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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-27 12:29:05
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int64 0
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| likes
int64 0
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| library_name
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barc0/llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4 | barc0 | 2024-10-11T01:09:36Z | 40 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems",
"dataset:barc0/transduction_angmented_100k_gpt4o-mini_generated_problems",
"dataset:barc0/transduction_rearc_dataset_400k",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-09T04:58:41Z | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems
- barc0/transduction_angmented_100k_gpt4o-mini_generated_problems
- barc0/transduction_rearc_dataset_400k
model-index:
- name: llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4
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. -->
# llama3.2-1b-instruct-fft-transduction-engineer_lr1e-5_epoch4
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the barc0/transduction_angmented_100k-gpt4-description-gpt4omini-code_generated_problems, the barc0/transduction_angmented_100k_gpt4o-mini_generated_problems and the barc0/transduction_rearc_dataset_400k datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0409
## 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
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0618 | 1.0 | 1126 | 0.0657 |
| 0.0504 | 2.0 | 2252 | 0.0494 |
| 0.0363 | 3.0 | 3378 | 0.0418 |
| 0.0238 | 4.0 | 4504 | 0.0409 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf | RichardErkhov | 2024-10-11T01:07:37Z | 22 | 0 | null | [
"gguf",
"arxiv:2403.19522",
"endpoints_compatible",
"region:us"
] | null | 2024-10-10T22:53:37Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-MahouDevil-8B - GGUF
- Model creator: https://huggingface.co/lemon07r/
- Original model: https://huggingface.co/lemon07r/Llama-3-MahouDevil-8B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3-MahouDevil-8B.Q2_K.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q2_K.gguf) | Q2_K | 2.96GB |
| [Llama-3-MahouDevil-8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Llama-3-MahouDevil-8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Llama-3-MahouDevil-8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Llama-3-MahouDevil-8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Llama-3-MahouDevil-8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q3_K.gguf) | Q3_K | 3.74GB |
| [Llama-3-MahouDevil-8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Llama-3-MahouDevil-8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Llama-3-MahouDevil-8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Llama-3-MahouDevil-8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Llama-3-MahouDevil-8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Llama-3-MahouDevil-8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Llama-3-MahouDevil-8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q4_K.gguf) | Q4_K | 4.58GB |
| [Llama-3-MahouDevil-8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Llama-3-MahouDevil-8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Llama-3-MahouDevil-8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Llama-3-MahouDevil-8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Llama-3-MahouDevil-8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q5_K.gguf) | Q5_K | 5.34GB |
| [Llama-3-MahouDevil-8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Llama-3-MahouDevil-8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Llama-3-MahouDevil-8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q6_K.gguf) | Q6_K | 6.14GB |
| [Llama-3-MahouDevil-8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/lemon07r_-_Llama-3-MahouDevil-8B-gguf/blob/main/Llama-3-MahouDevil-8B.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
base_model:
- mlabonne/NeuralDaredevil-8B-abliterated
- flammenai/Mahou-1.2-llama3-8B
- flammenai/Mahou-1.3-llama3-8B
library_name: transformers
tags:
- mergekit
- merge
license: llama3
---
# Llama-3-MahouDevil-8B
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) as a base.
### Models Merged
The following models were included in the merge:
* [flammenai/Mahou-1.2-llama3-8B](https://huggingface.co/flammenai/Mahou-1.2-llama3-8B)
* [flammenai/Mahou-1.3-llama3-8B](https://huggingface.co/flammenai/Mahou-1.3-llama3-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: mlabonne/NeuralDaredevil-8B-abliterated
dtype: bfloat16
merge_method: model_stock
slices:
- sources:
- layer_range: [0, 32]
model: flammenai/Mahou-1.2-llama3-8B
- layer_range: [0, 32]
model: flammenai/Mahou-1.3-llama3-8B
- layer_range: [0, 32]
model: mlabonne/NeuralDaredevil-8B-abliterated
```
|
kbulutozler/distilbert-base-uncased-FT-ner-BC5CDR-disease | kbulutozler | 2024-10-11T00:40:56Z | 199 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"BC5CDR-disease",
"NER",
"en",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-11T00:39:33Z | ---
library_name: transformers
tags:
- BC5CDR-disease
- NER
license: apache-2.0
language:
- en
metrics:
- seqeval
base_model:
- distilbert/distilbert-base-uncased
---
# Model Card for Model ID
Fine-tuned distilbert model. Trained on train set of BC5CDR-disease dataset taken from [BLURB](https://microsoft.github.io/BLURB/tasks.html).
## Model Details
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/kbulutozler/medical-llm-benchmark
## 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. -->
Train set of BC5CDR-disease dataset.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Classical fine-tuning.
#### 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 -->
learning_rate=5e-5
per_device_train_batch_size=16
per_device_eval_batch_size=16
num_train_epochs=3
weight_decay=0.01
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
Test set of BC5CDR-disease dataset.
### Results
Precision: 0.76
Recall: 0.81
Micro-F1: 0.79
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** 1xRTX A4000
- **Hours used:** 00:07:00
|
RefalMachine/ruadapt_qwen2.5_3B_ext_cl100k_bpe_32000_full_lr2e4_2k_bs256 | RefalMachine | 2024-10-11T00:30:33Z | 6 | 0 | null | [
"tensorboard",
"safetensors",
"qwen2",
"generated_from_trainer",
"base_model:RefalMachine/ruadapt_qwen2.5_3B_ext_cl100k_bpe_32000_mean_init",
"base_model:finetune:RefalMachine/ruadapt_qwen2.5_3B_ext_cl100k_bpe_32000_mean_init",
"region:us"
] | null | 2024-10-11T00:25:07Z | ---
base_model: RefalMachine/ruadapt_qwen2.5_3B_ext_cl100k_bpe_32000_mean_init
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ruadapt_qwen2.5_3B_ext_cl100k_bpe_32000_full_lr2e4_2k_bs256
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. -->
# ruadapt_qwen2.5_3B_ext_cl100k_bpe_32000_full_lr2e4_2k_bs256
This model is a fine-tuned version of [RefalMachine/ruadapt_qwen2.5_3B_ext_cl100k_bpe_32000_mean_init](https://huggingface.co/RefalMachine/ruadapt_qwen2.5_3B_ext_cl100k_bpe_32000_mean_init) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3590
- Accuracy: 0.5146
## 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
- distributed_type: multi-GPU
- num_devices: 64
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 0.0 | 1 | 5.2631 | 0.3345 |
| 2.5263 | 0.09 | 2000 | 2.4055 | 0.5090 |
| 2.4897 | 0.17 | 4000 | 2.3810 | 0.5119 |
| 2.4809 | 0.26 | 6000 | 2.3712 | 0.5130 |
| 2.4672 | 0.34 | 8000 | 2.3661 | 0.5138 |
| 2.4738 | 0.43 | 10000 | 2.3632 | 0.5140 |
| 2.4747 | 0.51 | 12000 | 2.3613 | 0.5142 |
| 2.4777 | 0.6 | 14000 | 2.3602 | 0.5145 |
| 2.4636 | 0.68 | 16000 | 2.3596 | 0.5145 |
| 2.4792 | 0.77 | 18000 | 2.3592 | 0.5146 |
| 2.4774 | 0.85 | 20000 | 2.3590 | 0.5147 |
| 2.4603 | 0.94 | 22000 | 2.3591 | 0.5146 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0a0+6ddf5cf85e.nv24.04
- Datasets 2.18.0
- Tokenizers 0.15.2
|
kbulutozler/distilbert-base-uncased-FT-ner-BC5CDR-chem | kbulutozler | 2024-10-11T00:28:27Z | 186 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"BC5CDR-chem",
"NER",
"en",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-11T00:25:55Z | ---
library_name: transformers
tags:
- BC5CDR-chem
- NER
license: apache-2.0
language:
- en
metrics:
- seqeval
base_model:
- distilbert/distilbert-base-uncased
---
# Model Card for Model ID
Fine-tuned distilbert model. Trained on train set of BC5CDR-chem dataset taken from [BLURB](https://microsoft.github.io/BLURB/tasks.html).
## Model Details
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/kbulutozler/medical-llm-benchmark
## 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. -->
Train set of BC5CDR-chem dataset.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Classical fine-tuning.
#### 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 -->
learning_rate=5e-5
per_device_train_batch_size=16
per_device_eval_batch_size=16
num_train_epochs=3
weight_decay=0.01
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
Test set of BC5CDR-chem dataset.
### Results
Precision: 0.89
Recall: 0.87
Micro-F1: 0.88
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** 1xRTX A4000
- **Hours used:** 00:07:00
|
haorandai/png_randomnoise_vehicle_solid_gray_20_with20constraints | haorandai | 2024-10-11T00:27:50Z | 29 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-11T00:23:10Z | ---
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Text-to-image finetuning - haorandai/png_randomnoise_vehicle_solid_gray_20_with20constraints
This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **haorandai/png_randomnoise_vehicle_solid_gray_20_with20constraints** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: None:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("haorandai/png_randomnoise_vehicle_solid_gray_20_with20constraints", torch_dtype=torch.float16)
prompt = "None"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 20
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 224
* Mixed-precision: fp16
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
haorandai/png_randomnoise_vehicle_solid_olive_20_with20constraints | haorandai | 2024-10-11T00:23:00Z | 29 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-11T00:18:43Z | ---
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Text-to-image finetuning - haorandai/png_randomnoise_vehicle_solid_olive_20_with20constraints
This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **haorandai/png_randomnoise_vehicle_solid_olive_20_with20constraints** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: None:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("haorandai/png_randomnoise_vehicle_solid_olive_20_with20constraints", torch_dtype=torch.float16)
prompt = "None"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 20
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 224
* Mixed-precision: fp16
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
mav23/gte-Qwen2-7B-instruct-GGUF | mav23 | 2024-10-11T00:14:54Z | 73 | 0 | sentence-transformers | [
"sentence-transformers",
"gguf",
"mteb",
"transformers",
"Qwen2",
"sentence-similarity",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"conversational"
] | sentence-similarity | 2024-10-10T23:23:46Z | ---
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
license: apache-2.0
model-index:
- name: gte-qwen2-7B-instruct
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 91.31343283582089
- type: ap
value: 67.64251402604096
- type: f1
value: 87.53372530755692
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.497825
- type: ap
value: 96.30329547047529
- type: f1
value: 97.49769793778039
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 62.564
- type: f1
value: 60.975777935041066
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 36.486000000000004
