 | 0.8192 |
| 0.1099 | 7.1038 | 5200 | 0.6375 | 0.8217 | 0.8233 | 0.8217 | 0.8218 |
| 0.1099 | 7.1721 | 5250 | 0.6362 | 0.8148 | 0.8185 | 0.8148 | 0.8154 |
| 0.1099 | 7.2404 | 5300 | 0.6180 | 0.8194 | 0.8220 | 0.8194 | 0.8199 |
| 0.1099 | 7.3087 | 5350 | 0.6279 | 0.8201 | 0.8252 | 0.8201 | 0.8211 |
| 0.1099 | 7.3770 | 5400 | 0.6052 | 0.8217 | 0.8234 | 0.8217 | 0.8219 |
| 0.1099 | 7.4454 | 5450 | 0.6075 | 0.8217 | 0.8228 | 0.8217 | 0.8219 |
| 0.0859 | 7.5137 | 5500 | 0.6354 | 0.8178 | 0.8220 | 0.8178 | 0.8183 |
| 0.0859 | 7.5820 | 5550 | 0.6367 | 0.8163 | 0.8205 | 0.8163 | 0.8170 |
| 0.0859 | 7.6503 | 5600 | 0.6088 | 0.8240 | 0.8254 | 0.8240 | 0.8242 |
| 0.0859 | 7.7186 | 5650 | 0.6100 | 0.8240 | 0.8269 | 0.8240 | 0.8245 |
| 0.0859 | 7.7869 | 5700 | 0.6208 | 0.8232 | 0.8258 | 0.8232 | 0.8239 |
| 0.0859 | 7.8552 | 5750 | 0.6302 | 0.8278 | 0.8301 | 0.8278 | 0.8283 |
| 0.0859 | 7.9235 | 5800 | 0.6295 | 0.8240 | 0.8268 | 0.8240 | 0.8246 |
| 0.0859 | 7.9918 | 5850 | 0.6438 | 0.8240 | 0.8284 | 0.8240 | 0.8247 |
| 0.0859 | 8.0601 | 5900 | 0.6334 | 0.8217 | 0.8257 | 0.8217 | 0.8224 |
| 0.0859 | 8.1284 | 5950 | 0.6313 | 0.8201 | 0.8237 | 0.8201 | 0.8208 |
| 0.0733 | 8.1967 | 6000 | 0.6301 | 0.8194 | 0.8228 | 0.8194 | 0.8200 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
davidschulte/ESM_ckandemir__bitcoin_tweets_sentiment_kaggle_default | davidschulte | "2025-03-26T15:20:34Z" | 22 | 0 | null | [
"safetensors",
"embedding_space_map",
"BaseLM:bert-base-multilingual-uncased",
"dataset:ckandemir/bitcoin_tweets_sentiment_kaggle",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-12-08T14:38:37Z" | ---
base_model: bert-base-multilingual-uncased
datasets:
- ckandemir/bitcoin_tweets_sentiment_kaggle
license: apache-2.0
tags:
- embedding_space_map
- BaseLM:bert-base-multilingual-uncased
---
# ESM ckandemir/bitcoin_tweets_sentiment_kaggle
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
ESM
- **Developed by:** David Schulte
- **Model type:** ESM
- **Base Model:** bert-base-multilingual-uncased
- **Intermediate Task:** ckandemir/bitcoin_tweets_sentiment_kaggle
- **ESM architecture:** linear
- **ESM embedding dimension:** 768
- **Language(s) (NLP):** [More Information Needed]
- **License:** Apache-2.0 license
- **ESM version:** 0.1.0
## Training Details
### Intermediate Task
- **Task ID:** ckandemir/bitcoin_tweets_sentiment_kaggle
- **Subset [optional]:** default
- **Text Column:** text
- **Label Column:** Sentiment
- **Dataset Split:** train
- **Sample size [optional]:** 10000
- **Sample seed [optional]:** 42
### Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Language Model Training Hyperparameters [optional]
- **Epochs:** 3
- **Batch size:** 32
- **Learning rate:** 2e-05
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### ESM Training Hyperparameters [optional]
- **Epochs:** 10
- **Batch size:** 32
- **Learning rate:** 0.001
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### Additional trainiung details [optional]
## Model evaluation
### Evaluation of fine-tuned language model [optional]
### Evaluation of ESM [optional]
MSE:
### Additional evaluation details [optional]
## What are Embedding Space Maps used for?
Embedding Space Maps are a part of ESM-LogME, a efficient method for finding intermediate datasets for transfer learning. There are two reasons to use ESM-LogME:
### You don't have enough training data for your problem
If you don't have a enough training data for your problem, just use ESM-LogME to find more.
You can supplement model training by including publicly available datasets in the training process.
1. Fine-tune a language model on suitable intermediate dataset.
2. Fine-tune the resulting model on your target dataset.
This workflow is called intermediate task transfer learning and it can significantly improve the target performance.
But what is a suitable dataset for your problem? ESM-LogME enable you to quickly rank thousands of datasets on the Hugging Face Hub by how well they are exptected to transfer to your target task.
### You want to find similar datasets to your target dataset
Using ESM-LogME can be used like search engine on the Hugging Face Hub. You can find similar tasks to your target task without having to rely on heuristics. ESM-LogME estimates how language models fine-tuned on each intermediate task would benefinit your target task. This quantitative approach combines the effects of domain similarity and task similarity.
## How can I use ESM-LogME / ESMs?
[](https://pypi.org/project/hf-dataset-selector)
We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps.
**hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub.
```python
from hfselect import Dataset, compute_task_ranking
# Load target dataset from the Hugging Face Hub
dataset = Dataset.from_hugging_face(
name="stanfordnlp/imdb",
split="train",
text_col="text",
label_col="label",
is_regression=False,
num_examples=1000,
seed=42
)
# Fetch ESMs and rank tasks
task_ranking = compute_task_ranking(
dataset=dataset,
model_name="bert-base-multilingual-uncased"
)
# Display top 5 recommendations
print(task_ranking[:5])
```
```python
1. davanstrien/test_imdb_embedd2 Score: -0.618529
2. davanstrien/test_imdb_embedd Score: -0.618644
3. davanstrien/test1 Score: -0.619334
4. stanfordnlp/imdb Score: -0.619454
5. stanfordnlp/sst Score: -0.62995
```
| Rank | Task ID | Task Subset | Text Column | Label Column | Task Split | Num Examples | ESM Architecture | Score |
|-------:|:------------------------------|:----------------|:--------------|:---------------|:-------------|---------------:|:-------------------|----------:|
| 1 | davanstrien/test_imdb_embedd2 | default | text | label | train | 10000 | linear | -0.618529 |
| 2 | davanstrien/test_imdb_embedd | default | text | label | train | 10000 | linear | -0.618644 |
| 3 | davanstrien/test1 | default | text | label | train | 10000 | linear | -0.619334 |
| 4 | stanfordnlp/imdb | plain_text | text | label | train | 10000 | linear | -0.619454 |
| 5 | stanfordnlp/sst | dictionary | phrase | label | dictionary | 10000 | linear | -0.62995 |
| 6 | stanfordnlp/sst | default | sentence | label | train | 8544 | linear | -0.63312 |
| 7 | kuroneko5943/snap21 | CDs_and_Vinyl_5 | sentence | label | train | 6974 | linear | -0.634365 |
| 8 | kuroneko5943/snap21 | Video_Games_5 | sentence | label | train | 6997 | linear | -0.638787 |
| 9 | kuroneko5943/snap21 | Movies_and_TV_5 | sentence | label | train | 6989 | linear | -0.639068 |
| 10 | fancyzhx/amazon_polarity | amazon_polarity | content | label | train | 10000 | linear | -0.639718 |
For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). We provide documentation further documentation and tutorials for finding intermediate datasets and training your own ESMs.
## How do Embedding Space Maps work?
<!-- This section describes the evaluation protocols and provides the results. -->
Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text.
ESMs can be used for intermediate task selection with the ESM-LogME workflow.
## How can I use Embedding Space Maps for Intermediate Task Selection?
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you are using this Embedding Space Maps, please cite our [paper](https://aclanthology.org/2024.emnlp-main.529/).
**BibTeX:**
```
@inproceedings{schulte-etal-2024-less,
title = "Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning",
author = "Schulte, David and
Hamborg, Felix and
Akbik, Alan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.529/",
doi = "10.18653/v1/2024.emnlp-main.529",
pages = "9431--9442",
abstract = "Intermediate task transfer learning can greatly improve model performance. If, for example, one has little training data for emotion detection, first fine-tuning a language model on a sentiment classification dataset may improve performance strongly. But which task to choose for transfer learning? Prior methods producing useful task rankings are infeasible for large source pools, as they require forward passes through all source language models. We overcome this by introducing Embedding Space Maps (ESMs), light-weight neural networks that approximate the effect of fine-tuning a language model. We conduct the largest study on NLP task transferability and task selection with 12k source-target pairs. We find that applying ESMs on a prior method reduces execution time and disk space usage by factors of 10 and 278, respectively, while retaining high selection performance (avg. regret@5 score of 2.95)."
}
```
**APA:**
```
Schulte, D., Hamborg, F., & Akbik, A. (2024, November). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 9431-9442).
```
## Additional Information
|
bitsanlp/simcse_finetuned_500k | bitsanlp | "2022-12-05T02:13:12Z" | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-12-05T01:46:52Z" | ---
tags:
- generated_from_trainer
model-index:
- name: simcse_finetuned_500k
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. -->
# simcse_finetuned_500k
This model is a fine-tuned version of [bitsanlp/simcse_retrain_edos_500k](https://huggingface.co/bitsanlp/simcse_retrain_edos_500k) on an unknown dataset.
## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 28
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF | Abirate | "2024-04-13T14:46:28Z" | 6 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-04-13T14:45:57Z" | ---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF
This model was converted to GGUF format from [`Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups`](https://huggingface.co/Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups) 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/Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF --model gemma-1.1-7b-it-finetuned-on-kaggle-writeups.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF --model gemma-1.1-7b-it-finetuned-on-kaggle-writeups.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.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m gemma-1.1-7b-it-finetuned-on-kaggle-writeups.Q4_K_M.gguf -n 128
```
|
samoline/f5cc865b-a7c9-4005-9366-09994782f648 | samoline | "2025-03-22T12:42:47Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:adapter:sethuiyer/Medichat-Llama3-8B",
"license:other",
"region:us"
] | null | "2025-03-22T12:27:06Z" | ---
library_name: peft
license: other
base_model: sethuiyer/Medichat-Llama3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f5cc865b-a7c9-4005-9366-09994782f648
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: sethuiyer/Medichat-Llama3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- c9601e2820367a8f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c9601e2820367a8f_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: false
group_by_length: false
hub_model_id: samoline/f5cc865b-a7c9-4005-9366-09994782f648
hub_repo: samoline
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 4
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 4
lora_target_linear: true
lr_scheduler: cosine
max_steps: 2
micro_batch_size: 1
mlflow_experiment_name: /tmp/c9601e2820367a8f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: samoline-nan
wandb_mode: online
wandb_name: 6ff84017-f5f6-493b-8fd8-1985d6c9a0ff
wandb_project: Gradients-On-Demand
wandb_run: dev
wandb_runid: 6ff84017-f5f6-493b-8fd8-1985d6c9a0ff
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# f5cc865b-a7c9-4005-9366-09994782f648
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9754
## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.277 | 0.0000 | 1 | 1.9761 |
| 1.0946 | 0.0000 | 2 | 1.9754 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mrpojam/Llama3.2-1B-De2Fr-Translation | mrpojam | "2025-03-13T15:07:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-03-13T14:44:09Z" | ---
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] |
scjones/distilbert-base-uncased-finetuned-emotion | scjones | "2022-06-21T00:16:41Z" | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-06-20T23:43:04Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9315
- name: F1
type: f1
value: 0.9317528216385311
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1630
- Accuracy: 0.9315
- F1: 0.9318
## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.2115 | 1.0 | 250 | 0.1696 | 0.93 | 0.9295 |
| 0.1376 | 2.0 | 500 | 0.1630 | 0.9315 | 0.9318 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ctranslate2-4you/Mistral-Nemo-Instruct-2407-ct2-int8 | ctranslate2-4you | "2024-10-22T16:55:11Z" | 10 | 0 | null | [
"safetensors",
"base_model:mistralai/Mistral-Nemo-Instruct-2407",
"base_model:finetune:mistralai/Mistral-Nemo-Instruct-2407",
"region:us"
] | null | "2024-10-22T13:56:05Z" | ---
base_model:
- mistralai/Mistral-Nemo-Instruct-2407
---
Ctranslate2 conversion of the model located at [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407)
Conversion script with graphical user interface can be downloaded [HERE](https://github.com/BBC-Esq/Ctranslate2-Converter)
## Tested with Ctranslate 4.4.0 and Torch 2.2.2
- NOTE: Ctranslate2 will soon release version 4.5.0, which will require greater than Torch 2.2.2.
## Example Usage:
```
import os
import sys
import ctranslate2
import gc
import torch
from transformers import AutoTokenizer
system_message = "You are a helpful person who answers questions."
user_message = "Hello, how are you today? I'd like you to write me a funny poem that is a parody of Milton's Paradise Lost if you are familiar with that famous epic poem?"
model_dir = r"D:\Scripts\bench_chat\models\mistralai--Mistral-Nemo-Instruct-2407-ct2-int8"
def build_prompt_mistral_nemo():
prompt = f"""<s>
[INST]{system_message}
{user_message}[/INST]"""
return prompt
def main():
model_name = os.path.basename(model_dir)
print(f"\033[32mLoading the model: {model_name}...\033[0m")
intra_threads = max(os.cpu_count() - 4, 4)
generator = ctranslate2.Generator(
model_dir,
device="cuda",
compute_type="int8",
intra_threads=intra_threads
)
tokenizer = AutoTokenizer.from_pretrained(model_dir, add_prefix_space=None)
prompt = build_prompt_mistral_nemo()
tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))
results_batch = generator.generate_batch(
[tokens],
include_prompt_in_result=False,
max_batch_size=4096,
batch_type="tokens",
beam_size=1,
num_hypotheses=1,
max_length=512,
sampling_temperature=0.0,
)
output = tokenizer.decode(results_batch[0].sequences_ids[0])
print("\nGenerated response:")
print(output)
del generator
del tokenizer
torch.cuda.empty_cache()
gc.collect()
if __name__ == "__main__":
main()
``` |
nksaisrinivas/llama3_finetuned_lora | nksaisrinivas | "2025-03-03T06:50:31Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | "2025-03-03T06:50:27Z" | ---
base_model: meta-llama/Llama-3.2-1B-instruct
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 |
fahuamancaja/whisper-small-es | fahuamancaja | "2024-03-08T12:52:19Z" | 62 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"es",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-03-06T12:27:53Z" | ---
language:
- es
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Es - Spanish Sampler
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: es
split: test
args: es
metrics:
- name: Wer
type: wer
value: 11.316615023383822
---
<!-- 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 Small Es - Spanish Sampler
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2701
- Wer Ortho: 16.7756
- Wer: 11.3166
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.241 | 0.03 | 500 | 0.2701 | 16.7756 | 11.3166 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2 | stefan-it | "2023-10-26T10:56:05Z" | 3 | 0 | flair | [
"flair",
"pytorch",
"tensorboard",
"token-classification",
"sequence-tagger-model",
"fr",
"base_model:dbmdz/bert-base-historic-multilingual-64k-td-cased",
"base_model:finetune:dbmdz/bert-base-historic-multilingual-64k-td-cased",
"license:mit",
"region:us"
] | token-classification | "2023-10-23T19:29:35Z" | ---
language: fr
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
base_model: dbmdz/bert-base-historic-multilingual-64k-td-cased
widget:
- text: — 469 . Πεδία . Les tribraques formés par un seul mot sont rares chez les
tragiques , partont ailleurs qu ’ au premier pied . CÉ . cependant QEd , Roi ,
719 , 826 , 4496 .
---
# Fine-tuned Flair Model on AjMC French NER Dataset (HIPE-2022)
This Flair model was fine-tuned on the
[AjMC French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md)
NER Dataset using hmBERT 64k as backbone LM.
The AjMC dataset consists of NE-annotated historical commentaries in the field of Classics,
and was created in the context of the [Ajax MultiCommentary](https://mromanello.github.io/ajax-multi-commentary/)
project.
The following NEs were annotated: `pers`, `work`, `loc`, `object`, `date` and `scope`.
# Results
We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:
* Batch Sizes: `[4, 8]`
* Learning Rates: `[3e-05, 5e-05]`
And report micro F1-score on development set:
| Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average |
|-------------------|--------------|-----------------|--------------|--------------|--------------|-----------------|
| `bs4-e10-lr3e-05` | [0.8586][1] | [0.8586][2] | [0.8688][3] | [0.8539][4] | [0.8529][5] | 0.8586 ± 0.0063 |
| `bs8-e10-lr5e-05` | [0.8539][6] | [**0.8653**][7] | [0.8518][8] | [0.8536][9] | [0.8374][10] | 0.8524 ± 0.0099 |
| `bs8-e10-lr3e-05` | [0.8486][11] | [0.8486][12] | [0.8522][13] | [0.8512][14] | [0.8414][15] | 0.8484 ± 0.0042 |
| `bs4-e10-lr5e-05` | [0.8529][16] | [0.8425][17] | [0.8501][18] | [0.8412][19] | [0.8501][20] | 0.8474 ± 0.0052 |
[1]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
[2]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
[3]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
[4]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
[5]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
[6]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
[7]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
[8]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
[9]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
[10]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
[11]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
[12]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
[13]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
[14]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
[15]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
[16]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
[17]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
[18]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
[19]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
[20]: https://hf.co/stefan-it/hmbench-ajmc-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
The [training log](training.log) and TensorBoard logs (not available for hmBERT Base model) are also uploaded to the model hub.
More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench).
# Acknowledgements
We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and
[Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models.
Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
Many Thanks for providing access to the TPUs ❤️
|
HPLT/hplt_bert_base_2_0_slv-Latn | HPLT | "2025-03-19T12:52:19Z" | 21 | 0 | null | [
"pytorch",
"BERT",
"HPLT",
"encoder",
"custom_code",
"sl",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
] | null | "2025-02-22T22:29:21Z" | ---
language:
- sl
inference: false
tags:
- BERT
- HPLT
- encoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
---
# HPLT v2.0 BERT for Slovenian
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the encoder-only monolingual language models trained as a second release by the [HPLT project](https://hplt-project.org/).
It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
We present monolingual LTG-BERT models for more than 50 languages out of 191 total in the [HPLT v2.0 dataset](https://hplt-project.org/datasets/v2.0).
All the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:
- hidden size: 768
- attention heads: 12
- layers: 12
- vocabulary size: 32768
Every model uses its own tokenizer trained on language-specific HPLT data.
[The training code](https://github.com/hplt-project/HPLT-WP4).
[The training statistics of all runs](https://api.wandb.ai/links/ltg/kduj7mjn)
## Example usage (tested with `transformers==4.46.1` and `tokenizers==0.20.1`)
This model currently needs a custom wrapper from `modeling_ltgbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("HPLT/hplt_bert_base_2_0_slv-Latn")
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_slv-Latn", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("It's a beautiful[MASK].", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] It's a beautiful place.[SEP]'
print(tokenizer.decode(output_text[0].tolist(), clean_up_tokenization_spaces=True))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Intermediate checkpoints
We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
You can load a specific model revision with `transformers` using the argument `revision`:
```python
model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_2_0_slv-Latn", revision="step21875", trust_remote_code=True)
```
You can access all the revisions for the models with the following code:
```python
from huggingface_hub import list_repo_refs
out = list_repo_refs("HPLT/hplt_bert_base_2_0_slv-Latn")
print([b.name for b in out.branches])
```
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-trained,
title = "Trained on 100 million words and still in shape: {BERT} meets {B}ritish {N}ational {C}orpus",
author = "Samuel, David and
Kutuzov, Andrey and
{\O}vrelid, Lilja and
Velldal, Erik",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.146",
doi = "10.18653/v1/2023.findings-eacl.146",
pages = "1954--1974"
}
```
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
```
|
Arkajyoti/Arkajyoti-Mistral-7B-v0.1-nli-random-standardized-many-random-names-easy | Arkajyoti | "2024-07-29T21:02:55Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-07-29T19:29:02Z" | ---
library_name: transformers
tags:
- trl
- sft
---
# 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] |
zelk12/MT2-MMMA-gemma-2-9B | zelk12 | "2024-10-14T16:10:48Z" | 17 | 1 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:zelk12/MT2-MA-gemma-2-RPMHv0.1Rv0.3-9B",
"base_model:merge:zelk12/MT2-MA-gemma-2-RPMHv0.1Rv0.3-9B",
"base_model:zelk12/MT2-MM-gemma-2-Rv0.4RAt0.25v0.1-9B",
"base_model:merge:zelk12/MT2-MM-gemma-2-Rv0.4RAt0.25v0.1-9B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-14T16:04:30Z" | ---
base_model:
- zelk12/MT2-MM-gemma-2-Rv0.4RAt0.25v0.1-9B
- zelk12/MT2-MA-gemma-2-RPMHv0.1Rv0.3-9B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [zelk12/MT2-MM-gemma-2-Rv0.4RAt0.25v0.1-9B](https://huggingface.co/zelk12/MT2-MM-gemma-2-Rv0.4RAt0.25v0.1-9B)
* [zelk12/MT2-MA-gemma-2-RPMHv0.1Rv0.3-9B](https://huggingface.co/zelk12/MT2-MA-gemma-2-RPMHv0.1Rv0.3-9B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: zelk12/MT2-MM-gemma-2-Rv0.4RAt0.25v0.1-9B
- model: zelk12/MT2-MA-gemma-2-RPMHv0.1Rv0.3-9B
merge_method: slerp
base_model: zelk12/MT2-MM-gemma-2-Rv0.4RAt0.25v0.1-9B
dtype: bfloat16
parameters:
t: 0.5
```
|
MahmoudRox/Paligemma_VQAMED2019 | MahmoudRox | "2024-06-08T17:09:00Z" | 19 | 5 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/paligemma-3b-pt-224",
"base_model:adapter:google/paligemma-3b-pt-224",
"license:gemma",
"region:us"
] | null | "2024-06-01T16:55:29Z" | ---
license: gemma
library_name: peft
tags:
- generated_from_trainer
base_model: google/paligemma-3b-pt-224
model-index:
- name: paligemma_VQAMed
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. -->
# paligemma_VQAMed2019
This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the [VQAMed 2019](https://zenodo.org/records/10499039) dataset.