- type: map_at_10
value: 54.842
- type: map_at_100
value: 55.206999999999994
- type: map_at_1000
value: 55.206999999999994
- type: map_at_3
value: 49.893
- type: map_at_5
value: 53.105000000000004
- type: mrr_at_1
value: 37.34
- type: mrr_at_10
value: 55.143
- type: mrr_at_100
value: 55.509
- type: mrr_at_1000
value: 55.509
- type: mrr_at_3
value: 50.212999999999994
- type: mrr_at_5
value: 53.432
- type: ndcg_at_1
value: 36.486000000000004
- type: ndcg_at_10
value: 64.273
- type: ndcg_at_100
value: 65.66199999999999
- type: ndcg_at_1000
value: 65.66199999999999
- type: ndcg_at_3
value: 54.352999999999994
- type: ndcg_at_5
value: 60.131
- type: precision_at_1
value: 36.486000000000004
- type: precision_at_10
value: 9.395000000000001
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.428
- type: precision_at_5
value: 16.259
- type: recall_at_1
value: 36.486000000000004
- type: recall_at_10
value: 93.95400000000001
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 67.283
- type: recall_at_5
value: 81.294
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 56.461169803700564
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 51.73600434466286
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.57827065898053
- type: mrr
value: 79.08136569493911
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 83.53324575999243
- type: cos_sim_spearman
value: 81.37173362822374
- type: euclidean_pearson
value: 82.19243335103444
- type: euclidean_spearman
value: 81.33679307304334
- type: manhattan_pearson
value: 82.38752665975699
- type: manhattan_spearman
value: 81.31510583189689
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.56818181818181
- type: f1
value: 87.25826722019875
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 50.09239610327673
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 46.64733054606282
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 33.997
- type: map_at_10
value: 48.176
- type: map_at_100
value: 49.82
- type: map_at_1000
value: 49.924
- type: map_at_3
value: 43.626
- type: map_at_5
value: 46.275
- type: mrr_at_1
value: 42.059999999999995
- type: mrr_at_10
value: 53.726
- type: mrr_at_100
value: 54.398
- type: mrr_at_1000
value: 54.416
- type: mrr_at_3
value: 50.714999999999996
- type: mrr_at_5
value: 52.639
- type: ndcg_at_1
value: 42.059999999999995
- type: ndcg_at_10
value: 55.574999999999996
- type: ndcg_at_100
value: 60.744
- type: ndcg_at_1000
value: 61.85699999999999
- type: ndcg_at_3
value: 49.363
- type: ndcg_at_5
value: 52.44
- type: precision_at_1
value: 42.059999999999995
- type: precision_at_10
value: 11.101999999999999
- type: precision_at_100
value: 1.73
- type: precision_at_1000
value: 0.218
- type: precision_at_3
value: 24.464
- type: precision_at_5
value: 18.026
- type: recall_at_1
value: 33.997
- type: recall_at_10
value: 70.35900000000001
- type: recall_at_100
value: 91.642
- type: recall_at_1000
value: 97.977
- type: recall_at_3
value: 52.76
- type: recall_at_5
value: 61.148
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 35.884
- type: map_at_10
value: 48.14
- type: map_at_100
value: 49.5
- type: map_at_1000
value: 49.63
- type: map_at_3
value: 44.646
- type: map_at_5
value: 46.617999999999995
- type: mrr_at_1
value: 44.458999999999996
- type: mrr_at_10
value: 53.751000000000005
- type: mrr_at_100
value: 54.37800000000001
- type: mrr_at_1000
value: 54.415
- type: mrr_at_3
value: 51.815
- type: mrr_at_5
value: 52.882
- type: ndcg_at_1
value: 44.458999999999996
- type: ndcg_at_10
value: 54.157
- type: ndcg_at_100
value: 58.362
- type: ndcg_at_1000
value: 60.178
- type: ndcg_at_3
value: 49.661
- type: ndcg_at_5
value: 51.74999999999999
- type: precision_at_1
value: 44.458999999999996
- type: precision_at_10
value: 10.248
- type: precision_at_100
value: 1.5890000000000002
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 23.928
- type: precision_at_5
value: 16.878999999999998
- type: recall_at_1
value: 35.884
- type: recall_at_10
value: 64.798
- type: recall_at_100
value: 82.345
- type: recall_at_1000
value: 93.267
- type: recall_at_3
value: 51.847
- type: recall_at_5
value: 57.601
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 39.383
- type: map_at_10
value: 53.714
- type: map_at_100
value: 54.838
- type: map_at_1000
value: 54.87800000000001
- type: map_at_3
value: 50.114999999999995
- type: map_at_5
value: 52.153000000000006
- type: mrr_at_1
value: 45.016
- type: mrr_at_10
value: 56.732000000000006
- type: mrr_at_100
value: 57.411
- type: mrr_at_1000
value: 57.431
- type: mrr_at_3
value: 54.044000000000004
- type: mrr_at_5
value: 55.639
- type: ndcg_at_1
value: 45.016
- type: ndcg_at_10
value: 60.228
- type: ndcg_at_100
value: 64.277
- type: ndcg_at_1000
value: 65.07
- type: ndcg_at_3
value: 54.124
- type: ndcg_at_5
value: 57.147000000000006
- type: precision_at_1
value: 45.016
- type: precision_at_10
value: 9.937
- type: precision_at_100
value: 1.288
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.471999999999998
- type: precision_at_5
value: 16.991
- type: recall_at_1
value: 39.383
- type: recall_at_10
value: 76.175
- type: recall_at_100
value: 93.02
- type: recall_at_1000
value: 98.60900000000001
- type: recall_at_3
value: 60.265
- type: recall_at_5
value: 67.46600000000001
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 27.426000000000002
- type: map_at_10
value: 37.397000000000006
- type: map_at_100
value: 38.61
- type: map_at_1000
value: 38.678000000000004
- type: map_at_3
value: 34.150999999999996
- type: map_at_5
value: 36.137
- type: mrr_at_1
value: 29.944
- type: mrr_at_10
value: 39.654
- type: mrr_at_100
value: 40.638000000000005
- type: mrr_at_1000
value: 40.691
- type: mrr_at_3
value: 36.817
- type: mrr_at_5
value: 38.524
- type: ndcg_at_1
value: 29.944
- type: ndcg_at_10
value: 43.094
- type: ndcg_at_100
value: 48.789
- type: ndcg_at_1000
value: 50.339999999999996
- type: ndcg_at_3
value: 36.984
- type: ndcg_at_5
value: 40.248
- type: precision_at_1
value: 29.944
- type: precision_at_10
value: 6.78
- type: precision_at_100
value: 1.024
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 15.895000000000001
- type: precision_at_5
value: 11.39
- type: recall_at_1
value: 27.426000000000002
- type: recall_at_10
value: 58.464000000000006
- type: recall_at_100
value: 84.193
- type: recall_at_1000
value: 95.52000000000001
- type: recall_at_3
value: 42.172
- type: recall_at_5
value: 50.101
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 19.721
- type: map_at_10
value: 31.604
- type: map_at_100
value: 32.972
- type: map_at_1000
value: 33.077
- type: map_at_3
value: 27.218999999999998
- type: map_at_5
value: 29.53
- type: mrr_at_1
value: 25.0
- type: mrr_at_10
value: 35.843
- type: mrr_at_100
value: 36.785000000000004
- type: mrr_at_1000
value: 36.842000000000006
- type: mrr_at_3
value: 32.193
- type: mrr_at_5
value: 34.264
- type: ndcg_at_1
value: 25.0
- type: ndcg_at_10
value: 38.606
- type: ndcg_at_100
value: 44.272
- type: ndcg_at_1000
value: 46.527
- type: ndcg_at_3
value: 30.985000000000003
- type: ndcg_at_5
value: 34.43
- type: precision_at_1
value: 25.0
- type: precision_at_10
value: 7.811
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.15
- type: precision_at_3
value: 15.423
- type: precision_at_5
value: 11.791
- type: recall_at_1
value: 19.721
- type: recall_at_10
value: 55.625
- type: recall_at_100
value: 79.34400000000001
- type: recall_at_1000
value: 95.208
- type: recall_at_3
value: 35.19
- type: recall_at_5
value: 43.626
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 33.784
- type: map_at_10
value: 47.522
- type: map_at_100
value: 48.949999999999996
- type: map_at_1000
value: 49.038
- type: map_at_3
value: 43.284
- type: map_at_5
value: 45.629
- type: mrr_at_1
value: 41.482
- type: mrr_at_10
value: 52.830999999999996
- type: mrr_at_100
value: 53.559999999999995
- type: mrr_at_1000
value: 53.588
- type: mrr_at_3
value: 50.016000000000005
- type: mrr_at_5
value: 51.614000000000004
- type: ndcg_at_1
value: 41.482
- type: ndcg_at_10
value: 54.569
- type: ndcg_at_100
value: 59.675999999999995
- type: ndcg_at_1000
value: 60.989000000000004
- type: ndcg_at_3
value: 48.187000000000005
- type: ndcg_at_5
value: 51.183
- type: precision_at_1
value: 41.482
- type: precision_at_10
value: 10.221
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.17500000000000002
- type: precision_at_3
value: 23.548
- type: precision_at_5
value: 16.805
- type: recall_at_1
value: 33.784
- type: recall_at_10
value: 69.798
- type: recall_at_100
value: 90.098
- type: recall_at_1000
value: 98.176
- type: recall_at_3
value: 52.127
- type: recall_at_5
value: 59.861
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.038999999999998
- type: map_at_10
value: 41.904
- type: map_at_100
value: 43.36
- type: map_at_1000
value: 43.453
- type: map_at_3
value: 37.785999999999994
- type: map_at_5
value: 40.105000000000004
- type: mrr_at_1
value: 35.046
- type: mrr_at_10
value: 46.926
- type: mrr_at_100
value: 47.815000000000005
- type: mrr_at_1000
value: 47.849000000000004
- type: mrr_at_3
value: 44.273
- type: mrr_at_5
value: 45.774
- type: ndcg_at_1
value: 35.046
- type: ndcg_at_10
value: 48.937000000000005
- type: ndcg_at_100
value: 54.544000000000004
- type: ndcg_at_1000
value: 56.069
- type: ndcg_at_3
value: 42.858000000000004
- type: ndcg_at_5
value: 45.644
- type: precision_at_1
value: 35.046
- type: precision_at_10
value: 9.452
- type: precision_at_100
value: 1.429
- type: precision_at_1000
value: 0.173
- type: precision_at_3
value: 21.346999999999998
- type: precision_at_5
value: 15.342
- type: recall_at_1
value: 28.038999999999998
- type: recall_at_10
value: 64.59700000000001
- type: recall_at_100
value: 87.735
- type: recall_at_1000
value: 97.41300000000001
- type: recall_at_3
value: 47.368
- type: recall_at_5
value: 54.93900000000001
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 28.17291666666667
- type: map_at_10
value: 40.025749999999995
- type: map_at_100
value: 41.39208333333333
- type: map_at_1000
value: 41.499249999999996
- type: map_at_3
value: 36.347
- type: map_at_5
value: 38.41391666666667
- type: mrr_at_1
value: 33.65925
- type: mrr_at_10
value: 44.085499999999996
- type: mrr_at_100
value: 44.94116666666667
- type: mrr_at_1000
value: 44.9855
- type: mrr_at_3
value: 41.2815
- type: mrr_at_5
value: 42.91491666666666
- type: ndcg_at_1
value: 33.65925
- type: ndcg_at_10
value: 46.430833333333325
- type: ndcg_at_100
value: 51.761
- type: ndcg_at_1000
value: 53.50899999999999
- type: ndcg_at_3
value: 40.45133333333333
- type: ndcg_at_5
value: 43.31483333333334
- type: precision_at_1
value: 33.65925
- type: precision_at_10
value: 8.4995
- type: precision_at_100
value: 1.3210000000000004
- type: precision_at_1000
value: 0.16591666666666666
- type: precision_at_3
value: 19.165083333333335
- type: precision_at_5
value: 13.81816666666667
- type: recall_at_1
value: 28.17291666666667
- type: recall_at_10
value: 61.12624999999999
- type: recall_at_100
value: 83.97266666666667
- type: recall_at_1000
value: 95.66550000000001
- type: recall_at_3
value: 44.661249999999995
- type: recall_at_5
value: 51.983333333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 24.681
- type: map_at_10
value: 34.892
- type: map_at_100
value: 35.996
- type: map_at_1000
value: 36.083
- type: map_at_3
value: 31.491999999999997
- type: map_at_5
value: 33.632
- type: mrr_at_1
value: 28.528
- type: mrr_at_10
value: 37.694
- type: mrr_at_100
value: 38.613
- type: mrr_at_1000
value: 38.668
- type: mrr_at_3
value: 34.714
- type: mrr_at_5
value: 36.616
- type: ndcg_at_1
value: 28.528
- type: ndcg_at_10
value: 40.703
- type: ndcg_at_100
value: 45.993
- type: ndcg_at_1000
value: 47.847
- type: ndcg_at_3
value: 34.622
- type: ndcg_at_5
value: 38.035999999999994
- type: precision_at_1
value: 28.528
- type: precision_at_10
value: 6.902
- type: precision_at_100
value: 1.0370000000000001
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 15.798000000000002
- type: precision_at_5
value: 11.655999999999999
- type: recall_at_1
value: 24.681
- type: recall_at_10
value: 55.81
- type: recall_at_100
value: 79.785
- type: recall_at_1000
value: 92.959
- type: recall_at_3
value: 39.074
- type: recall_at_5
value: 47.568
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.627
- type: map_at_10
value: 27.872000000000003
- type: map_at_100
value: 29.237999999999996
- type: map_at_1000
value: 29.363
- type: map_at_3
value: 24.751
- type: map_at_5
value: 26.521
- type: mrr_at_1
value: 23.021
- type: mrr_at_10
value: 31.924000000000003
- type: mrr_at_100
value: 32.922000000000004
- type: mrr_at_1000
value: 32.988
- type: mrr_at_3
value: 29.192
- type: mrr_at_5
value: 30.798
- type: ndcg_at_1
value: 23.021
- type: ndcg_at_10
value: 33.535
- type: ndcg_at_100
value: 39.732
- type: ndcg_at_1000
value: 42.201
- type: ndcg_at_3
value: 28.153
- type: ndcg_at_5
value: 30.746000000000002
- type: precision_at_1
value: 23.021
- type: precision_at_10
value: 6.459
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 13.719000000000001
- type: precision_at_5
value: 10.193000000000001
- type: recall_at_1
value: 18.627
- type: recall_at_10
value: 46.463
- type: recall_at_100
value: 74.226
- type: recall_at_1000
value: 91.28500000000001
- type: recall_at_3
value: 31.357000000000003
- type: recall_at_5
value: 38.067
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 31.457
- type: map_at_10
value: 42.888
- type: map_at_100
value: 44.24
- type: map_at_1000
value: 44.327
- type: map_at_3
value: 39.588
- type: map_at_5
value: 41.423
- type: mrr_at_1
value: 37.126999999999995
- type: mrr_at_10
value: 47.083000000000006
- type: mrr_at_100
value: 47.997
- type: mrr_at_1000
value: 48.044
- type: mrr_at_3
value: 44.574000000000005
- type: mrr_at_5
value: 46.202
- type: ndcg_at_1
value: 37.126999999999995
- type: ndcg_at_10
value: 48.833
- type: ndcg_at_100
value: 54.327000000000005
- type: ndcg_at_1000
value: 56.011
- type: ndcg_at_3
value: 43.541999999999994
- type: ndcg_at_5
value: 46.127
- type: precision_at_1
value: 37.126999999999995
- type: precision_at_10
value: 8.376999999999999
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 20.211000000000002
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 31.457
- type: recall_at_10
value: 62.369
- type: recall_at_100
value: 85.444
- type: recall_at_1000
value: 96.65599999999999
- type: recall_at_3
value: 47.961
- type: recall_at_5
value: 54.676
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.139999999999997
- type: map_at_10
value: 38.801
- type: map_at_100
value: 40.549
- type: map_at_1000
value: 40.802
- type: map_at_3
value: 35.05
- type: map_at_5
value: 36.884
- type: mrr_at_1
value: 33.004
- type: mrr_at_10
value: 43.864
- type: mrr_at_100
value: 44.667
- type: mrr_at_1000
value: 44.717
- type: mrr_at_3
value: 40.777
- type: mrr_at_5
value: 42.319
- type: ndcg_at_1
value: 33.004
- type: ndcg_at_10
value: 46.022
- type: ndcg_at_100
value: 51.542
- type: ndcg_at_1000
value: 53.742000000000004
- type: ndcg_at_3
value: 39.795
- type: ndcg_at_5
value: 42.272
- type: precision_at_1
value: 33.004
- type: precision_at_10
value: 9.012
- type: precision_at_100
value: 1.7770000000000001
- type: precision_at_1000
value: 0.26
- type: precision_at_3
value: 19.038
- type: precision_at_5
value: 13.675999999999998
- type: recall_at_1
value: 27.139999999999997
- type: recall_at_10
value: 60.961
- type: recall_at_100
value: 84.451
- type: recall_at_1000
value: 98.113
- type: recall_at_3
value: 43.001
- type: recall_at_5
value: 49.896
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 17.936
- type: map_at_10
value: 27.399
- type: map_at_100
value: 28.632
- type: map_at_1000
value: 28.738000000000003
- type: map_at_3
value: 24.456
- type: map_at_5
value: 26.06
- type: mrr_at_1
value: 19.224
- type: mrr_at_10
value: 28.998
- type: mrr_at_100
value: 30.11
- type: mrr_at_1000
value: 30.177
- type: mrr_at_3
value: 26.247999999999998
- type: mrr_at_5
value: 27.708
- type: ndcg_at_1
value: 19.224
- type: ndcg_at_10
value: 32.911
- type: ndcg_at_100
value: 38.873999999999995
- type: ndcg_at_1000
value: 41.277
- type: ndcg_at_3
value: 27.142
- type: ndcg_at_5
value: 29.755
- type: precision_at_1
value: 19.224
- type: precision_at_10
value: 5.6930000000000005
- type: precision_at_100
value: 0.9259999999999999
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 12.138
- type: precision_at_5
value: 8.909
- type: recall_at_1
value: 17.936
- type: recall_at_10
value: 48.096
- type: recall_at_100
value: 75.389
- type: recall_at_1000
value: 92.803
- type: recall_at_3
value: 32.812999999999995
- type: recall_at_5
value: 38.851
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 22.076999999999998
- type: map_at_10
value: 35.44
- type: map_at_100
value: 37.651
- type: map_at_1000
value: 37.824999999999996
- type: map_at_3
value: 30.764999999999997
- type: map_at_5
value: 33.26
- type: mrr_at_1
value: 50.163000000000004
- type: mrr_at_10
value: 61.207
- type: mrr_at_100
value: 61.675000000000004
- type: mrr_at_1000
value: 61.692
- type: mrr_at_3
value: 58.60999999999999
- type: mrr_at_5
value: 60.307
- type: ndcg_at_1
value: 50.163000000000004
- type: ndcg_at_10
value: 45.882
- type: ndcg_at_100
value: 53.239999999999995
- type: ndcg_at_1000
value: 55.852000000000004
- type: ndcg_at_3
value: 40.514
- type: ndcg_at_5
value: 42.038
- type: precision_at_1
value: 50.163000000000004
- type: precision_at_10
value: 13.466000000000001
- type: precision_at_100
value: 2.164
- type: precision_at_1000
value: 0.266
- type: precision_at_3
value: 29.707
- type: precision_at_5
value: 21.694
- type: recall_at_1
value: 22.076999999999998
- type: recall_at_10
value: 50.193
- type: recall_at_100
value: 74.993
- type: recall_at_1000
value: 89.131
- type: recall_at_3
value: 35.472
- type: recall_at_5
value: 41.814
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.953
- type: map_at_10
value: 24.515
- type: map_at_100
value: 36.173
- type: map_at_1000
value: 38.351
- type: map_at_3
value: 16.592000000000002
- type: map_at_5
value: 20.036
- type: mrr_at_1
value: 74.25
- type: mrr_at_10
value: 81.813
- type: mrr_at_100
value: 82.006
- type: mrr_at_1000
value: 82.011
- type: mrr_at_3
value: 80.875
- type: mrr_at_5
value: 81.362
- type: ndcg_at_1
value: 62.5
- type: ndcg_at_10
value: 52.42
- type: ndcg_at_100
value: 56.808
- type: ndcg_at_1000
value: 63.532999999999994
- type: ndcg_at_3
value: 56.654
- type: ndcg_at_5
value: 54.18300000000001
- type: precision_at_1
value: 74.25
- type: precision_at_10
value: 42.699999999999996
- type: precision_at_100
value: 13.675
- type: precision_at_1000
value: 2.664
- type: precision_at_3
value: 60.5
- type: precision_at_5
value: 52.800000000000004
- type: recall_at_1
value: 9.953
- type: recall_at_10
value: 30.253999999999998
- type: recall_at_100
value: 62.516000000000005
- type: recall_at_1000
value: 84.163
- type: recall_at_3
value: 18.13
- type: recall_at_5
value: 22.771
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 79.455
- type: f1
value: 74.16798697647569
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 87.531
- type: map_at_10
value: 93.16799999999999
- type: map_at_100
value: 93.341
- type: map_at_1000
value: 93.349
- type: map_at_3
value: 92.444
- type: map_at_5
value: 92.865
- type: mrr_at_1
value: 94.014
- type: mrr_at_10
value: 96.761
- type: mrr_at_100
value: 96.762
- type: mrr_at_1000
value: 96.762
- type: mrr_at_3
value: 96.672
- type: mrr_at_5
value: 96.736
- type: ndcg_at_1
value: 94.014
- type: ndcg_at_10
value: 95.112
- type: ndcg_at_100
value: 95.578
- type: ndcg_at_1000
value: 95.68900000000001
- type: ndcg_at_3
value: 94.392
- type: ndcg_at_5
value: 94.72500000000001
- type: precision_at_1
value: 94.014
- type: precision_at_10
value: 11.065
- type: precision_at_100
value: 1.157
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 35.259
- type: precision_at_5
value: 21.599
- type: recall_at_1
value: 87.531
- type: recall_at_10
value: 97.356
- type: recall_at_100
value: 98.965
- type: recall_at_1000
value: 99.607
- type: recall_at_3
value: 95.312
- type: recall_at_5
value: 96.295
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.055
- type: map_at_10
value: 53.114
- type: map_at_100
value: 55.235
- type: map_at_1000
value: 55.345
- type: map_at_3
value: 45.854
- type: map_at_5
value: 50.025
- type: mrr_at_1
value: 60.34
- type: mrr_at_10
value: 68.804
- type: mrr_at_100
value: 69.309
- type: mrr_at_1000
value: 69.32199999999999
- type: mrr_at_3
value: 66.40899999999999
- type: mrr_at_5
value: 67.976
- type: ndcg_at_1
value: 60.34
- type: ndcg_at_10
value: 62.031000000000006
- type: ndcg_at_100
value: 68.00500000000001
- type: ndcg_at_1000
value: 69.286
- type: ndcg_at_3
value: 56.355999999999995
- type: ndcg_at_5
value: 58.687
- type: precision_at_1
value: 60.34
- type: precision_at_10
value: 17.176
- type: precision_at_100
value: 2.36
- type: precision_at_1000
value: 0.259
- type: precision_at_3
value: 37.14
- type: precision_at_5
value: 27.809
- type: recall_at_1
value: 32.055
- type: recall_at_10
value: 70.91
- type: recall_at_100
value: 91.83
- type: recall_at_1000
value: 98.871
- type: recall_at_3
value: 51.202999999999996
- type: recall_at_5
value: 60.563
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 43.68
- type: map_at_10
value: 64.389
- type: map_at_100
value: 65.24
- type: map_at_1000
value: 65.303
- type: map_at_3
value: 61.309000000000005
- type: map_at_5
value: 63.275999999999996
- type: mrr_at_1
value: 87.36
- type: mrr_at_10
value: 91.12
- type: mrr_at_100
value: 91.227
- type: mrr_at_1000
value: 91.229
- type: mrr_at_3
value: 90.57600000000001
- type: mrr_at_5
value: 90.912
- type: ndcg_at_1
value: 87.36
- type: ndcg_at_10
value: 73.076
- type: ndcg_at_100
value: 75.895
- type: ndcg_at_1000
value: 77.049
- type: ndcg_at_3
value: 68.929
- type: ndcg_at_5
value: 71.28
- type: precision_at_1
value: 87.36
- type: precision_at_10
value: 14.741000000000001
- type: precision_at_100
value: 1.694
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 43.043
- type: precision_at_5
value: 27.681
- type: recall_at_1
value: 43.68
- type: recall_at_10
value: 73.707
- type: recall_at_100
value: 84.7
- type: recall_at_1000
value: 92.309
- type: recall_at_3
value: 64.564
- type: recall_at_5
value: 69.203
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 96.75399999999999
- type: ap
value: 95.29389839242187
- type: f1
value: 96.75348377433475
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 25.176
- type: map_at_10
value: 38.598
- type: map_at_100
value: 39.707
- type: map_at_1000
value: 39.744
- type: map_at_3
value: 34.566
- type: map_at_5
value: 36.863
- type: mrr_at_1
value: 25.874000000000002
- type: mrr_at_10
value: 39.214
- type: mrr_at_100
value: 40.251
- type: mrr_at_1000
value: 40.281
- type: mrr_at_3
value: 35.291
- type: mrr_at_5
value: 37.545
- type: ndcg_at_1
value: 25.874000000000002
- type: ndcg_at_10
value: 45.98
- type: ndcg_at_100
value: 51.197
- type: ndcg_at_1000
value: 52.073
- type: ndcg_at_3
value: 37.785999999999994
- type: ndcg_at_5
value: 41.870000000000005
- type: precision_at_1
value: 25.874000000000002
- type: precision_at_10
value: 7.181
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 16.051000000000002
- type: precision_at_5
value: 11.713
- type: recall_at_1
value: 25.176
- type: recall_at_10
value: 68.67699999999999
- type: recall_at_100
value: 92.55
- type: recall_at_1000
value: 99.164
- type: recall_at_3
value: 46.372
- type: recall_at_5
value: 56.16
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 99.03784769721841
- type: f1
value: 98.97791641821495