Fine-tuning code is [here](https://colab.research.google.com/github/mahmoudBidry/Finetune-Google-Paligemma-3B-VQA/blob/main/Fine_tune_PaliGemma_on_VQAMed2019_dataset.ipynb).
## How to use
To use the model, follow the [colab notebook](https://colab.research.google.com/drive/1SfrNNHE32k9kBWdR6U0DQr4LI_AVIAb1?usp=sharing).
Below is a quick example.
To ensure you have the latest version of Transformers, install it using the following command:
```bash
!pip install -qU git+https://github.com/huggingface/transformers.git
```
```python
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
import torch
from PIL import Image
import requests
processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-224")
model = PaliGemmaForConditionalGeneration.from_pretrained("MahmoudRox/Paligemma_VQAMED2019")
prompt = "Which part of the body is in the picture?" #your question
image_file = "https://prod-images-static.radiopaedia.org/images/9289883/1c20962e46c92ee83a3f551adb24fa_big_gallery.jpg" #your image
raw_image = Image.open(requests.get(image_file, stream=True).raw)
def generate_response(prompt, image):
inputs = processor(images=image, text=prompt, return_tensors="pt")
# Check if the attention mask needs to be inverted
attention_mask = inputs['attention_mask']
if torch.max(attention_mask) == 1:
attention_mask = 1 - attention_mask
# Generate a response
outputs = model.generate(
input_ids=inputs['input_ids'],
attention_mask=attention_mask,
pixel_values=inputs['pixel_values'],
max_new_tokens=1,
no_repeat_ngram_size=2
)
# Decode and print the response
decoded_response = processor.decode(outputs[0], skip_special_tokens=True)[len(prompt):]
return decoded_response
print(generate_response(prompt, raw_image))
#spine
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |
opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill | opensearch-project | "2025-02-24T05:01:33Z" | 1,959,514 | 5 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"learned sparse",
"opensearch",
"retrieval",
"passage-retrieval",
"document-expansion",
"bag-of-words",
"en",
"arxiv:2411.04403",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-07-17T07:51:35Z" | ---
language: en
license: apache-2.0
tags:
- learned sparse
- opensearch
- transformers
- retrieval
- passage-retrieval
- document-expansion
- bag-of-words
---
# opensearch-neural-sparse-encoding-doc-v2-distill
## Select the model
The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' **zero-shot performance** on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora.
Overall, the v2 series of models have better search relevance, efficiency and inference speed than the v1 series. The specific advantages and disadvantages may vary across different datasets.
| Model | Inference-free for Retrieval | Model Parameters | AVG NDCG@10 | AVG FLOPS |
|-------|------------------------------|------------------|-------------|-----------|
| [opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1) | | 133M | 0.524 | 11.4 |
| [opensearch-neural-sparse-encoding-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v2-distill) | | 67M | 0.528 | 8.3 |
| [opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) | ✔️ | 133M | 0.490 | 2.3 |
| [opensearch-neural-sparse-encoding-doc-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill) | ✔️ | 67M | 0.504 | 1.8 |
| [opensearch-neural-sparse-encoding-doc-v2-mini](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-mini) | ✔️ | 23M | 0.497 | 1.7 |
## Overview
- **Paper**: [Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers](https://arxiv.org/abs/2411.04403)
- **Fine-tuning sample**: [opensearch-sparse-model-tuning-sample](https://github.com/zhichao-aws/opensearch-sparse-model-tuning-sample)
This is a learned sparse retrieval model. It encodes the documents to 30522 dimensional **sparse vectors**. For queries, it just use a tokenizer and a weight look-up table to generate sparse vectors. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token. And the similarity score is the inner product of query/document sparse vectors.
The training datasets includes MS MARCO, eli5_question_answer, squad_pairs, WikiAnswers, yahoo_answers_title_question, gooaq_pairs, stackexchange_duplicate_questions_body_body, wikihow, S2ORC_title_abstract, stackexchange_duplicate_questions_title-body_title-body, yahoo_answers_question_answer, searchQA_top5_snippets, stackexchange_duplicate_questions_title_title, yahoo_answers_title_answer.
OpenSearch neural sparse feature supports learned sparse retrieval with lucene inverted index. Link: https://opensearch.org/docs/latest/query-dsl/specialized/neural-sparse/. The indexing and search can be performed with OpenSearch high-level API.
## Usage (HuggingFace)
This model is supposed to run inside OpenSearch cluster. But you can also use it outside the cluster, with HuggingFace models API.
```python
import json
import itertools
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
# get sparse vector from dense vectors with shape batch_size * seq_len * vocab_size
def get_sparse_vector(feature, output):
values, _ = torch.max(output*feature["attention_mask"].unsqueeze(-1), dim=1)
values = torch.log(1 + torch.relu(values))
values[:,special_token_ids] = 0
return values
# transform the sparse vector to a dict of (token, weight)
def transform_sparse_vector_to_dict(sparse_vector):
sample_indices,token_indices=torch.nonzero(sparse_vector,as_tuple=True)
non_zero_values = sparse_vector[(sample_indices,token_indices)].tolist()
number_of_tokens_for_each_sample = torch.bincount(sample_indices).cpu().tolist()
tokens = [transform_sparse_vector_to_dict.id_to_token[_id] for _id in token_indices.tolist()]
output = []
end_idxs = list(itertools.accumulate([0]+number_of_tokens_for_each_sample))
for i in range(len(end_idxs)-1):
token_strings = tokens[end_idxs[i]:end_idxs[i+1]]
weights = non_zero_values[end_idxs[i]:end_idxs[i+1]]
output.append(dict(zip(token_strings, weights)))
return output
# download the idf file from model hub. idf is used to give weights for query tokens
def get_tokenizer_idf(tokenizer):
from huggingface_hub import hf_hub_download
local_cached_path = hf_hub_download(repo_id="opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill", filename="idf.json")
with open(local_cached_path) as f:
idf = json.load(f)
idf_vector = [0]*tokenizer.vocab_size
for token,weight in idf.items():
_id = tokenizer._convert_token_to_id_with_added_voc(token)
idf_vector[_id]=weight
return torch.tensor(idf_vector)
# load the model
model = AutoModelForMaskedLM.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill")
tokenizer = AutoTokenizer.from_pretrained("opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill")
idf = get_tokenizer_idf(tokenizer)
# set the special tokens and id_to_token transform for post-process
special_token_ids = [tokenizer.vocab[token] for token in tokenizer.special_tokens_map.values()]
get_sparse_vector.special_token_ids = special_token_ids
id_to_token = ["" for i in range(tokenizer.vocab_size)]
for token, _id in tokenizer.vocab.items():
id_to_token[_id] = token
transform_sparse_vector_to_dict.id_to_token = id_to_token
query = "What's the weather in ny now?"
document = "Currently New York is rainy."
# encode the query
feature_query = tokenizer([query], padding=True, truncation=True, return_tensors='pt', return_token_type_ids=False)
input_ids = feature_query["input_ids"]
batch_size = input_ids.shape[0]
query_vector = torch.zeros(batch_size, tokenizer.vocab_size)
query_vector[torch.arange(batch_size).unsqueeze(-1), input_ids] = 1
query_sparse_vector = query_vector*idf
# encode the document
feature_document = tokenizer([document], padding=True, truncation=True, return_tensors='pt', return_token_type_ids=False)
output = model(**feature_document)[0]
document_sparse_vector = get_sparse_vector(feature_document, output)
# get similarity score
sim_score = torch.matmul(query_sparse_vector[0],document_sparse_vector[0])
print(sim_score) # tensor(17.5307, grad_fn=<DotBackward0>)
query_token_weight = transform_sparse_vector_to_dict(query_sparse_vector)[0]
document_query_token_weight = transform_sparse_vector_to_dict(document_sparse_vector)[0]
for token in sorted(query_token_weight, key=lambda x:query_token_weight[x], reverse=True):
if token in document_query_token_weight:
print("score in query: %.4f, score in document: %.4f, token: %s"%(query_token_weight[token],document_query_token_weight[token],token))
# result:
# score in query: 5.7729, score in document: 1.4109, token: ny
# score in query: 4.5684, score in document: 1.4673, token: weather
# score in query: 3.5895, score in document: 0.7473, token: now
```
The above code sample shows an example of neural sparse search. Although there is no overlap token in original query and document, but this model performs a good match.