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 91.88326493388054
- type: f1
value: 73.74809928034335
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 85.41358439811701
- type: f1
value: 83.503679460639
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 89.77135171486215
- type: f1
value: 88.89843747468366
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 46.22695362087359
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 44.132372165849425
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.35680810650402
- type: mrr
value: 34.72625715637218
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 7.165000000000001
- type: map_at_10
value: 15.424
- type: map_at_100
value: 20.28
- type: map_at_1000
value: 22.065
- type: map_at_3
value: 11.236
- type: map_at_5
value: 13.025999999999998
- type: mrr_at_1
value: 51.702999999999996
- type: mrr_at_10
value: 59.965
- type: mrr_at_100
value: 60.667
- type: mrr_at_1000
value: 60.702999999999996
- type: mrr_at_3
value: 58.772000000000006
- type: mrr_at_5
value: 59.267
- type: ndcg_at_1
value: 49.536
- type: ndcg_at_10
value: 40.6
- type: ndcg_at_100
value: 37.848
- type: ndcg_at_1000
value: 46.657
- type: ndcg_at_3
value: 46.117999999999995
- type: ndcg_at_5
value: 43.619
- type: precision_at_1
value: 51.393
- type: precision_at_10
value: 30.31
- type: precision_at_100
value: 9.972
- type: precision_at_1000
value: 2.329
- type: precision_at_3
value: 43.137
- type: precision_at_5
value: 37.585
- type: recall_at_1
value: 7.165000000000001
- type: recall_at_10
value: 19.689999999999998
- type: recall_at_100
value: 39.237
- type: recall_at_1000
value: 71.417
- type: recall_at_3
value: 12.247
- type: recall_at_5
value: 14.902999999999999
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 42.653999999999996
- type: map_at_10
value: 59.611999999999995
- type: map_at_100
value: 60.32300000000001
- type: map_at_1000
value: 60.336
- type: map_at_3
value: 55.584999999999994
- type: map_at_5
value: 58.19
- type: mrr_at_1
value: 47.683
- type: mrr_at_10
value: 62.06700000000001
- type: mrr_at_100
value: 62.537
- type: mrr_at_1000
value: 62.544999999999995
- type: mrr_at_3
value: 59.178
- type: mrr_at_5
value: 61.034
- type: ndcg_at_1
value: 47.654
- type: ndcg_at_10
value: 67.001
- type: ndcg_at_100
value: 69.73899999999999
- type: ndcg_at_1000
value: 69.986
- type: ndcg_at_3
value: 59.95700000000001
- type: ndcg_at_5
value: 64.025
- type: precision_at_1
value: 47.654
- type: precision_at_10
value: 10.367999999999999
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 26.651000000000003
- type: precision_at_5
value: 18.459
- type: recall_at_1
value: 42.653999999999996
- type: recall_at_10
value: 86.619
- type: recall_at_100
value: 98.04899999999999
- type: recall_at_1000
value: 99.812
- type: recall_at_3
value: 68.987
- type: recall_at_5
value: 78.158
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 72.538
- type: map_at_10
value: 86.702
- type: map_at_100
value: 87.31
- type: map_at_1000
value: 87.323
- type: map_at_3
value: 83.87
- type: map_at_5
value: 85.682
- type: mrr_at_1
value: 83.31
- type: mrr_at_10
value: 89.225
- type: mrr_at_100
value: 89.30399999999999
- type: mrr_at_1000
value: 89.30399999999999
- type: mrr_at_3
value: 88.44300000000001
- type: mrr_at_5
value: 89.005
- type: ndcg_at_1
value: 83.32000000000001
- type: ndcg_at_10
value: 90.095
- type: ndcg_at_100
value: 91.12
- type: ndcg_at_1000
value: 91.179
- type: ndcg_at_3
value: 87.606
- type: ndcg_at_5
value: 89.031
- type: precision_at_1
value: 83.32000000000001
- type: precision_at_10
value: 13.641
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.377
- type: precision_at_5
value: 25.162000000000003
- type: recall_at_1
value: 72.538
- type: recall_at_10
value: 96.47200000000001
- type: recall_at_100
value: 99.785
- type: recall_at_1000
value: 99.99900000000001
- type: recall_at_3
value: 89.278
- type: recall_at_5
value: 93.367
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 73.55219145406065
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 74.13437105242755
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.873
- type: map_at_10
value: 17.944
- type: map_at_100
value: 21.171
- type: map_at_1000
value: 21.528
- type: map_at_3
value: 12.415
- type: map_at_5
value: 15.187999999999999
- type: mrr_at_1
value: 33.800000000000004
- type: mrr_at_10
value: 46.455
- type: mrr_at_100
value: 47.378
- type: mrr_at_1000
value: 47.394999999999996
- type: mrr_at_3
value: 42.367
- type: mrr_at_5
value: 44.972
- type: ndcg_at_1
value: 33.800000000000004
- type: ndcg_at_10
value: 28.907
- type: ndcg_at_100
value: 39.695
- type: ndcg_at_1000
value: 44.582
- type: ndcg_at_3
value: 26.949
- type: ndcg_at_5
value: 23.988
- type: precision_at_1
value: 33.800000000000004
- type: precision_at_10
value: 15.079999999999998
- type: precision_at_100
value: 3.056
- type: precision_at_1000
value: 0.42100000000000004
- type: precision_at_3
value: 25.167
- type: precision_at_5
value: 21.26
- type: recall_at_1
value: 6.873
- type: recall_at_10
value: 30.568
- type: recall_at_100
value: 62.062
- type: recall_at_1000
value: 85.37700000000001
- type: recall_at_3
value: 15.312999999999999
- type: recall_at_5
value: 21.575
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.37009118256057
- type: cos_sim_spearman
value: 79.27986395671529
- type: euclidean_pearson
value: 79.18037715442115
- type: euclidean_spearman
value: 79.28004791561621
- type: manhattan_pearson
value: 79.34062972800541
- type: manhattan_spearman
value: 79.43106695543402
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.48474767383833
- type: cos_sim_spearman
value: 79.54505388752513
- type: euclidean_pearson
value: 83.43282704179565
- type: euclidean_spearman
value: 79.54579919925405
- type: manhattan_pearson
value: 83.77564492427952
- type: manhattan_spearman
value: 79.84558396989286
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.803698035802
- type: cos_sim_spearman
value: 88.83451367754881
- type: euclidean_pearson
value: 88.28939285711628
- type: euclidean_spearman
value: 88.83528996073112
- type: manhattan_pearson
value: 88.28017412671795
- type: manhattan_spearman
value: 88.9228828016344
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.27469288153428
- type: cos_sim_spearman
value: 83.87477064876288
- type: euclidean_pearson
value: 84.2601737035379
- type: euclidean_spearman
value: 83.87431082479074
- type: manhattan_pearson
value: 84.3621547772745
- type: manhattan_spearman
value: 84.12094375000423
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.12749863201587
- type: cos_sim_spearman
value: 88.54287568368565
- type: euclidean_pearson
value: 87.90429700607999
- type: euclidean_spearman
value: 88.5437689576261
- type: manhattan_pearson
value: 88.19276653356833
- type: manhattan_spearman
value: 88.99995393814679
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.68398747560902
- type: cos_sim_spearman
value: 86.48815303460574
- type: euclidean_pearson
value: 85.52356631237954
- type: euclidean_spearman
value: 86.486391949551
- type: manhattan_pearson
value: 85.67267981761788
- type: manhattan_spearman
value: 86.7073696332485
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.9057107443124
- type: cos_sim_spearman
value: 88.7312168757697
- type: euclidean_pearson
value: 88.72810439714794
- type: euclidean_spearman
value: 88.71976185854771
- type: manhattan_pearson
value: 88.50433745949111
- type: manhattan_spearman
value: 88.51726175544195
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 67.59391795109886
- type: cos_sim_spearman
value: 66.87613008631367
- type: euclidean_pearson
value: 69.23198488262217
- type: euclidean_spearman
value: 66.85427723013692
- type: manhattan_pearson
value: 69.50730124841084
- type: manhattan_spearman
value: 67.10404669820792
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.0820605344619
- type: cos_sim_spearman
value: 86.8518089863434
- type: euclidean_pearson
value: 86.31087134689284
- type: euclidean_spearman
value: 86.8518520517941
- type: manhattan_pearson
value: 86.47203796160612
- type: manhattan_spearman
value: 87.1080149734421
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 89.09255369305481
- type: mrr
value: 97.10323445617563
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 61.260999999999996
- type: map_at_10
value: 74.043
- type: map_at_100
value: 74.37700000000001
- type: map_at_1000
value: 74.384
- type: map_at_3
value: 71.222
- type: map_at_5
value: 72.875
- type: mrr_at_1
value: 64.333
- type: mrr_at_10
value: 74.984
- type: mrr_at_100
value: 75.247
- type: mrr_at_1000
value: 75.25500000000001
- type: mrr_at_3
value: 73.167
- type: mrr_at_5
value: 74.35000000000001
- type: ndcg_at_1
value: 64.333
- type: ndcg_at_10
value: 79.06
- type: ndcg_at_100
value: 80.416
- type: ndcg_at_1000
value: 80.55600000000001
- type: ndcg_at_3
value: 74.753
- type: ndcg_at_5
value: 76.97500000000001
- type: precision_at_1
value: 64.333
- type: precision_at_10
value: 10.567
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 29.889
- type: precision_at_5
value: 19.533
- type: recall_at_1
value: 61.260999999999996
- type: recall_at_10
value: 93.167
- type: recall_at_100
value: 99.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 81.667
- type: recall_at_5
value: 87.394
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.71980198019801
- type: cos_sim_ap
value: 92.81616007802704
- type: cos_sim_f1
value: 85.17548454688318
- type: cos_sim_precision
value: 89.43894389438944
- type: cos_sim_recall
value: 81.3
- type: dot_accuracy
value: 99.71980198019801
- type: dot_ap
value: 92.81398760591358
- type: dot_f1
value: 85.17548454688318
- type: dot_precision
value: 89.43894389438944
- type: dot_recall
value: 81.3
- type: euclidean_accuracy
value: 99.71980198019801
- type: euclidean_ap
value: 92.81560637245072
- type: euclidean_f1
value: 85.17548454688318
- type: euclidean_precision
value: 89.43894389438944
- type: euclidean_recall
value: 81.3
- type: manhattan_accuracy
value: 99.73069306930694
- type: manhattan_ap
value: 93.14005487480794
- type: manhattan_f1
value: 85.56263269639068
- type: manhattan_precision
value: 91.17647058823529
- type: manhattan_recall
value: 80.60000000000001
- type: max_accuracy
value: 99.73069306930694
- type: max_ap
value: 93.14005487480794
- type: max_f1
value: 85.56263269639068
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 79.86443362395185
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 49.40897096662564
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.66040806627947
- type: mrr
value: 56.58670475766064
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.51015090598575
- type: cos_sim_spearman
value: 31.35016454939226
- type: dot_pearson
value: 31.5150068731
- type: dot_spearman
value: 31.34790869023487
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.254
- type: map_at_10
value: 2.064
- type: map_at_100
value: 12.909
- type: map_at_1000
value: 31.761
- type: map_at_3
value: 0.738
- type: map_at_5
value: 1.155
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 98.0
- type: mrr_at_100
value: 98.0
- type: mrr_at_1000
value: 98.0
- type: mrr_at_3
value: 98.0
- type: mrr_at_5
value: 98.0
- type: ndcg_at_1
value: 93.0
- type: ndcg_at_10
value: 82.258
- type: ndcg_at_100
value: 64.34
- type: ndcg_at_1000
value: 57.912
- type: ndcg_at_3
value: 90.827
- type: ndcg_at_5
value: 86.79
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 84.8
- type: precision_at_100
value: 66.0
- type: precision_at_1000
value: 25.356
- type: precision_at_3
value: 94.667
- type: precision_at_5
value: 90.4
- type: recall_at_1
value: 0.254
- type: recall_at_10
value: 2.1950000000000003
- type: recall_at_100
value: 16.088
- type: recall_at_1000
value: 54.559000000000005
- type: recall_at_3
value: 0.75
- type: recall_at_5
value: 1.191
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.976
- type: map_at_10
value: 11.389000000000001
- type: map_at_100
value: 18.429000000000002
- type: map_at_1000
value: 20.113
- type: map_at_3
value: 6.483
- type: map_at_5
value: 8.770999999999999
- type: mrr_at_1
value: 40.816
- type: mrr_at_10
value: 58.118
- type: mrr_at_100
value: 58.489999999999995
- type: mrr_at_1000
value: 58.489999999999995
- type: mrr_at_3
value: 53.061
- type: mrr_at_5
value: 57.041
- type: ndcg_at_1
value: 40.816
- type: ndcg_at_10
value: 30.567
- type: ndcg_at_100
value: 42.44
- type: ndcg_at_1000
value: 53.480000000000004
- type: ndcg_at_3
value: 36.016
- type: ndcg_at_5
value: 34.257
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 25.714
- type: precision_at_100
value: 8.429
- type: precision_at_1000
value: 1.5939999999999999
- type: precision_at_3
value: 36.735
- type: precision_at_5
value: 33.878
- type: recall_at_1
value: 2.976
- type: recall_at_10
value: 17.854999999999997
- type: recall_at_100
value: 51.833
- type: recall_at_1000
value: 86.223
- type: recall_at_3
value: 7.887
- type: recall_at_5
value: 12.026
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 85.1174
- type: ap
value: 30.169441069345748
- type: f1
value: 69.79254701873245
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 72.58347481607245
- type: f1
value: 72.74877295564937
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.90586138221305
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.35769207844072
- type: cos_sim_ap
value: 77.9645072410354
- type: cos_sim_f1
value: 71.32352941176471
- type: cos_sim_precision
value: 66.5903890160183
- type: cos_sim_recall
value: 76.78100263852242
- type: dot_accuracy
value: 87.37557370209214
- type: dot_ap
value: 77.96250046429908
- type: dot_f1
value: 71.28932757557064
- type: dot_precision
value: 66.95249130938586
- type: dot_recall
value: 76.22691292875989
- type: euclidean_accuracy
value: 87.35173153722357
- type: euclidean_ap
value: 77.96520460741593
- type: euclidean_f1
value: 71.32470733210104
- type: euclidean_precision
value: 66.91329479768785
- type: euclidean_recall
value: 76.35883905013192
- type: manhattan_accuracy
value: 87.25636287774931
- type: manhattan_ap
value: 77.77752485611796
- type: manhattan_f1
value: 71.18148599269183
- type: manhattan_precision
value: 66.10859728506787
- type: manhattan_recall
value: 77.0976253298153
- type: max_accuracy
value: 87.37557370209214
- type: max_ap
value: 77.96520460741593
- type: max_f1
value: 71.32470733210104
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.38176737687739
- type: cos_sim_ap
value: 86.58811861657401
- type: cos_sim_f1
value: 79.09430644097604
- type: cos_sim_precision
value: 75.45085977911366
- type: cos_sim_recall
value: 83.10748383122882
- type: dot_accuracy
value: 89.38370784336554
- type: dot_ap
value: 86.58840606004333
- type: dot_f1
value: 79.10179860068133
- type: dot_precision
value: 75.44546153308643
- type: dot_recall
value: 83.13058207576223
- type: euclidean_accuracy
value: 89.38564830985369
- type: euclidean_ap
value: 86.58820721061164
- type: euclidean_f1
value: 79.09070942235888
- type: euclidean_precision
value: 75.38729937194697
- type: euclidean_recall
value: 83.17677856482906
- type: manhattan_accuracy
value: 89.40699344122326
- type: manhattan_ap
value: 86.60631843011362
- type: manhattan_f1
value: 79.14949970570925
- type: manhattan_precision
value: 75.78191039729502
- type: manhattan_recall
value: 82.83030489682784
- type: max_accuracy
value: 89.40699344122326
- type: max_ap
value: 86.60631843011362
- type: max_f1
value: 79.14949970570925
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 65.58442135663871
- type: cos_sim_spearman
value: 72.2538631361313
- type: euclidean_pearson
value: 70.97255486607429
- type: euclidean_spearman
value: 72.25374250228647
- type: manhattan_pearson
value: 70.83250199989911
- type: manhattan_spearman
value: 72.14819496536272
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 59.99478404929932
- type: cos_sim_spearman
value: 62.61836216999812
- type: euclidean_pearson
value: 66.86429811933593
- type: euclidean_spearman
value: 62.6183520374191
- type: manhattan_pearson
value: 66.8063778911633
- type: manhattan_spearman
value: 62.569607573241115
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.98400000000001
- type: f1
value: 51.21447361350723
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 79.11941660686553
- type: cos_sim_spearman
value: 81.25029594540435
- type: euclidean_pearson
value: 82.06973504238826
- type: euclidean_spearman
value: 81.2501989488524
- type: manhattan_pearson
value: 82.10094630392753
- type: manhattan_spearman
value: 81.27987244392389
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 47.07270168705156
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 45.98511703185043
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.19895157194931
- type: mrr
value: 90.21424603174603
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 88.03317320980119
- type: mrr
value: 89.9461507936508
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 29.037000000000003
- type: map_at_10
value: 42.001
- type: map_at_100
value: 43.773
- type: map_at_1000
value: 43.878
- type: map_at_3
value: 37.637
- type: map_at_5
value: 40.034
- type: mrr_at_1
value: 43.136
- type: mrr_at_10
value: 51.158
- type: mrr_at_100
value: 52.083
- type: mrr_at_1000
value: 52.12
- type: mrr_at_3
value: 48.733
- type: mrr_at_5
value: 50.025
- type: ndcg_at_1
value: 43.136
- type: ndcg_at_10
value: 48.685
- type: ndcg_at_100
value: 55.513
- type: ndcg_at_1000
value: 57.242000000000004
- type: ndcg_at_3
value: 43.329
- type: ndcg_at_5
value: 45.438
- type: precision_at_1
value: 43.136
- type: precision_at_10
value: 10.56
- type: precision_at_100
value: 1.6129999999999998
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 24.064
- type: precision_at_5
value: 17.269000000000002
- type: recall_at_1
value: 29.037000000000003
- type: recall_at_10
value: 59.245000000000005
- type: recall_at_100
value: 87.355
- type: recall_at_1000
value: 98.74000000000001
- type: recall_at_3
value: 42.99
- type: recall_at_5
value: 49.681999999999995
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 82.68190018039687
- type: cos_sim_ap
value: 90.18017125327886
- type: cos_sim_f1
value: 83.64080906868193
- type: cos_sim_precision
value: 79.7076890489303
- type: cos_sim_recall
value: 87.98223053542202
- type: dot_accuracy
value: 82.68190018039687
- type: dot_ap
value: 90.18782350103646
- type: dot_f1
value: 83.64242087729039
- type: dot_precision
value: 79.65313028764805
- type: dot_recall
value: 88.05237315875614
- type: euclidean_accuracy
value: 82.68190018039687
- type: euclidean_ap
value: 90.1801957900632
- type: euclidean_f1
value: 83.63636363636364
- type: euclidean_precision
value: 79.52772506852203
- type: euclidean_recall
value: 88.19265840542437
- type: manhattan_accuracy
value: 82.14070956103427
- type: manhattan_ap
value: 89.96178420101427
- type: manhattan_f1
value: 83.21087838578791
- type: manhattan_precision
value: 78.35605121850475
- type: manhattan_recall
value: 88.70703764320785
- type: max_accuracy
value: 82.68190018039687
- type: max_ap
value: 90.18782350103646
- type: max_f1
value: 83.64242087729039
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 72.234
- type: map_at_10
value: 80.10000000000001
- type: map_at_100
value: 80.36
- type: map_at_1000
value: 80.363
- type: map_at_3
value: 78.315
- type: map_at_5
value: 79.607
- type: mrr_at_1
value: 72.392
- type: mrr_at_10
value: 80.117
- type: mrr_at_100
value: 80.36999999999999
- type: mrr_at_1000
value: 80.373
- type: mrr_at_3
value: 78.469
- type: mrr_at_5
value: 79.633
- type: ndcg_at_1
value: 72.392
- type: ndcg_at_10
value: 83.651
- type: ndcg_at_100
value: 84.749
- type: ndcg_at_1000
value: 84.83000000000001
- type: ndcg_at_3
value: 80.253
- type: ndcg_at_5
value: 82.485
- type: precision_at_1
value: 72.392
- type: precision_at_10
value: 9.557
- type: precision_at_100
value: 1.004
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 28.732000000000003
- type: precision_at_5
value: 18.377
- type: recall_at_1
value: 72.234
- type: recall_at_10
value: 94.573
- type: recall_at_100
value: 99.368
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 85.669
- type: recall_at_5
value: 91.01700000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 26.173999999999996
- type: map_at_10
value: 80.04
- type: map_at_100
value: 82.94500000000001
- type: map_at_1000
value: 82.98100000000001
- type: map_at_3
value: 55.562999999999995
- type: map_at_5
value: 69.89800000000001
- type: mrr_at_1
value: 89.5
- type: mrr_at_10
value: 92.996
- type: mrr_at_100
value: 93.06400000000001
- type: mrr_at_1000
value: 93.065
- type: mrr_at_3
value: 92.658
- type: mrr_at_5
value: 92.84599999999999
- type: ndcg_at_1
value: 89.5
- type: ndcg_at_10
value: 87.443
- type: ndcg_at_100
value: 90.253
- type: ndcg_at_1000
value: 90.549
- type: ndcg_at_3
value: 85.874
- type: ndcg_at_5
value: 84.842
- type: precision_at_1
value: 89.5
- type: precision_at_10
value: 41.805
- type: precision_at_100
value: 4.827
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 76.85
- type: precision_at_5
value: 64.8
- type: recall_at_1
value: 26.173999999999996
- type: recall_at_10
value: 89.101
- type: recall_at_100
value: 98.08099999999999
- type: recall_at_1000
value: 99.529
- type: recall_at_3
value: 57.902
- type: recall_at_5
value: 74.602
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 56.10000000000001
- type: map_at_10
value: 66.15299999999999
- type: map_at_100
value: 66.625
- type: map_at_1000
value: 66.636
- type: map_at_3
value: 63.632999999999996
- type: map_at_5
value: 65.293
- type: mrr_at_1
value: 56.10000000000001
- type: mrr_at_10
value: 66.15299999999999
- type: mrr_at_100
value: 66.625
- type: mrr_at_1000
value: 66.636
- type: mrr_at_3
value: 63.632999999999996
- type: mrr_at_5
value: 65.293
- type: ndcg_at_1
value: 56.10000000000001
- type: ndcg_at_10
value: 71.146
- type: ndcg_at_100
value: 73.27799999999999
- type: ndcg_at_1000
value: 73.529
- type: ndcg_at_3
value: 66.09
- type: ndcg_at_5
value: 69.08999999999999
- type: precision_at_1
value: 56.10000000000001
- type: precision_at_10
value: 8.68
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 24.4
- type: precision_at_5
value: 16.1
- type: recall_at_1
value: 56.10000000000001
- type: recall_at_10
value: 86.8
- type: recall_at_100
value: 96.39999999999999
- type: recall_at_1000
value: 98.3
- type: recall_at_3
value: 73.2
- type: recall_at_5
value: 80.5
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 54.52096960369373
- type: f1
value: 40.930845295808695
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 86.51031894934334
- type: ap
value: 55.9516014323483
- type: f1
value: 81.54813679326381
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_pearson
value: 69.67437838574276
- type: cos_sim_spearman
value: 73.81314174653045
- type: euclidean_pearson
value: 72.63430276680275
- type: euclidean_spearman
value: 73.81358736777001
- type: manhattan_pearson
value: 72.58743833842829
- type: manhattan_spearman
value: 73.7590419009179
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 31.648613483640254
- type: mrr
value: 30.37420634920635
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 73.28099999999999
- type: map_at_10
value: 81.977
- type: map_at_100
value: 82.222
- type: map_at_1000
value: 82.22699999999999
- type: map_at_3
value: 80.441
- type: map_at_5
value: 81.46600000000001
- type: mrr_at_1
value: 75.673
- type: mrr_at_10
value: 82.41000000000001
- type: mrr_at_100
value: 82.616
- type: mrr_at_1000
value: 82.621
- type: mrr_at_3
value: 81.094
- type: mrr_at_5