## Detailed Search Relevance
<div style="overflow-x: auto;">
| Model | Average | Trec Covid | NFCorpus | NQ | HotpotQA | FiQA | ArguAna | Touche | DBPedia | SCIDOCS | FEVER | Climate FEVER | SciFact | Quora |
|-------|---------|------------|----------|----|----------|------|---------|--------|---------|---------|-------|---------------|---------|-------|
| [opensearch-neural-sparse-encoding-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v1) | 0.524 | 0.771 | 0.360 | 0.553 | 0.697 | 0.376 | 0.508 | 0.278 | 0.447 | 0.164 | 0.821 | 0.263 | 0.723 | 0.856 |
| [opensearch-neural-sparse-encoding-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-v2-distill) | 0.528 | 0.775 | 0.347 | 0.561 | 0.685 | 0.374 | 0.551 | 0.278 | 0.435 | 0.173 | 0.849 | 0.249 | 0.722 | 0.863 |
| [opensearch-neural-sparse-encoding-doc-v1](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v1) | 0.490 | 0.707 | 0.352 | 0.521 | 0.677 | 0.344 | 0.461 | 0.294 | 0.412 | 0.154 | 0.743 | 0.202 | 0.716 | 0.788 |
| [opensearch-neural-sparse-encoding-doc-v2-distill](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill) | 0.504 | 0.690 | 0.343 | 0.528 | 0.675 | 0.357 | 0.496 | 0.287 | 0.418 | 0.166 | 0.818 | 0.224 | 0.715 | 0.841 |
| [opensearch-neural-sparse-encoding-doc-v2-mini](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v2-mini) | 0.497 | 0.709 | 0.336 | 0.510 | 0.666 | 0.338 | 0.480 | 0.285 | 0.407 | 0.164 | 0.812 | 0.216 | 0.699 | 0.837 |
</div>
## License
This project is licensed under the [Apache v2.0 License](https://github.com/opensearch-project/neural-search/blob/main/LICENSE).
## Copyright
Copyright OpenSearch Contributors. See [NOTICE](https://github.com/opensearch-project/neural-search/blob/main/NOTICE) for details. |
aravindhank/tiny-bart-sst2-distilled | aravindhank | "2024-04-28T17:29:34Z" | 120 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:aravindhank/valuenet-bart-base",
"base_model:finetune:aravindhank/valuenet-bart-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-04-26T08:18:18Z" | ---
base_model: aravindhank/valuenet-bart-base
tags:
- generated_from_trainer
model-index:
- name: tiny-bart-sst2-distilled
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. -->
# tiny-bart-sst2-distilled
This model is a fine-tuned version of [aravindhank/valuenet-bart-base](https://huggingface.co/aravindhank/valuenet-bart-base) on the None dataset.
## 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: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
fabiancpl/nlbse25_pharo | fabiancpl | "2024-12-13T02:21:28Z" | 25 | 0 | setfit | [
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:NLBSE/nlbse25_pharo",
"base_model:finetune:NLBSE/nlbse25_pharo",
"region:us"
] | text-classification | "2024-12-13T02:21:25Z" | ---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget: []
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: NLBSE/nlbse25_pharo
---
# SetFit with NLBSE/nlbse25_pharo
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [NLBSE/nlbse25_pharo](https://huggingface.co/NLBSE/nlbse25_pharo) as the Sentence Transformer embedding model. A RandomForestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [NLBSE/nlbse25_pharo](https://huggingface.co/NLBSE/nlbse25_pharo)
- **Classification head:** a RandomForestClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("fabiancpl/nlbse25_pharo")
# Run inference
preds = model("I loved the spiderman movie!")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## 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
### Framework Versions
- Python: 3.12.4
- SetFit: 1.1.0
- Sentence Transformers: 3.3.0
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu124
- Datasets: 3.1.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
jddqd/Reinforce-1 | jddqd | "2025-02-12T22:04:59Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2025-02-12T22:04:09Z" | ---
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: 422.80 +/- 141.85
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
|
nhoxinh/8c2eeaea-bf4d-4063-ad01-741d4bd84e45 | nhoxinh | "2025-01-13T07:04:52Z" | 10 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-13T06:52:56Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8c2eeaea-bf4d-4063-ad01-741d4bd84e45
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ff4f37673cc248ee_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ff4f37673cc248ee_train_data.json
type:
field_input: content
field_instruction: question
field_output: correct_line
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nhoxinh/8c2eeaea-bf4d-4063-ad01-741d4bd84e45
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/ff4f37673cc248ee_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: aba0d157-7509-4493-873b-9910eab62a7a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: aba0d157-7509-4493-873b-9910eab62a7a
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8c2eeaea-bf4d-4063-ad01-741d4bd84e45
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4461
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5999 | 0.4010 | 200 | 1.4461 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
LHRuig/onoffmenssx | LHRuig | "2025-03-25T19:00:30Z" | 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 | "2025-03-25T19:00:18Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: suit
output:
url: images/suit.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: man
---
# onoffmensx
<Gallery />
## Model description
onoffmensx lora
## Trigger words
You should use `man` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/LHRuig/onoffmenssx/tree/main) them in the Files & versions tab.
|
Triangle104/Skyfall-36B-v2-Q3_K_M-GGUF | Triangle104 | "2025-02-18T09:56:24Z" | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:TheDrummer/Skyfall-36B-v2",
"base_model:quantized:TheDrummer/Skyfall-36B-v2",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-02-18T09:45:50Z" | ---
license: other
base_model: TheDrummer/Skyfall-36B-v2
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Skyfall-36B-v2-Q3_K_M-GGUF
This model was converted to GGUF format from [`TheDrummer/Skyfall-36B-v2`](https://huggingface.co/TheDrummer/Skyfall-36B-v2) 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/TheDrummer/Skyfall-36B-v2) for more details on the model.
---
Skyfall v2 is an upscaled version of Mistral Small 2501 with continued training for creativity and RP.
Supported Chat Templates
-
Mistral v7 Tekken (highly recommended)
Metharme (not recommended)
Alpaca (may be interesting, especially for cyoa / story)
Description
-
Creativity, good writing style, good instruct, chain of thought
capability, mathematics understanding, and solid tool use performance...
This model is peak! This will be my new daily model over all the 70Bs I
have used.
---
## 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 Triangle104/Skyfall-36B-v2-Q3_K_M-GGUF --hf-file skyfall-36b-v2-q3_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Skyfall-36B-v2-Q3_K_M-GGUF --hf-file skyfall-36b-v2-q3_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 Triangle104/Skyfall-36B-v2-Q3_K_M-GGUF --hf-file skyfall-36b-v2-q3_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Skyfall-36B-v2-Q3_K_M-GGUF --hf-file skyfall-36b-v2-q3_k_m.gguf -c 2048
```
|
nathanialhunt/af131d52-bf77-4b0d-bf95-af07bd344220 | nathanialhunt | "2025-02-05T00:42:48Z" | 9 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Phi-3-mini-4k-instruct",
"base_model:adapter:unsloth/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null | "2025-02-05T00:37:24Z" | ---
library_name: peft
license: mit
base_model: unsloth/Phi-3-mini-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: af131d52-bf77-4b0d-bf95-af07bd344220
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
# af131d52-bf77-4b0d-bf95-af07bd344220
This model is a fine-tuned version of [unsloth/Phi-3-mini-4k-instruct](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5761
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
yoavush/FILM_flux | yoavush | "2024-08-22T19:09:31Z" | 8 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"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 | "2024-08-22T18:20:02Z" | ---
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
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
instance_prompt: FILM
---
# Film_Flux
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `FILM` to trigger the image generation.
## 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('yoavush/FILM_flux', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
mradermacher/winter-garden-7b-alpha-GGUF | mradermacher | "2024-05-06T06:12:31Z" | 43 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"conversational",
"multi-task",
"en",
"base_model:maldv/winter-garden-7b-alpha",
"base_model:quantized:maldv/winter-garden-7b-alpha",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | "2024-03-15T15:43:09Z" | ---
base_model: maldv/winter-garden-7b-alpha
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- merge
- conversational
- multi-task
---
## About
static quants of https://huggingface.co/maldv/winter-garden-7b-alpha
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/winter-garden-7b-alpha-GGUF/resolve/main/winter-garden-7b-alpha.Q8_0.gguf) | Q8_0 | 7.9 | 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 -->
|
kalytm/nous-7 | kalytm | "2024-05-20T06:56:48Z" | 170 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T00:04:27Z" | ---
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] |
Zeze24/dqn-SpaceInvadersNoFrameskip-v4 | Zeze24 | "2024-01-21T11:53:59Z" | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-01-21T11:53:22Z" | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 432.00 +/- 124.26
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Zeze24 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Zeze24 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Zeze24
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Edgar404/Reinforce-001 | Edgar404 | "2024-04-30T11:14:59Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2024-04-30T11:14:40Z" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-001
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
|
Realgon/left_padding70model | Realgon | "2023-11-27T07:15:40Z" | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-11-07T17:44:24Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: left_padding70model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93092
---
<!-- 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. -->
# left_padding70model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9309
- Loss: 0.7142
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.0473 | 1.0 | 1563 | 0.9279 | 0.4618 |
| 0.0096 | 2.0 | 3126 | 0.929 | 0.5406 |
| 0.0328 | 3.0 | 4689 | 0.92 | 0.5954 |
| 0.0192 | 4.0 | 6252 | 0.9288 | 0.5570 |
| 0.0171 | 5.0 | 7815 | 0.9294 | 0.5905 |
| 0.006 | 6.0 | 9378 | 0.9301 | 0.6330 |
| 0.0084 | 7.0 | 10941 | 0.9270 | 0.6311 |
| 0.0003 | 8.0 | 12504 | 0.9288 | 0.6783 |
| 0.0048 | 9.0 | 14067 | 0.9315 | 0.6987 |
| 0.0001 | 10.0 | 15630 | 0.9309 | 0.7142 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|
MetaIX/GPT4-X-Alpaca-30B-4bit | MetaIX | "2023-05-27T13:33:42Z" | 1,504 | 162 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-04-14T17:23:57Z" | <p><strong><font size="5">Information</font></strong></p>
GPT4-X-Alpaca 30B 4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.
<p>This was made using <a href="https://huggingface.co/chansung/gpt4-alpaca-lora-30b">Chansung's GPT4-Alpaca Lora</a></p>
<p><strong><font size="5">Update 05.26.2023</font></strong></p>
<p>Updated the ggml quantizations to be compatible with the latest version of llamacpp (again).</p>
<p><strong>What's included</strong></p>
<P>GPTQ: 2 quantized versions. One quantized --true-sequential and act-order optimizations, and the other was quantized using --true-sequential --groupsize 128 optimizations</P>
<P>GGML: 3 quantized versions. One quantized using q4_1, another one was quantized using q5_0, and the last one was quantized using q5_1.</P>
<p><strong>GPU/GPTQ Usage</strong></p>
<p>To use with your GPU using GPTQ pick one of the .safetensors along with all of the .jsons and .model files.</p>
<p>Oobabooga: If you require further instruction, see <a href="https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md">here</a> and <a href="https://github.com/oobabooga/text-generation-webui/blob/main/docs/LLaMA-model.md">here</a></p>
<p>KoboldAI: If you require further instruction, see <a href="https://github.com/0cc4m/KoboldAI">here</a></p>
<p><strong>CPU/GGML Usage</strong></p>
<p>To use your CPU using GGML(Llamacpp) you only need the single .bin ggml file.</p>
<p>Oobabooga: If you require further instruction, see <a href="https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md">here</a></p>
<p>KoboldAI: If you require further instruction, see <a href="https://github.com/LostRuins/koboldcpp">here</a></p>
<p><strong>Training Parameters</strong></p>
<ul><li>num_epochs=10</li><li>cutoff_len=512</li><li>group_by_length</li><li>lora_target_modules='[q_proj,k_proj,v_proj,o_proj]'</li><li>lora_r=16</li><li>micro_batch_size=8</li></ul>
<p><strong><font size="5">Benchmarks</font></strong></p>
<p><strong><font size="4">--true-sequential --act-order</font></strong></p>
<strong>Wikitext2</strong>: 4.481280326843262
<strong>Ptb-New</strong>: 8.539161682128906
<strong>C4-New</strong>: 6.451964855194092
<strong>Note</strong>: This version does not use <i>--groupsize 128</i>, therefore evaluations are minimally higher. However, this version allows fitting the whole model at full context using only 24GB VRAM.
<p><strong><font size="4">--true-sequential --groupsize 128</font></strong></p>
<strong>Wikitext2</strong>: 4.285132884979248
<strong>Ptb-New</strong>: 8.34856128692627
<strong>C4-New</strong>: 6.292652130126953
<strong>Note</strong>: This version uses <i>--groupsize 128</i>, resulting in better evaluations. However, it consumes more VRAM. |
lesso03/96189575-87dd-4039-9b9c-2b857e3aecce | lesso03 | "2025-01-10T12:24:35Z" | 13 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NovaSearch/stella_en_1.5B_v5",
"base_model:adapter:NovaSearch/stella_en_1.5B_v5",
"license:mit",
"region:us"
] | null | "2025-01-10T12:01:21Z" | ---
library_name: peft
license: mit
base_model: dunzhang/stella_en_1.5B_v5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 96189575-87dd-4039-9b9c-2b857e3aecce
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: dunzhang/stella_en_1.5B_v5
bf16: true
chat_template: llama3
datasets:
- data_files:
- c16dc9cb46034ec9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c16dc9cb46034ec9_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso03/96189575-87dd-4039-9b9c-2b857e3aecce
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 70GiB
max_steps: 30
micro_batch_size: 4
mlflow_experiment_name: /tmp/c16dc9cb46034ec9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 20
save_strategy: steps
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9baba420-c84e-4ea6-8fe4-a4ce0fd08525
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9baba420-c84e-4ea6-8fe4-a4ce0fd08525
warmup_steps: 5
weight_decay: 0.01
xformers_attention: false
```
</details><br>
# 96189575-87dd-4039-9b9c-2b857e3aecce
This model is a fine-tuned version of [dunzhang/stella_en_1.5B_v5](https://huggingface.co/dunzhang/stella_en_1.5B_v5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0004 | 4 | nan |
| 0.0 | 0.0008 | 8 | nan |
| 0.0 | 0.0012 | 12 | nan |
| 0.0 | 0.0017 | 16 | nan |
| 0.0 | 0.0021 | 20 | nan |
| 0.0 | 0.0025 | 24 | nan |
| 0.0 | 0.0029 | 28 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mah92/SalamTTS | mah92 | "2025-03-21T10:36:26Z" | 0 | 1 | null | [
"fa",
"en",
"dataset:mah92/Khadijah-FA_EN-Public-Phone-Audio-Dataset",
"dataset:mah92/Musa-FA_EN-Public-Phone-Audio-Dataset",
"base_model:mah92/Khadijah-FA_EN-Matcha-TTS-Model",
"base_model:finetune:mah92/Khadijah-FA_EN-Matcha-TTS-Model",
"license:cc0-1.0",
"region:us"
] | null | "2025-03-21T07:45:29Z" | ---
license: cc0-1.0
datasets:
- mah92/Khadijah-FA_EN-Public-Phone-Audio-Dataset
- mah92/Musa-FA_EN-Public-Phone-Audio-Dataset
language:
- fa
- en
base_model:
- mah92/Khadijah-FA_EN-Matcha-TTS-Model
- mah92/Musa-FA_EN-Matcha-TTS-Model
---
# Besm ALLAH
# SalamTTS-v9
This repository contains the following APK files:
- **SalamTTS-v9-Khadijah.apk**: [Download](https://huggingface.co/mah92/SalamTTS/blob/main/SalamTTS-v9-Khadijah.apk)
- **SalamTTS-v9-Musa.apk**: [Download](https://huggingface.co/mah92/SalamTTS/blob/main/SalamTTS-v9-Musa.apk) |
davidschulte/ESM_nala-cub__americas_nli_shp | davidschulte | "2025-03-26T13:28:40Z" | 16 | 0 | null | [
"safetensors",
"embedding_space_map",
"BaseLM:bert-base-multilingual-uncased",
"dataset:nala-cub/americas_nli",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-11-10T13:50:07Z" | ---
base_model: bert-base-multilingual-uncased
datasets:
- nala-cub/americas_nli
license: apache-2.0
tags:
- embedding_space_map
- BaseLM:bert-base-multilingual-uncased
---
# ESM nala-cub/americas_nli
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
ESM
- **Developed by:** David Schulte
- **Model type:** ESM
- **Base Model:** bert-base-multilingual-uncased
- **Intermediate Task:** nala-cub/americas_nli
- **ESM architecture:** linear
- **ESM embedding dimension:** 768
- **Language(s) (NLP):** [More Information Needed]
- **License:** Apache-2.0 license
- **ESM version:** 0.1.0
## Training Details
### Intermediate Task
- **Task ID:** nala-cub/americas_nli
- **Subset [optional]:** shp
- **Text Column:** ['premise', 'hypothesis']
- **Label Column:** label
- **Dataset Split:** test
- **Sample size [optional]:** 750
- **Sample seed [optional]:**
### Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Language Model Training Hyperparameters [optional]
- **Epochs:** 3
- **Batch size:** 32
- **Learning rate:** 2e-05
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### ESM Training Hyperparameters [optional]
- **Epochs:** 10
- **Batch size:** 32
- **Learning rate:** 0.001
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### Additional trainiung details [optional]
## Model evaluation
### Evaluation of fine-tuned language model [optional]
### Evaluation of ESM [optional]
MSE:
### Additional evaluation details [optional]
## What are Embedding Space Maps used for?
Embedding Space Maps are a part of ESM-LogME, a efficient method for finding intermediate datasets for transfer learning. There are two reasons to use ESM-LogME:
### You don't have enough training data for your problem
If you don't have a enough training data for your problem, just use ESM-LogME to find more.
You can supplement model training by including publicly available datasets in the training process.
1. Fine-tune a language model on suitable intermediate dataset.
2. Fine-tune the resulting model on your target dataset.
This workflow is called intermediate task transfer learning and it can significantly improve the target performance.
But what is a suitable dataset for your problem? ESM-LogME enable you to quickly rank thousands of datasets on the Hugging Face Hub by how well they are exptected to transfer to your target task.
### You want to find similar datasets to your target dataset
Using ESM-LogME can be used like search engine on the Hugging Face Hub. You can find similar tasks to your target task without having to rely on heuristics. ESM-LogME estimates how language models fine-tuned on each intermediate task would benefinit your target task. This quantitative approach combines the effects of domain similarity and task similarity.
## How can I use ESM-LogME / ESMs?
[](https://pypi.org/project/hf-dataset-selector)
We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps.
**hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub.
```python
from hfselect import Dataset, compute_task_ranking
# Load target dataset from the Hugging Face Hub
dataset = Dataset.from_hugging_face(
name="stanfordnlp/imdb",
split="train",
text_col="text",
label_col="label",
is_regression=False,
num_examples=1000,
seed=42
)
# Fetch ESMs and rank tasks
task_ranking = compute_task_ranking(
dataset=dataset,
model_name="bert-base-multilingual-uncased"
)
# Display top 5 recommendations
print(task_ranking[:5])
```
```python
1. davanstrien/test_imdb_embedd2 Score: -0.618529
2. davanstrien/test_imdb_embedd Score: -0.618644
3. davanstrien/test1 Score: -0.619334
4. stanfordnlp/imdb Score: -0.619454
5. stanfordnlp/sst Score: -0.62995
```
| Rank | Task ID | Task Subset | Text Column | Label Column | Task Split | Num Examples | ESM Architecture | Score |
|-------:|:------------------------------|:----------------|:--------------|:---------------|:-------------|---------------:|:-------------------|----------:|
| 1 | davanstrien/test_imdb_embedd2 | default | text | label | train | 10000 | linear | -0.618529 |
| 2 | davanstrien/test_imdb_embedd | default | text | label | train | 10000 | linear | -0.618644 |
| 3 | davanstrien/test1 | default | text | label | train | 10000 | linear | -0.619334 |
| 4 | stanfordnlp/imdb | plain_text | text | label | train | 10000 | linear | -0.619454 |
| 5 | stanfordnlp/sst | dictionary | phrase | label | dictionary | 10000 | linear | -0.62995 |
| 6 | stanfordnlp/sst | default | sentence | label | train | 8544 | linear | -0.63312 |
| 7 | kuroneko5943/snap21 | CDs_and_Vinyl_5 | sentence | label | train | 6974 | linear | -0.634365 |
| 8 | kuroneko5943/snap21 | Video_Games_5 | sentence | label | train | 6997 | linear | -0.638787 |
| 9 | kuroneko5943/snap21 | Movies_and_TV_5 | sentence | label | train | 6989 | linear | -0.639068 |
| 10 | fancyzhx/amazon_polarity | amazon_polarity | content | label | train | 10000 | linear | -0.639718 |
For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). We provide documentation further documentation and tutorials for finding intermediate datasets and training your own ESMs.
## How do Embedding Space Maps work?
<!-- This section describes the evaluation protocols and provides the results. -->
Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text.