value: 81.962
- type: ndcg_at_1
value: 75.673
- type: ndcg_at_10
value: 85.15599999999999
- type: ndcg_at_100
value: 86.151
- type: ndcg_at_1000
value: 86.26899999999999
- type: ndcg_at_3
value: 82.304
- type: ndcg_at_5
value: 84.009
- type: precision_at_1
value: 75.673
- type: precision_at_10
value: 10.042
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 30.673000000000002
- type: precision_at_5
value: 19.326999999999998
- type: recall_at_1
value: 73.28099999999999
- type: recall_at_10
value: 94.446
- type: recall_at_100
value: 98.737
- type: recall_at_1000
value: 99.649
- type: recall_at_3
value: 86.984
- type: recall_at_5
value: 91.024
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 81.08607935440484
- type: f1
value: 78.24879986066307
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 86.05917955615332
- type: f1
value: 85.05279279434997
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 56.2
- type: map_at_10
value: 62.57899999999999
- type: map_at_100
value: 63.154999999999994
- type: map_at_1000
value: 63.193
- type: map_at_3
value: 61.217
- type: map_at_5
value: 62.012
- type: mrr_at_1
value: 56.3
- type: mrr_at_10
value: 62.629000000000005
- type: mrr_at_100
value: 63.205999999999996
- type: mrr_at_1000
value: 63.244
- type: mrr_at_3
value: 61.267
- type: mrr_at_5
value: 62.062
- type: ndcg_at_1
value: 56.2
- type: ndcg_at_10
value: 65.592
- type: ndcg_at_100
value: 68.657
- type: ndcg_at_1000
value: 69.671
- type: ndcg_at_3
value: 62.808
- type: ndcg_at_5
value: 64.24499999999999
- type: precision_at_1
value: 56.2
- type: precision_at_10
value: 7.5
- type: precision_at_100
value: 0.899
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 22.467000000000002
- type: precision_at_5
value: 14.180000000000001
- type: recall_at_1
value: 56.2
- type: recall_at_10
value: 75.0
- type: recall_at_100
value: 89.9
- type: recall_at_1000
value: 97.89999999999999
- type: recall_at_3
value: 67.4
- type: recall_at_5
value: 70.89999999999999
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 76.87666666666667
- type: f1
value: 76.7317686219665
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 79.64266377910124
- type: cos_sim_ap
value: 84.78274442344829
- type: cos_sim_f1
value: 81.16947472745292
- type: cos_sim_precision
value: 76.47058823529412
- type: cos_sim_recall
value: 86.48363252375924
- type: dot_accuracy
value: 79.64266377910124
- type: dot_ap
value: 84.7851404063692
- type: dot_f1
value: 81.16947472745292
- type: dot_precision
value: 76.47058823529412
- type: dot_recall
value: 86.48363252375924
- type: euclidean_accuracy
value: 79.64266377910124
- type: euclidean_ap
value: 84.78068373762378
- type: euclidean_f1
value: 81.14794656110837
- type: euclidean_precision
value: 76.35009310986965
- type: euclidean_recall
value: 86.58922914466737
- type: manhattan_accuracy
value: 79.48023822414727
- type: manhattan_ap
value: 84.72928897427576
- type: manhattan_f1
value: 81.32084770823064
- type: manhattan_precision
value: 76.24768946395564
- type: manhattan_recall
value: 87.11721224920802
- type: max_accuracy
value: 79.64266377910124
- type: max_ap
value: 84.7851404063692
- type: max_f1
value: 81.32084770823064
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 94.3
- type: ap
value: 92.8664032274438
- type: f1
value: 94.29311102997727
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 48.51392279882909
- type: cos_sim_spearman
value: 54.06338895994974
- type: euclidean_pearson
value: 52.58480559573412
- type: euclidean_spearman
value: 54.06417276612201
- type: manhattan_pearson
value: 52.69525121721343
- type: manhattan_spearman
value: 54.048147455389675
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 29.728387290757325
- type: cos_sim_spearman
value: 31.366121633635284
- type: euclidean_pearson
value: 29.14588368552961
- type: euclidean_spearman
value: 31.36764411112844
- type: manhattan_pearson
value: 29.63517350523121
- type: manhattan_spearman
value: 31.94157020583762
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 63.64868296271406
- type: cos_sim_spearman
value: 66.12800618164744
- type: euclidean_pearson
value: 63.21405767340238
- type: euclidean_spearman
value: 66.12786567790748
- type: manhattan_pearson
value: 64.04300276525848
- type: manhattan_spearman
value: 66.5066857145652
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 81.2302623912794
- type: cos_sim_spearman
value: 81.16833673266562
- type: euclidean_pearson
value: 79.47647843876024
- type: euclidean_spearman
value: 81.16944349524972
- type: manhattan_pearson
value: 79.84947238492208
- type: manhattan_spearman
value: 81.64626599410026
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 67.80129586475687
- type: mrr
value: 77.77402311635554
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 28.666999999999998
- type: map_at_10
value: 81.063
- type: map_at_100
value: 84.504
- type: map_at_1000
value: 84.552
- type: map_at_3
value: 56.897
- type: map_at_5
value: 70.073
- type: mrr_at_1
value: 92.087
- type: mrr_at_10
value: 94.132
- type: mrr_at_100
value: 94.19800000000001
- type: mrr_at_1000
value: 94.19999999999999
- type: mrr_at_3
value: 93.78999999999999
- type: mrr_at_5
value: 94.002
- type: ndcg_at_1
value: 92.087
- type: ndcg_at_10
value: 87.734
- type: ndcg_at_100
value: 90.736
- type: ndcg_at_1000
value: 91.184
- type: ndcg_at_3
value: 88.78
- type: ndcg_at_5
value: 87.676
- type: precision_at_1
value: 92.087
- type: precision_at_10
value: 43.46
- type: precision_at_100
value: 5.07
- type: precision_at_1000
value: 0.518
- type: precision_at_3
value: 77.49000000000001
- type: precision_at_5
value: 65.194
- type: recall_at_1
value: 28.666999999999998
- type: recall_at_10
value: 86.632
- type: recall_at_100
value: 96.646
- type: recall_at_1000
value: 98.917
- type: recall_at_3
value: 58.333999999999996
- type: recall_at_5
value: 72.974
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 52.971999999999994
- type: f1
value: 50.2898280984929
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 86.0797948663824
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 85.10759092255017
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 65.60000000000001
- type: map_at_10
value: 74.773
- type: map_at_100
value: 75.128
- type: map_at_1000
value: 75.136
- type: map_at_3
value: 73.05
- type: map_at_5
value: 74.13499999999999
- type: mrr_at_1
value: 65.60000000000001
- type: mrr_at_10
value: 74.773
- type: mrr_at_100
value: 75.128
- type: mrr_at_1000
value: 75.136
- type: mrr_at_3
value: 73.05
- type: mrr_at_5
value: 74.13499999999999
- type: ndcg_at_1
value: 65.60000000000001
- type: ndcg_at_10
value: 78.84299999999999
- type: ndcg_at_100
value: 80.40899999999999
- type: ndcg_at_1000
value: 80.57
- type: ndcg_at_3
value: 75.40599999999999
- type: ndcg_at_5
value: 77.351
- type: precision_at_1
value: 65.60000000000001
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 27.400000000000002
- type: precision_at_5
value: 17.380000000000003
- type: recall_at_1
value: 65.60000000000001
- type: recall_at_10
value: 91.4
- type: recall_at_100
value: 98.4
- type: recall_at_1000
value: 99.6
- type: recall_at_3
value: 82.19999999999999
- type: recall_at_5
value: 86.9
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 89.47
- type: ap
value: 75.59561751845389
- type: f1
value: 87.95207751382563
- dataset:
config: default
name: MTEB AlloProfClusteringP2P
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 76.05592323841036
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloProfClusteringS2S
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 64.51718058866508
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloprofReranking
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
split: test
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
metrics:
- type: map
value: 73.08278490943373
- type: mrr
value: 74.66561454570449
task:
type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: map_at_1
value: 38.912
- type: map_at_10
value: 52.437999999999995
- type: map_at_100
value: 53.38
- type: map_at_1000
value: 53.427
- type: map_at_3
value: 48.879
- type: map_at_5
value: 50.934000000000005
- type: mrr_at_1
value: 44.085
- type: mrr_at_10
value: 55.337
- type: mrr_at_100
value: 56.016999999999996
- type: mrr_at_1000
value: 56.043
- type: mrr_at_3
value: 52.55499999999999
- type: mrr_at_5
value: 54.20399999999999
- type: ndcg_at_1
value: 44.085
- type: ndcg_at_10
value: 58.876
- type: ndcg_at_100
value: 62.714000000000006
- type: ndcg_at_1000
value: 63.721000000000004
- type: ndcg_at_3
value: 52.444
- type: ndcg_at_5
value: 55.692
- type: precision_at_1
value: 44.085
- type: precision_at_10
value: 9.21
- type: precision_at_100
value: 1.164
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 23.043
- type: precision_at_5
value: 15.898000000000001
- type: recall_at_1
value: 38.912
- type: recall_at_10
value: 75.577
- type: recall_at_100
value: 92.038
- type: recall_at_1000
value: 99.325
- type: recall_at_3
value: 58.592
- type: recall_at_5
value: 66.235
task:
type: Retrieval
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 55.532000000000004
- type: f1
value: 52.5783943471605
task:
type: Classification
- dataset:
config: default
name: MTEB BSARDRetrieval
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
metrics:
- type: map_at_1
value: 8.108
- type: map_at_10
value: 14.710999999999999
- type: map_at_100
value: 15.891
- type: map_at_1000
value: 15.983
- type: map_at_3
value: 12.237
- type: map_at_5
value: 13.679
- type: mrr_at_1
value: 8.108
- type: mrr_at_10
value: 14.710999999999999
- type: mrr_at_100
value: 15.891
- type: mrr_at_1000
value: 15.983
- type: mrr_at_3
value: 12.237
- type: mrr_at_5
value: 13.679
- type: ndcg_at_1
value: 8.108
- type: ndcg_at_10
value: 18.796
- type: ndcg_at_100
value: 25.098
- type: ndcg_at_1000
value: 27.951999999999998
- type: ndcg_at_3
value: 13.712
- type: ndcg_at_5
value: 16.309
- type: precision_at_1
value: 8.108
- type: precision_at_10
value: 3.198
- type: precision_at_100
value: 0.626
- type: precision_at_1000
value: 0.086
- type: precision_at_3
value: 6.006
- type: precision_at_5
value: 4.865
- type: recall_at_1
value: 8.108
- type: recall_at_10
value: 31.982
- type: recall_at_100
value: 62.613
- type: recall_at_1000
value: 86.036
- type: recall_at_3
value: 18.018
- type: recall_at_5
value: 24.324
task:
type: Retrieval
- dataset:
config: default
name: MTEB HALClusteringS2S
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
split: test
type: lyon-nlp/clustering-hal-s2s
metrics:
- type: v_measure
value: 30.833269778867116
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringP2P
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 50.0281928004713
task:
type: Clustering
- dataset:
config: default
name: MTEB MLSUMClusteringS2S
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
split: test
type: mlsum
metrics:
- type: v_measure
value: 43.699961510636534
task:
type: Clustering
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 96.68963357344191
- type: f1
value: 96.45175170820961
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 87.46946445349202
- type: f1
value: 65.79860440988624
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClassification (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: accuracy
value: 82.60663507109005
- type: f1
value: 77.20462646604777
task:
type: Classification
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringP2P (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 60.19311264967803
task:
type: Clustering
- dataset:
config: fra
name: MTEB MasakhaNEWSClusteringS2S (fra)
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
split: test
type: masakhane/masakhanews
metrics:
- type: v_measure
value: 63.6235764409785
task:
type: Clustering