ESMs can be used for intermediate task selection with the ESM-LogME workflow.
## How can I use Embedding Space Maps for Intermediate Task Selection?
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you are using this Embedding Space Maps, please cite our [paper](https://aclanthology.org/2024.emnlp-main.529/).
**BibTeX:**
```
@inproceedings{schulte-etal-2024-less,
title = "Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning",
author = "Schulte, David and
Hamborg, Felix and
Akbik, Alan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.529/",
doi = "10.18653/v1/2024.emnlp-main.529",
pages = "9431--9442",
abstract = "Intermediate task transfer learning can greatly improve model performance. If, for example, one has little training data for emotion detection, first fine-tuning a language model on a sentiment classification dataset may improve performance strongly. But which task to choose for transfer learning? Prior methods producing useful task rankings are infeasible for large source pools, as they require forward passes through all source language models. We overcome this by introducing Embedding Space Maps (ESMs), light-weight neural networks that approximate the effect of fine-tuning a language model. We conduct the largest study on NLP task transferability and task selection with 12k source-target pairs. We find that applying ESMs on a prior method reduces execution time and disk space usage by factors of 10 and 278, respectively, while retaining high selection performance (avg. regret@5 score of 2.95)."
}
```
**APA:**
```
Schulte, D., Hamborg, F., & Akbik, A. (2024, November). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 9431-9442).
```
## Additional Information
|
DarkSM/AdamRaguseaRVC | DarkSM | "2023-10-06T15:34:22Z" | 0 | 0 | null | [
"en",
"region:us"
] | null | "2023-10-06T15:33:16Z" | ---
language:
- en
---
Do not credit me for the model, but do not steal also :b |
kazeric/whisper-small-dv-streaming | kazeric | "2025-03-10T18:56:34Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2025-03-10T13:15:31Z" | ---
library_name: transformers
language:
- dv
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper_Small_Dhivehi_Streaming
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- name: Wer
type: wer
value: 14.62600410334875
---
<!-- 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_Small_Dhivehi_Streaming
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2024
- Wer Ortho: 68.1454
- Wer: 14.6260
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.1315 | 2.328 | 500 | 0.2024 | 68.1454 | 14.6260 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
featherless-ai-quants/gordicaleksa-YugoGPT-GGUF | featherless-ai-quants | "2024-11-04T21:05:48Z" | 22 | 0 | null | [
"gguf",
"text-generation",
"base_model:gordicaleksa/YugoGPT",
"base_model:quantized:gordicaleksa/YugoGPT",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-04T20:20:37Z" | ---
base_model: gordicaleksa/YugoGPT
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# gordicaleksa/YugoGPT GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [gordicaleksa-YugoGPT-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [gordicaleksa-YugoGPT-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [gordicaleksa-YugoGPT-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [gordicaleksa-YugoGPT-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [gordicaleksa-YugoGPT-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [gordicaleksa-YugoGPT-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [gordicaleksa-YugoGPT-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [gordicaleksa-YugoGPT-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [gordicaleksa-YugoGPT-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [gordicaleksa-YugoGPT-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q6_K.gguf) | 5666.79 MB |
| Q8_0 | [gordicaleksa-YugoGPT-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/gordicaleksa/YugoGPT-GGUF/blob/main/gordicaleksa-YugoGPT-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models) |
cantillation/Teamim-small_WeightDecay-0.05_Augmented_New-Data_nusach-yerushalmi_date-24-07-2024 | cantillation | "2024-07-25T04:24:27Z" | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"he",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-07-24T11:05:58Z" | ---
language:
- he
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
metrics:
- wer
model-index:
- name: he-cantillation
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. -->
# he-cantillation
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2759
- Wer: 12.0860
- Avg Precision Exact: 0.9045
- Avg Recall Exact: 0.9054
- Avg F1 Exact: 0.9045
- Avg Precision Letter Shift: 0.9223
- Avg Recall Letter Shift: 0.9233
- Avg F1 Letter Shift: 0.9224
- Avg Precision Word Level: 0.9250
- Avg Recall Word Level: 0.9259
- Avg F1 Word Level: 0.9250
- Avg Precision Word Shift: 0.9777
- Avg Recall Word Shift: 0.9785
- Avg F1 Word Shift: 0.9777
- Precision Median Exact: 1.0
- Recall Median Exact: 1.0
- F1 Median Exact: 1.0
- Precision Max Exact: 1.0
- Recall Max Exact: 1.0
- F1 Max Exact: 1.0
- Precision Min Exact: 0.0
- Recall Min Exact: 0.0
- F1 Min Exact: 0.0
- Precision Min Letter Shift: 0.0
- Recall Min Letter Shift: 0.0
- F1 Min Letter Shift: 0.0
- Precision Min Word Level: 0.0
- Recall Min Word Level: 0.0
- F1 Min Word Level: 0.0
- Precision Min Word Shift: 0.0
- Recall Min Word Shift: 0.0
- F1 Min Word Shift: 0.0
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 200000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Avg Precision Exact | Avg Recall Exact | Avg F1 Exact | Avg Precision Letter Shift | Avg Recall Letter Shift | Avg F1 Letter Shift | Avg Precision Word Level | Avg Recall Word Level | Avg F1 Word Level | Avg Precision Word Shift | Avg Recall Word Shift | Avg F1 Word Shift | Precision Median Exact | Recall Median Exact | F1 Median Exact | Precision Max Exact | Recall Max Exact | F1 Max Exact | Precision Min Exact | Recall Min Exact | F1 Min Exact | Precision Min Letter Shift | Recall Min Letter Shift | F1 Min Letter Shift | Precision Min Word Level | Recall Min Word Level | F1 Min Word Level | Precision Min Word Shift | Recall Min Word Shift | F1 Min Word Shift |
|:-------------:|:-------:|:------:|:---------------:|:--------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------:|:------------:|:-------------------:|:----------------:|:------------:|:--------------------------:|:-----------------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:------------------------:|:---------------------:|:-----------------:|
| No log | 0.0004 | 1 | 6.8177 | 106.5214 | 0.0004 | 0.0012 | 0.0006 | 0.0038 | 0.0036 | 0.0033 | 0.0030 | 0.0121 | 0.0043 | 0.0322 | 0.0342 | 0.0300 | 0.0 | 0.0 | 0.0 | 0.0909 | 0.3333 | 0.1429 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0059 | 3.7023 | 10000 | 0.1748 | 15.8840 | 0.8772 | 0.8813 | 0.8786 | 0.9013 | 0.9056 | 0.9028 | 0.9063 | 0.9103 | 0.9077 | 0.9648 | 0.9693 | 0.9663 | 0.9286 | 0.9375 | 0.9474 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0045 | 7.4047 | 20000 | 0.2047 | 15.1038 | 0.8686 | 0.8670 | 0.8673 | 0.8906 | 0.8892 | 0.8894 | 0.8952 | 0.8935 | 0.8938 | 0.9722 | 0.9711 | 0.9710 | 0.9375 | 0.9333 | 0.9524 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.001 | 11.1070 | 30000 | 0.2024 | 13.8083 | 0.8862 | 0.8876 | 0.8863 | 0.9076 | 0.9094 | 0.9080 | 0.9109 | 0.9127 | 0.9113 | 0.9743 | 0.9767 | 0.9749 | 1.0 | 1.0 | 0.9600 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.001 | 14.8093 | 40000 | 0.2188 | 13.8083 | 0.8924 | 0.8918 | 0.8916 | 0.9125 | 0.9118 | 0.9116 | 0.9166 | 0.9156 | 0.9155 | 0.9733 | 0.9730 | 0.9726 | 1.0 | 1.0 | 0.9630 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0005 | 18.5117 | 50000 | 0.2256 | 13.6464 | 0.8921 | 0.8937 | 0.8924 | 0.9131 | 0.9148 | 0.9135 | 0.9161 | 0.9176 | 0.9164 | 0.9760 | 0.9774 | 0.9762 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0012 | 22.2140 | 60000 | 0.2194 | 12.8515 | 0.8896 | 0.8917 | 0.8902 | 0.9089 | 0.9110 | 0.9095 | 0.9116 | 0.9138 | 0.9122 | 0.9748 | 0.9780 | 0.9759 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0006 | 25.9163 | 70000 | 0.2265 | 13.0870 | 0.8981 | 0.9013 | 0.8992 | 0.9191 | 0.9224 | 0.9203 | 0.9219 | 0.9249 | 0.9229 | 0.9756 | 0.9776 | 0.9761 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0001 | 29.6187 | 80000 | 0.2249 | 13.0870 | 0.8938 | 0.8961 | 0.8945 | 0.9139 | 0.9163 | 0.9146 | 0.9169 | 0.9191 | 0.9175 | 0.9749 | 0.9764 | 0.9752 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0001 | 33.3210 | 90000 | 0.2379 | 13.2342 | 0.8960 | 0.8987 | 0.8969 | 0.9160 | 0.9189 | 0.9169 | 0.9197 | 0.9224 | 0.9206 | 0.9759 | 0.9780 | 0.9764 | 1.0 | 1.0 | 0.9697 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 37.0233 | 100000 | 0.2302 | 13.1312 | 0.8910 | 0.8958 | 0.8930 | 0.9121 | 0.9171 | 0.9142 | 0.9149 | 0.9195 | 0.9167 | 0.9742 | 0.9786 | 0.9759 | 1.0 | 1.0 | 0.9677 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0004 | 40.7257 | 110000 | 0.2294 | 12.9987 | 0.9032 | 0.9028 | 0.9025 | 0.9220 | 0.9216 | 0.9213 | 0.9255 | 0.9249 | 0.9247 | 0.9762 | 0.9773 | 0.9763 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0001 | 44.4280 | 120000 | 0.2322 | 12.6601 | 0.9038 | 0.9045 | 0.9037 | 0.9234 | 0.9242 | 0.9233 | 0.9262 | 0.9270 | 0.9262 | 0.9766 | 0.9784 | 0.9770 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 48.1303 | 130000 | 0.2362 | 12.5129 | 0.9054 | 0.9058 | 0.9051 | 0.9241 | 0.9247 | 0.9239 | 0.9277 | 0.9284 | 0.9276 | 0.9763 | 0.9777 | 0.9766 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 51.8327 | 140000 | 0.2430 | 13.1753 | 0.8973 | 0.8993 | 0.8978 | 0.9184 | 0.9205 | 0.9189 | 0.9216 | 0.9237 | 0.9221 | 0.9766 | 0.9783 | 0.9770 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0005 | 55.5350 | 150000 | 0.2325 | 12.7926 | 0.9032 | 0.9032 | 0.9028 | 0.9226 | 0.9228 | 0.9223 | 0.9251 | 0.9252 | 0.9247 | 0.9781 | 0.9785 | 0.9779 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 59.2373 | 160000 | 0.2428 | 12.2332 | 0.9090 | 0.9104 | 0.9093 | 0.9275 | 0.9289 | 0.9278 | 0.9301 | 0.9315 | 0.9304 | 0.9773 | 0.9791 | 0.9778 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 62.9397 | 170000 | 0.2499 | 12.1301 | 0.9067 | 0.9081 | 0.9070 | 0.9246 | 0.9261 | 0.9249 | 0.9273 | 0.9286 | 0.9275 | 0.9775 | 0.9794 | 0.9780 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0003 | 66.6420 | 180000 | 0.2572 | 12.2185 | 0.9050 | 0.9049 | 0.9045 | 0.9238 | 0.9238 | 0.9234 | 0.9265 | 0.9263 | 0.9260 | 0.9785 | 0.9784 | 0.9780 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 70.3443 | 190000 | 0.2704 | 12.1449 | 0.9058 | 0.9068 | 0.9059 | 0.9237 | 0.9247 | 0.9238 | 0.9263 | 0.9273 | 0.9264 | 0.9775 | 0.9787 | 0.9777 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 74.0466 | 200000 | 0.2759 | 12.0860 | 0.9045 | 0.9054 | 0.9045 | 0.9223 | 0.9233 | 0.9224 | 0.9250 | 0.9259 | 0.9250 | 0.9777 | 0.9785 | 0.9777 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