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 81.65097511768661
- type: f1
value: 78.77796091490924
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 86.64425016812373
- type: f1
value: 85.4912728670017
task:
type: Classification
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
metrics:
- type: map_at_1
value: 35.913000000000004
- type: map_at_10
value: 48.147
- type: map_at_100
value: 48.91
- type: map_at_1000
value: 48.949
- type: map_at_3
value: 45.269999999999996
- type: map_at_5
value: 47.115
- type: mrr_at_1
value: 35.913000000000004
- type: mrr_at_10
value: 48.147
- type: mrr_at_100
value: 48.91
- type: mrr_at_1000
value: 48.949
- type: mrr_at_3
value: 45.269999999999996
- type: mrr_at_5
value: 47.115
- type: ndcg_at_1
value: 35.913000000000004
- type: ndcg_at_10
value: 54.03
- type: ndcg_at_100
value: 57.839
- type: ndcg_at_1000
value: 58.925000000000004
- type: ndcg_at_3
value: 48.217999999999996
- type: ndcg_at_5
value: 51.56699999999999
- type: precision_at_1
value: 35.913000000000004
- type: precision_at_10
value: 7.244000000000001
- type: precision_at_100
value: 0.9039999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 18.905
- type: precision_at_5
value: 12.981000000000002
- type: recall_at_1
value: 35.913000000000004
- type: recall_at_10
value: 72.441
- type: recall_at_100
value: 90.41799999999999
- type: recall_at_1000
value: 99.099
- type: recall_at_3
value: 56.716
- type: recall_at_5
value: 64.90599999999999
task:
type: Retrieval
- dataset:
config: fr
name: MTEB OpusparcusPC (fr)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test
type: GEM/opusparcus
metrics:
- type: cos_sim_accuracy
value: 99.90069513406156
- type: cos_sim_ap
value: 100.0
- type: cos_sim_f1
value: 99.95032290114257
- type: cos_sim_precision
value: 100.0
- type: cos_sim_recall
value: 99.90069513406156
- type: dot_accuracy
value: 99.90069513406156
- type: dot_ap
value: 100.0
- type: dot_f1
value: 99.95032290114257
- type: dot_precision
value: 100.0
- type: dot_recall
value: 99.90069513406156
- type: euclidean_accuracy
value: 99.90069513406156
- type: euclidean_ap
value: 100.0
- type: euclidean_f1
value: 99.95032290114257
- type: euclidean_precision
value: 100.0
- type: euclidean_recall
value: 99.90069513406156
- type: manhattan_accuracy
value: 99.90069513406156
- type: manhattan_ap
value: 100.0
- type: manhattan_f1
value: 99.95032290114257
- type: manhattan_precision
value: 100.0
- type: manhattan_recall
value: 99.90069513406156
- type: max_accuracy
value: 99.90069513406156
- type: max_ap
value: 100.0
- type: max_f1
value: 99.95032290114257
task:
type: PairClassification
- dataset:
config: fr
name: MTEB PawsX (fr)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: paws-x
metrics:
- type: cos_sim_accuracy
value: 75.25
- type: cos_sim_ap
value: 80.86376001270014
- type: cos_sim_f1
value: 73.65945437441204
- type: cos_sim_precision
value: 64.02289452166802
- type: cos_sim_recall
value: 86.71096345514951
- type: dot_accuracy
value: 75.25
- type: dot_ap
value: 80.93686107633002
- type: dot_f1
value: 73.65945437441204
- type: dot_precision
value: 64.02289452166802
- type: dot_recall
value: 86.71096345514951
- type: euclidean_accuracy
value: 75.25
- type: euclidean_ap
value: 80.86379136218862
- type: euclidean_f1
value: 73.65945437441204
- type: euclidean_precision
value: 64.02289452166802
- type: euclidean_recall
value: 86.71096345514951
- type: manhattan_accuracy
value: 75.3
- type: manhattan_ap
value: 80.87826606097734
- type: manhattan_f1
value: 73.68421052631581
- type: manhattan_precision
value: 64.0
- type: manhattan_recall
value: 86.82170542635659
- type: max_accuracy
value: 75.3
- type: max_ap
value: 80.93686107633002
- type: max_f1
value: 73.68421052631581
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICKFr
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
metrics:
- type: cos_sim_pearson
value: 81.42349425981143
- type: cos_sim_spearman
value: 78.90454327031226
- type: euclidean_pearson
value: 78.39086497435166
- type: euclidean_spearman
value: 78.9046133980509
- type: manhattan_pearson
value: 78.63743094286502
- type: manhattan_spearman
value: 79.12136348449269
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 81.452697919749
- type: cos_sim_spearman
value: 82.58116836039301
- type: euclidean_pearson
value: 81.04038478932786
- type: euclidean_spearman
value: 82.58116836039301
- type: manhattan_pearson
value: 81.37075396187771
- type: manhattan_spearman
value: 82.73678231355368
task:
type: STS
- dataset:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
split: test
type: stsb_multi_mt
metrics:
- type: cos_sim_pearson
value: 85.7419764013806
- type: cos_sim_spearman
value: 85.46085808849622
- type: euclidean_pearson
value: 83.70449639870063
- type: euclidean_spearman
value: 85.46159013076233
- type: manhattan_pearson
value: 83.95259510313929
- type: manhattan_spearman
value: 85.8029724659458
task:
type: STS
- dataset:
config: default
name: MTEB SummEvalFr
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
split: test
type: lyon-nlp/summarization-summeval-fr-p2p
metrics:
- type: cos_sim_pearson
value: 32.61063271753325
- type: cos_sim_spearman
value: 31.454589417353603
- type: dot_pearson
value: 32.6106288643431
- type: dot_spearman
value: 31.454589417353603
task:
type: Summarization
- dataset:
config: default
name: MTEB SyntecReranking
revision: b205c5084a0934ce8af14338bf03feb19499c84d
split: test
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
metrics:
- type: map
value: 84.31666666666666
- type: mrr
value: 84.31666666666666
task:
type: Reranking
- dataset:
config: default
name: MTEB SyntecRetrieval
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
split: test
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
metrics:
- type: map_at_1
value: 63.0
- type: map_at_10
value: 73.471
- type: map_at_100
value: 73.87
- type: map_at_1000
value: 73.87
- type: map_at_3
value: 70.5
- type: map_at_5
value: 73.05
- type: mrr_at_1
value: 63.0
- type: mrr_at_10
value: 73.471
- type: mrr_at_100
value: 73.87
- type: mrr_at_1000
value: 73.87
- type: mrr_at_3
value: 70.5
- type: mrr_at_5
value: 73.05
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 78.255
- type: ndcg_at_100
value: 79.88
- type: ndcg_at_1000
value: 79.88
- type: ndcg_at_3
value: 72.702
- type: ndcg_at_5
value: 77.264
- type: precision_at_1
value: 63.0
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 26.333000000000002
- type: precision_at_5
value: 18.0
- type: recall_at_1
value: 63.0
- type: recall_at_10
value: 93.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 79.0
- type: recall_at_5
value: 90.0
task:
type: Retrieval
- dataset:
config: fr
name: MTEB XPQARetrieval (fr)
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
split: test
type: jinaai/xpqa
metrics:
- type: map_at_1
value: 40.338
- type: map_at_10
value: 61.927
- type: map_at_100
value: 63.361999999999995
- type: map_at_1000
value: 63.405
- type: map_at_3
value: 55.479
- type: map_at_5
value: 59.732
- type: mrr_at_1
value: 63.551
- type: mrr_at_10
value: 71.006
- type: mrr_at_100
value: 71.501
- type: mrr_at_1000
value: 71.509
- type: mrr_at_3
value: 69.07
- type: mrr_at_5
value: 70.165
- type: ndcg_at_1
value: 63.551
- type: ndcg_at_10
value: 68.297
- type: ndcg_at_100
value: 73.13199999999999
- type: ndcg_at_1000
value: 73.751
- type: ndcg_at_3
value: 62.999
- type: ndcg_at_5
value: 64.89
- type: precision_at_1
value: 63.551
- type: precision_at_10
value: 15.661
- type: precision_at_100
value: 1.9789999999999999
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 38.273
- type: precision_at_5
value: 27.61
- type: recall_at_1
value: 40.338
- type: recall_at_10
value: 77.267
- type: recall_at_100
value: 95.892
- type: recall_at_1000
value: 99.75500000000001
- type: recall_at_3
value: 60.36
- type: recall_at_5
value: 68.825
task:
type: Retrieval
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 51.36126303874126
task:
type: Clustering
- dataset:
config: default
name: MTEB AllegroReviews
revision: None
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 67.13717693836979
- type: f1
value: 57.27609848003782
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna-PL
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
split: test
type: clarin-knext/arguana-pl
metrics:
- type: map_at_1
value: 35.276999999999994
- type: map_at_10
value: 51.086
- type: map_at_100
value: 51.788000000000004
- type: map_at_1000
value: 51.791
- type: map_at_3
value: 46.147
- type: map_at_5
value: 49.078
- type: mrr_at_1
value: 35.917
- type: mrr_at_10
value: 51.315999999999995
- type: mrr_at_100
value: 52.018
- type: mrr_at_1000
value: 52.022
- type: mrr_at_3
value: 46.349000000000004
- type: mrr_at_5
value: 49.297000000000004
- type: ndcg_at_1
value: 35.276999999999994
- type: ndcg_at_10
value: 59.870999999999995
- type: ndcg_at_100
value: 62.590999999999994
- type: ndcg_at_1000
value: 62.661
- type: ndcg_at_3
value: 49.745
- type: ndcg_at_5
value: 55.067
- type: precision_at_1
value: 35.276999999999994
- type: precision_at_10
value: 8.791
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.057
- type: precision_at_5
value: 14.637
- type: recall_at_1
value: 35.276999999999994
- type: recall_at_10
value: 87.909
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 60.171
- type: recall_at_5
value: 73.18599999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB CBD
revision: None
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 78.03000000000002
- type: ap
value: 29.12548553897622
- type: f1
value: 66.54857118886073
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: None
split: test
type: PL-MTEB/cdsce-pairclassification
metrics:
- type: cos_sim_accuracy
value: 89.0
- type: cos_sim_ap
value: 76.75437826834582
- type: cos_sim_f1
value: 66.4850136239782
- type: cos_sim_precision
value: 68.92655367231639
- type: cos_sim_recall
value: 64.21052631578948
- type: dot_accuracy
value: 89.0
- type: dot_ap
value: 76.75437826834582
- type: dot_f1
value: 66.4850136239782
- type: dot_precision
value: 68.92655367231639
- type: dot_recall
value: 64.21052631578948
- type: euclidean_accuracy
value: 89.0
- type: euclidean_ap
value: 76.75437826834582
- type: euclidean_f1
value: 66.4850136239782
- type: euclidean_precision
value: 68.92655367231639
- type: euclidean_recall
value: 64.21052631578948
- type: manhattan_accuracy
value: 89.0
- type: manhattan_ap
value: 76.66074220647083
- type: manhattan_f1
value: 66.47058823529412
- type: manhattan_precision
value: 75.33333333333333
- type: manhattan_recall
value: 59.473684210526315
- type: max_accuracy
value: 89.0
- type: max_ap
value: 76.75437826834582
- type: max_f1
value: 66.4850136239782
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: None
split: test
type: PL-MTEB/cdscr-sts
metrics:
- type: cos_sim_pearson
value: 93.12903172428328
- type: cos_sim_spearman
value: 92.66381487060741
- type: euclidean_pearson
value: 90.37278396708922
- type: euclidean_spearman
value: 92.66381487060741
- type: manhattan_pearson
value: 90.32503296540962
- type: manhattan_spearman
value: 92.6902938354313
task:
type: STS
- dataset:
config: default
name: MTEB DBPedia-PL
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
split: test
type: clarin-knext/dbpedia-pl
metrics:
- type: map_at_1
value: 8.83
- type: map_at_10
value: 18.326
- type: map_at_100
value: 26.496
- type: map_at_1000
value: 28.455000000000002
- type: map_at_3
value: 12.933
- type: map_at_5
value: 15.168000000000001
- type: mrr_at_1
value: 66.0
- type: mrr_at_10
value: 72.76700000000001
- type: mrr_at_100
value: 73.203
- type: mrr_at_1000
value: 73.219
- type: mrr_at_3
value: 71.458
- type: mrr_at_5
value: 72.246
- type: ndcg_at_1
value: 55.375
- type: ndcg_at_10
value: 41.3
- type: ndcg_at_100
value: 45.891
- type: ndcg_at_1000