hgnoi/rrgXZg1mZ2Pdeu9e | hgnoi | "2024-05-25T11:24:00Z" | 77 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-25T11:21:42Z" | ---
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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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### Recommendations
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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
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[More Information Needed]
## Training Details
### Training Data
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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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#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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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Alphatao/1884ae85-c593-4798-9674-0b9af03c13dd | Alphatao | "2025-03-13T14:36:46Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/llama-2-7b-chat",
"base_model:adapter:unsloth/llama-2-7b-chat",
"license:apache-2.0",
"region:us"
] | null | "2025-03-13T10:47:10Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/llama-2-7b-chat
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1884ae85-c593-4798-9674-0b9af03c13dd
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/llama-2-7b-chat
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 92c03c5ab2158f88_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/92c03c5ab2158f88_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
device_map:
? ''
: 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/1884ae85-c593-4798-9674-0b9af03c13dd
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 840
micro_batch_size: 4
mlflow_experiment_name: /tmp/92c03c5ab2158f88_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: c144bd9a-5f78-4e37-b021-d91f2c0c0d5f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c144bd9a-5f78-4e37-b021-d91f2c0c0d5f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 1884ae85-c593-4798-9674-0b9af03c13dd
This model is a fine-tuned version of [unsloth/llama-2-7b-chat](https://huggingface.co/unsloth/llama-2-7b-chat) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1916
## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 840
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8756 | 0.0011 | 1 | 1.7959 |
| 0.2531 | 0.1059 | 100 | 0.2592 |
| 0.2352 | 0.2118 | 200 | 0.2345 |
| 0.1999 | 0.3178 | 300 | 0.2183 |
| 0.1546 | 0.4237 | 400 | 0.2117 |
| 0.291 | 0.5296 | 500 | 0.2037 |
| 0.1577 | 0.6355 | 600 | 0.1983 |
| 0.2267 | 0.7414 | 700 | 0.1929 |
| 0.1747 | 0.8473 | 800 | 0.1916 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/Vera-V1.3-GGUF | mradermacher | "2025-03-30T01:20:40Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Dorian2B/Vera-V1.3",
"base_model:quantized:Dorian2B/Vera-V1.3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-03-30T00:59:20Z" | ---
base_model: Dorian2B/Vera-V1.3
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Dorian2B/Vera-V1.3
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q2_K.gguf) | Q2_K | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q3_K_S.gguf) | Q3_K_S | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q3_K_M.gguf) | Q3_K_M | 1.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q3_K_L.gguf) | Q3_K_L | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.IQ4_XS.gguf) | IQ4_XS | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q4_K_M.gguf) | Q4_K_M | 1.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q5_K_S.gguf) | Q5_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q5_K_M.gguf) | Q5_K_M | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q6_K.gguf) | Q6_K | 2.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.Q8_0.gguf) | Q8_0 | 2.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Vera-V1.3-GGUF/resolve/main/Vera-V1.3.f16.gguf) | f16 | 5.3 | 16 bpw, overkill |
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 -->
|
Nexspear/1d5b3126-a524-4eda-bf0e-be21cea15183 | Nexspear | "2025-01-24T22:50:39Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codegemma-7b-it",
"base_model:adapter:unsloth/codegemma-7b-it",
"license:apache-2.0",
"region:us"
] | null | "2025-01-24T22:25:40Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/codegemma-7b-it
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 1d5b3126-a524-4eda-bf0e-be21cea15183
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/codegemma-7b-it
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4f327ff36134b9ea_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4f327ff36134b9ea_train_data.json
type:
field_input: ''
field_instruction: problem
field_output: solution
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: Nexspear/1d5b3126-a524-4eda-bf0e-be21cea15183
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/4f327ff36134b9ea_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: techspear-hub
wandb_mode: online
wandb_name: b285face-4656-4e7d-8064-270d1ff4db96
wandb_project: Gradients-On-Four
wandb_run: your_name
wandb_runid: b285face-4656-4e7d-8064-270d1ff4db96
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 1d5b3126-a524-4eda-bf0e-be21cea15183
This model is a fine-tuned version of [unsloth/codegemma-7b-it](https://huggingface.co/unsloth/codegemma-7b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4324
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0043 | 1 | 0.5219 |
| 0.4964 | 0.0388 | 9 | 0.4895 |
| 0.446 | 0.0777 | 18 | 0.4565 |
| 0.3904 | 0.1165 | 27 | 0.4438 |
| 0.4331 | 0.1553 | 36 | 0.4393 |
| 0.4545 | 0.1942 | 45 | 0.4371 |
| 0.378 | 0.2330 | 54 | 0.4372 |
| 0.4208 | 0.2718 | 63 | 0.4334 |
| 0.3789 | 0.3107 | 72 | 0.4352 |
| 0.4618 | 0.3495 | 81 | 0.4325 |
| 0.4479 | 0.3883 | 90 | 0.4320 |
| 0.4111 | 0.4272 | 99 | 0.4324 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
abbassix/pn6_800 | abbassix | "2024-01-04T12:34:32Z" | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-01-04T12:33:59Z" | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: pn6_800
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. -->
# pn6_800
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.505
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 100 | 0.7003 | 0.495 |
| No log | 2.0 | 200 | 0.6936 | 0.495 |
| No log | 3.0 | 300 | 0.6932 | 0.505 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
IoanaLivia/whisper-small-finetuned-400-standard-A-epochs-10 | IoanaLivia | "2025-03-17T12:31:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:horoscope_standard_a_400_19_20_5_03",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2025-03-16T19:53:28Z" | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- horoscope_standard_a_400_19_20_5_03
metrics:
- wer
model-index:
- name: whisper-small-finetuned-400-standard-A-epochs-10
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: horoscope_standard_a_400_19_20_5_03
type: horoscope_standard_a_400_19_20_5_03
config: default
split: validation
args: default
metrics:
- name: Wer
type: wer
value: 27.414809121188828
---
<!-- 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-small-finetuned-400-standard-A-epochs-10
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the horoscope_standard_a_400_19_20_5_03 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5064
- Wer: 27.4148
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Use 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 0 | 0 | 0.6890 | 41.0325 |
| 0.1722 | 1.0 | 50 | 0.4912 | 30.1563 |
| 0.0365 | 2.0 | 100 | 0.4825 | 28.5678 |
| 0.0134 | 3.0 | 150 | 0.4958 | 27.7479 |
| 0.0065 | 4.0 | 200 | 0.5046 | 28.3500 |
| 0.0042 | 5.0 | 250 | 0.5026 | 27.6326 |
| 0.0027 | 6.0 | 300 | 0.5018 | 27.5045 |
| 0.0019 | 7.0 | 350 | 0.5035 | 27.4789 |
| 0.0017 | 8.0 | 400 | 0.5048 | 27.3123 |
| 0.0015 | 9.0 | 450 | 0.5060 | 27.4276 |
| 0.0015 | 10.0 | 500 | 0.5064 | 27.4148 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu121
- Datasets 3.4.0
- Tokenizers 0.21.0
|
askenaz/results-7655726778571638724 | askenaz | "2024-02-20T21:25:13Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | "2024-02-20T21:25:05Z" | ---
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-chat-hf
model-index:
- name: results-7655726778571638724
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. -->
# results-7655726778571638724
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## 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: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 |
juliowaissman/q-FrozenLake-v1-4x4-noSlippery | juliowaissman | "2024-01-30T05:22:26Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-01-30T05:09:28Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="juliowaissman/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Kquant03/FrankenDPO-4x7B-GGUF | Kquant03 | "2024-01-18T11:03:27Z" | 10 | 2 | null | [
"gguf",
"merge",
"en",
"arxiv:2101.03961",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-01-16T02:21:03Z" | ---
license: apache-2.0
language:
- en
tags:
- merge
---
# It's alive!!!! Half the size and better on GSM8k and Winogrande than Mixtral Instruct 8x 7B! Also rank 6 on Ayumi's ERP Bench!
A frankenMoE using only DPO models. To be used with Chat-instruct mode enabled.
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Q2_K Tiny](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q2_k.gguf) | Q2_K | 2 | 7.87 GB| 9.87 GB | smallest, significant quality loss - not recommended for most purposes |
| [Q3_K_M](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q3_k_m.gguf) | Q3_K_M | 3 | 10.28 GB| 12.28 GB | very small, high quality loss |
| [Q4_0](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q4_0.gguf) | Q4_0 | 4 | 13.3 GB| 15.3 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Q4_K_M](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q4_k_m.gguf) | Q4_K_M | 4 | 13.32 GB| 15.32 GB | medium, balanced quality - recommended |
| [Q5_0](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q5_0.gguf) | Q5_0 | 5 | 16.24 GB| 18.24 GB | legacy; large, balanced quality |
| [Q5_K_M](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q5_k_m.gguf) | Q5_K_M | 5 | ~16.24 GB| ~18.24 GB | large, balanced quality - recommended |
| [Q6 XL](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q6_k.gguf) | Q6_K | 6 | 19.35 GB| 21.35 GB | very large, extremely minor degradation |
| [Q8 XXL](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q8_0.gguf) | Q8_0 | 8 | 25.1 GB| 27.1 GB | very large, extremely minor degradation - not recommended |
- [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - router
- [udkai/Turdus](https://huggingface.co/udkai/Turdus) - expert #1
- [distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) - expert #2
- [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) - expert #3
- [Neuronovo/neuronovo-9B-v0.3](https://huggingface.co/Neuronovo/neuronovo-9B-v0.3) - expert #4
# "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)"
### (from the MistralAI papers...click the quoted question above to navigate to it directly.)
The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps.
Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining.
So, what exactly is a MoE? In the context of transformer models, a MoE consists of two main elements:
Sparse MoE layers are used instead of dense feed-forward network (FFN) layers. MoE layers have a certain number of “experts” (e.g. 32 in my "frankenMoE"), where each expert is a neural network. In practice, the experts are FFNs, but they can also be more complex networks or even a MoE itself, leading to hierarchical MoEs!
A gate network or router, that determines which tokens are sent to which expert. For example, in the image below, the token “More” is sent to the second expert, and the token "Parameters” is sent to the first network. As we’ll explore later, we can send a token to more than one expert. How to route a token to an expert is one of the big decisions when working with MoEs - the router is composed of learned parameters and is pretrained at the same time as the rest of the network.
At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively.
Switch Layer
MoE layer from the [Switch Transformers paper](https://arxiv.org/abs/2101.03961)
So, to recap, in MoEs we replace every FFN layer of the transformer model with an MoE layer, which is composed of a gate network and a certain number of experts.
Although MoEs provide benefits like efficient pretraining and faster inference compared to dense models, they also come with challenges:
Training: MoEs enable significantly more compute-efficient pretraining, but they’ve historically struggled to generalize during fine-tuning, leading to overfitting.
Inference: Although a MoE might have many parameters, only some of them are used during inference. This leads to much faster inference compared to a dense model with the same number of parameters. However, all parameters need to be loaded in RAM, so memory requirements are high. For example, [given a MoE like Mixtral 8x7B](https://huggingface.co/blog/moe), we’ll need to have enough VRAM to hold a dense 47B parameter model. Why 47B parameters and not 8 x 7B = 56B? That’s because in MoE models, only the FFN layers are treated as individual experts, and the rest of the model parameters are shared. At the same time, assuming just two experts are being used per token, the inference speed (FLOPs) is like using a 12B model (as opposed to a 14B model), because it computes 2x7B matrix multiplications, but with some layers shared (more on this soon).
If all our tokens are sent to just a few popular experts, that will make training inefficient. In a normal MoE training, the gating network converges to mostly activate the same few experts. This self-reinforces as favored experts are trained quicker and hence selected more. To mitigate this, an auxiliary loss is added to encourage giving all experts equal importance. This loss ensures that all experts receive a roughly equal number of training examples. The following sections will also explore the concept of expert capacity, which introduces a threshold of how many tokens can be processed by an expert. In transformers, the auxiliary loss is exposed via the aux_loss parameter.
## "Wait...but you called this a frankenMoE?"
The difference between MoE and "frankenMoE" lies in the fact that the router layer in a model like the one on this repo is not trained simultaneously. |
elemtopos/dqn-SpaceInvadersNoFrameskip-v4 | elemtopos | "2023-09-21T08:46:40Z" | 6 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-09-20T15:49:36Z" | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 270.50 +/- 83.53
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga elemtopos -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga elemtopos -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga elemtopos
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 200000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
usabuts/codegen-350M-mono-python-18k-alpaca | usabuts | "2024-05-31T05:01:44Z" | 106 | 0 | transformers | [
"transformers",
"safetensors",
"codegen",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-31T05:01:10Z" | ---
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]
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OwOpeepeepoopoo/gemmerica_r3_2 | OwOpeepeepoopoo | "2024-03-03T18:37:56Z" | 3 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-03T18:35:46Z" | ---
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]
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## Uses
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### Direct Use
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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. -->
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## Model Examination [optional]
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|
EryriLabs/Llama-3.2-SARA-3b | EryriLabs | "2024-11-17T11:26:05Z" | 7 | 0 | null | [
"safetensors",
"llama",
"en",
"base_model:unsloth/Llama-3.2-3B-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-3B-bnb-4bit",
"license:llama3.2",
"region:us"
] | null | "2024-11-14T20:08:30Z" | ---
license: llama3.2
language:
- en
base_model:
- unsloth/Llama-3.2-3B-bnb-4bit
---
# Llama-3.2-SARA-3b
<figure>
<img src="SARA.png" alt="SARA" width="300">
</figure>
This model is a fine-tuned version of the `unsloth/Llama-3.2-3B-bnb-4bit`, developed to act as SARA—the Security Awareness and Resilience Assistant. SARA is optimized to be a lightweight, offline-friendly AI assistant capable of running on low-spec laptops, designed to provide practical cybersecurity advice in a conversational style.
## Model Details
### Model Description
This model is fine-tuned for conversational question-answering focused on basic cybersecurity topics. It was trained as part of an ongoing blog series (https://www.eryrilabs.co.uk/post/building-sara-a-lightweight-cybersecurity-assistant-for-everyday-laptops) to deliver short, actionable responses suitable for users who want quick guidance on digital safety without needing advanced technical knowledge.
- **Developed by:** EryriLabs
- **Funded by:** Personal Project
- **Model type:** Fine-tuned conversational LLM for cybersecurity question-answering
- **Language(s) (NLP):** English (en)
- **License:** llama3.2
- **Finetuned from model:** unsloth/Llama-3.2-3B-bnb-4bit
### Model Sources
- **Repository:** [https://huggingface.co/EryriLabs/Llama-3.2-SARA-3b](https://huggingface.co/EryriLabs/Llama-3.2-SARA-3b)
## Uses
This model is intended for providing cybersecurity information and guidance to general users in an accessible, offline-friendly way.
### Direct Use
This model can be used as an offline assistant for basic cybersecurity questions, answering common queries in a conversational format. It is ideal for use cases where an internet connection is not available or where low-spec hardware constraints apply.
### Out-of-Scope Use
This model should not be used for professional or critical cybersecurity advice, as it is designed for general guidance and may lack the specificity required for advanced technical issues. It is also not suitable for providing nuanced advice in areas outside basic cybersecurity practices.
## Bias, Risks, and Limitations
While SARA is optimized for basic cybersecurity education, it has limitations in depth and may lack the ability to answer highly technical questions. Additionally, it may be limited in handling complex, nuanced queries due to its lightweight design and quantized 4-bit structure.
### Recommendations
Users should consider SARA as an educational tool rather than a replacement for professional cybersecurity advice. Further fine-tuning could help improve the model's handling of diverse inputs and conversational depth, making it more robust for varied user needs.
## How to Get Started with the Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("EryriLabs/Llama-3.2-SARA-3b", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("EryriLabs/Llama-3.2-SARA-3b")
# Sample question
input_text = "What make a strong password?"
# Tokenize and generate response
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(inputs["input_ids"], max_length=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Training Details
### Training Data
The model was fine-tuned on a custom Q&A-style dataset centered on cybersecurity fundamentals, such as creating a strong password, using 2-Step Verification etc.
### Training Procedure
The fine-tuning was conducted on a system with an Intel i9 12900k CPU, an NVIDIA GeForce RTX 4090 GPU, and 32GB RAM. Unsloth’s 4-bit quantization (bnb-4bit) was applied to keep the model compact and efficient for low-spec laptop deployment.
#### Training Hyperparameters
- **Training regime:** Mixed precision with 4-bit quantization (bnb-4bit)
#### Speeds, Sizes, Times [optional]
Training took approximately 10 minutes, with additional fine-tuning recommended for improved performance, especially for handling varied text inputs and enhancing conversational depth.
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
Testing was conducted on a dataset of common cybersecurity questions to evaluate the model’s responsiveness and accuracy for general use cases.
#### Factors
The model was evaluated based on its ability to provide clear, direct answers to basic cybersecurity questions.
#### Metrics
The main evaluation metric was response accuracy for typical cybersecurity queries.
### Results
The model performs adequately for its intended purpose, with room for improvement in response handling and input variability.
#### Summary
SARA functions well for basic cybersecurity guidance but requires additional fine-tuning to better handle diverse inputs and enhance conversational flow.
## Environmental Impact
Carbon emissions for this project can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
- **Hardware Type:** Intel i9 12900k CPU, NVIDIA GeForce RTX 4090 GPU
- **Hours used:** ~10 minutes of fine-tuning
- **Carbon Emitted:** 0.01
### Compute Infrastructure
The fine-tuning process was conducted on a high-spec machine, with final deployment optimized for low-spec hardware.
#### Hardware
Intel i9 12900k CPU, NVIDIA GeForce RTX 4090 GPU, 32GB RAM
#### Software
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)
## Contact
For questions or issues, please contact `EryriLabs`. |
vocabtrimmer/mt5-small-jaquad-qa-trimmed-ja-10000 | vocabtrimmer | "2023-04-28T15:09:11Z" | 105 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2023-03-15T15:47:57Z" | # Vocabulary Trimmed [lmqg/mt5-small-jaquad-qa](https://huggingface.co/lmqg/mt5-small-jaquad-qa): `vocabtrimmer/mt5-small-jaquad-qa-trimmed-ja-10000`
This model is a trimmed version of [lmqg/mt5-small-jaquad-qa](https://huggingface.co/lmqg/mt5-small-jaquad-qa) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-jaquad-qa | vocabtrimmer/mt5-small-jaquad-qa-trimmed-ja-10000 |
|:---------------------------|:---------------------------|:----------------------------------------------------|
| parameter_size_full | 300,165,504 | 54,304,128 |
| parameter_size_embedding | 256,103,424 | 10,242,048 |
| vocab_size | 250,101 | 10,002 |
| compression_rate_full | 100.0 | 18.09 |
| compression_rate_embedding | 100.0 | 4.0 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 10000 | 2 | |
robot-test/old-clip-tokenizer | robot-test | "2022-02-07T21:44:19Z" | 0 | 0 | null | [
"region:us"
] | null | "2022-03-02T23:29:05Z" | Old version of the CLIP fast tokenizer
cf [this issue](https://github.com/huggingface/transformers/issues/12648) on transformers |
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