value: 52.905
- type: ndcg_at_3
value: 46.472
- type: ndcg_at_5
value: 43.734
- type: precision_at_1
value: 66.0
- type: precision_at_10
value: 33.074999999999996
- type: precision_at_100
value: 11.094999999999999
- type: precision_at_1000
value: 2.374
- type: precision_at_3
value: 48.583
- type: precision_at_5
value: 42.0
- type: recall_at_1
value: 8.83
- type: recall_at_10
value: 22.587
- type: recall_at_100
value: 50.61600000000001
- type: recall_at_1000
value: 73.559
- type: recall_at_3
value: 13.688
- type: recall_at_5
value: 16.855
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA-PL
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
split: test
type: clarin-knext/fiqa-pl
metrics:
- type: map_at_1
value: 20.587
- type: map_at_10
value: 33.095
- type: map_at_100
value: 35.24
- type: map_at_1000
value: 35.429
- type: map_at_3
value: 28.626
- type: map_at_5
value: 31.136999999999997
- type: mrr_at_1
value: 40.586
- type: mrr_at_10
value: 49.033
- type: mrr_at_100
value: 49.952999999999996
- type: mrr_at_1000
value: 49.992
- type: mrr_at_3
value: 46.553
- type: mrr_at_5
value: 48.035
- type: ndcg_at_1
value: 40.586
- type: ndcg_at_10
value: 41.046
- type: ndcg_at_100
value: 48.586
- type: ndcg_at_1000
value: 51.634
- type: ndcg_at_3
value: 36.773
- type: ndcg_at_5
value: 38.389
- type: precision_at_1
value: 40.586
- type: precision_at_10
value: 11.466
- type: precision_at_100
value: 1.909
- type: precision_at_1000
value: 0.245
- type: precision_at_3
value: 24.434
- type: precision_at_5
value: 18.426000000000002
- type: recall_at_1
value: 20.587
- type: recall_at_10
value: 47.986000000000004
- type: recall_at_100
value: 75.761
- type: recall_at_1000
value: 94.065
- type: recall_at_3
value: 33.339
- type: recall_at_5
value: 39.765
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA-PL
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
split: test
type: clarin-knext/hotpotqa-pl
metrics:
- type: map_at_1
value: 40.878
- type: map_at_10
value: 58.775999999999996
- type: map_at_100
value: 59.632
- type: map_at_1000
value: 59.707
- type: map_at_3
value: 56.074
- type: map_at_5
value: 57.629
- type: mrr_at_1
value: 81.756
- type: mrr_at_10
value: 86.117
- type: mrr_at_100
value: 86.299
- type: mrr_at_1000
value: 86.30600000000001
- type: mrr_at_3
value: 85.345
- type: mrr_at_5
value: 85.832
- type: ndcg_at_1
value: 81.756
- type: ndcg_at_10
value: 67.608
- type: ndcg_at_100
value: 70.575
- type: ndcg_at_1000
value: 71.99600000000001
- type: ndcg_at_3
value: 63.723
- type: ndcg_at_5
value: 65.70700000000001
- type: precision_at_1
value: 81.756
- type: precision_at_10
value: 13.619
- type: precision_at_100
value: 1.5939999999999999
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 39.604
- type: precision_at_5
value: 25.332
- type: recall_at_1
value: 40.878
- type: recall_at_10
value: 68.096
- type: recall_at_100
value: 79.696
- type: recall_at_1000
value: 89.082
- type: recall_at_3
value: 59.406000000000006
- type: recall_at_5
value: 63.329
task:
type: Retrieval
- dataset:
config: default
name: MTEB MSMARCO-PL
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
split: test
type: clarin-knext/msmarco-pl
metrics:
- type: map_at_1
value: 2.1839999999999997
- type: map_at_10
value: 11.346
- type: map_at_100
value: 30.325000000000003
- type: map_at_1000
value: 37.806
- type: map_at_3
value: 4.842
- type: map_at_5
value: 6.891
- type: mrr_at_1
value: 86.047
- type: mrr_at_10
value: 89.14699999999999
- type: mrr_at_100
value: 89.46600000000001
- type: mrr_at_1000
value: 89.46600000000001
- type: mrr_at_3
value: 89.14699999999999
- type: mrr_at_5
value: 89.14699999999999
- type: ndcg_at_1
value: 67.829
- type: ndcg_at_10
value: 62.222
- type: ndcg_at_100
value: 55.337
- type: ndcg_at_1000
value: 64.076
- type: ndcg_at_3
value: 68.12700000000001
- type: ndcg_at_5
value: 64.987
- type: precision_at_1
value: 86.047
- type: precision_at_10
value: 69.535
- type: precision_at_100
value: 32.93
- type: precision_at_1000
value: 6.6049999999999995
- type: precision_at_3
value: 79.845
- type: precision_at_5
value: 75.349
- type: recall_at_1
value: 2.1839999999999997
- type: recall_at_10
value: 12.866
- type: recall_at_100
value: 43.505
- type: recall_at_1000
value: 72.366
- type: recall_at_3
value: 4.947
- type: recall_at_5
value: 7.192
task:
type: Retrieval
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 80.75319435104238
- type: f1
value: 77.58961444860606
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 85.54472091459313
- type: f1
value: 84.29498563572106
task:
type: Classification
- dataset:
config: default
name: MTEB NFCorpus-PL
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
split: test
type: clarin-knext/nfcorpus-pl
metrics:
- type: map_at_1
value: 4.367
- type: map_at_10
value: 10.38
- type: map_at_100
value: 13.516
- type: map_at_1000
value: 14.982000000000001
- type: map_at_3
value: 7.367
- type: map_at_5
value: 8.59
- type: mrr_at_1
value: 41.486000000000004
- type: mrr_at_10
value: 48.886
- type: mrr_at_100
value: 49.657000000000004
- type: mrr_at_1000
value: 49.713
- type: mrr_at_3
value: 46.904
- type: mrr_at_5
value: 48.065000000000005
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 30.885
- type: ndcg_at_100
value: 28.393
- type: ndcg_at_1000
value: 37.428
- type: ndcg_at_3
value: 35.394999999999996
- type: ndcg_at_5
value: 33.391999999999996
- type: precision_at_1
value: 41.486000000000004
- type: precision_at_10
value: 23.437
- type: precision_at_100
value: 7.638
- type: precision_at_1000
value: 2.0389999999999997
- type: precision_at_3
value: 32.817
- type: precision_at_5
value: 28.915999999999997
- type: recall_at_1
value: 4.367
- type: recall_at_10
value: 14.655000000000001
- type: recall_at_100
value: 29.665999999999997
- type: recall_at_1000
value: 62.073
- type: recall_at_3
value: 8.51
- type: recall_at_5
value: 10.689
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ-PL
revision: f171245712cf85dd4700b06bef18001578d0ca8d
split: test
type: clarin-knext/nq-pl
metrics:
- type: map_at_1
value: 28.616000000000003
- type: map_at_10
value: 41.626000000000005
- type: map_at_100
value: 42.689
- type: map_at_1000
value: 42.733
- type: map_at_3
value: 37.729
- type: map_at_5
value: 39.879999999999995
- type: mrr_at_1
value: 32.068000000000005
- type: mrr_at_10
value: 44.029
- type: mrr_at_100
value: 44.87
- type: mrr_at_1000
value: 44.901
- type: mrr_at_3
value: 40.687
- type: mrr_at_5
value: 42.625
- type: ndcg_at_1
value: 32.068000000000005
- type: ndcg_at_10
value: 48.449999999999996
- type: ndcg_at_100
value: 53.13
- type: ndcg_at_1000
value: 54.186
- type: ndcg_at_3
value: 40.983999999999995
- type: ndcg_at_5
value: 44.628
- type: precision_at_1
value: 32.068000000000005
- type: precision_at_10
value: 7.9750000000000005
- type: precision_at_100
value: 1.061
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 18.404999999999998
- type: precision_at_5
value: 13.111
- type: recall_at_1
value: 28.616000000000003
- type: recall_at_10
value: 66.956
- type: recall_at_100
value: 87.657
- type: recall_at_1000
value: 95.548
- type: recall_at_3
value: 47.453
- type: recall_at_5
value: 55.87800000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB PAC
revision: None
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 69.04141326382856
- type: ap
value: 77.47589122111044
- type: f1
value: 66.6332277374775
task:
type: Classification
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 86.4
- type: cos_sim_ap
value: 94.1044939667201
- type: cos_sim_f1
value: 88.78048780487805
- type: cos_sim_precision
value: 87.22044728434504
- type: cos_sim_recall
value: 90.39735099337747
- type: dot_accuracy
value: 86.4
- type: dot_ap
value: 94.1044939667201
- type: dot_f1
value: 88.78048780487805
- type: dot_precision
value: 87.22044728434504
- type: dot_recall
value: 90.39735099337747
- type: euclidean_accuracy
value: 86.4
- type: euclidean_ap
value: 94.1044939667201
- type: euclidean_f1
value: 88.78048780487805
- type: euclidean_precision
value: 87.22044728434504
- type: euclidean_recall
value: 90.39735099337747
- type: manhattan_accuracy
value: 86.4
- type: manhattan_ap
value: 94.11438365697387
- type: manhattan_f1
value: 88.77968877968877
- type: manhattan_precision
value: 87.84440842787681
- type: manhattan_recall
value: 89.73509933774835
- type: max_accuracy
value: 86.4
- type: max_ap
value: 94.11438365697387
- type: max_f1
value: 88.78048780487805
task:
type: PairClassification
- dataset:
config: default
name: MTEB PSC
revision: None
split: test
type: PL-MTEB/psc-pairclassification
metrics:
- type: cos_sim_accuracy
value: 97.86641929499072
- type: cos_sim_ap
value: 99.36904211868182
- type: cos_sim_f1
value: 96.56203288490283
- type: cos_sim_precision
value: 94.72140762463343
- type: cos_sim_recall
value: 98.47560975609755
- type: dot_accuracy
value: 97.86641929499072
- type: dot_ap
value: 99.36904211868183
- type: dot_f1
value: 96.56203288490283
- type: dot_precision
value: 94.72140762463343
- type: dot_recall
value: 98.47560975609755
- type: euclidean_accuracy
value: 97.86641929499072
- type: euclidean_ap
value: 99.36904211868183
- type: euclidean_f1
value: 96.56203288490283
- type: euclidean_precision
value: 94.72140762463343
- type: euclidean_recall
value: 98.47560975609755
- type: manhattan_accuracy
value: 98.14471243042672
- type: manhattan_ap
value: 99.43359540492416
- type: manhattan_f1
value: 96.98795180722892
- type: manhattan_precision
value: 95.83333333333334
- type: manhattan_recall
value: 98.17073170731707
- type: max_accuracy
value: 98.14471243042672
- type: max_ap
value: 99.43359540492416
- type: max_f1
value: 96.98795180722892
task:
type: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: None
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 89.39058171745152
- type: f1
value: 86.8552093529568
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: None
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 74.97975708502024
- type: f1
value: 58.73081628832407
task:
type: Classification
- dataset:
config: default
name: MTEB Quora-PL
revision: 0be27e93455051e531182b85e85e425aba12e9d4
split: test
type: clarin-knext/quora-pl
metrics:
- type: map_at_1
value: 64.917
- type: map_at_10
value: 78.74600000000001
- type: map_at_100
value: 79.501
- type: map_at_1000
value: 79.524
- type: map_at_3
value: 75.549
- type: map_at_5
value: 77.495
- type: mrr_at_1
value: 74.9
- type: mrr_at_10
value: 82.112
- type: mrr_at_100
value: 82.314
- type: mrr_at_1000
value: 82.317
- type: mrr_at_3
value: 80.745
- type: mrr_at_5
value: 81.607
- type: ndcg_at_1
value: 74.83999999999999
- type: ndcg_at_10
value: 83.214
- type: ndcg_at_100
value: 84.997
- type: ndcg_at_1000
value: 85.207
- type: ndcg_at_3
value: 79.547
- type: ndcg_at_5
value: 81.46600000000001
- type: precision_at_1
value: 74.83999999999999
- type: precision_at_10
value: 12.822
- type: precision_at_100
value: 1.506
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 34.903
- type: precision_at_5
value: 23.16
- type: recall_at_1
value: 64.917
- type: recall_at_10
value: 92.27199999999999
- type: recall_at_100
value: 98.715
- type: recall_at_1000
value: 99.854
- type: recall_at_3
value: 82.04599999999999
- type: recall_at_5
value: 87.2
task:
type: Retrieval
- dataset:
config: default
name: MTEB SCIDOCS-PL
revision: 45452b03f05560207ef19149545f168e596c9337
split: test
type: clarin-knext/scidocs-pl
metrics:
- type: map_at_1
value: 3.51
- type: map_at_10
value: 9.046999999999999
- type: map_at_100
value: 10.823
- type: map_at_1000
value: 11.144
- type: map_at_3
value: 6.257
- type: map_at_5
value: 7.648000000000001
- type: mrr_at_1
value: 17.299999999999997
- type: mrr_at_10
value: 27.419
- type: mrr_at_100
value: 28.618
- type: mrr_at_1000
value: 28.685
- type: mrr_at_3
value: 23.817
- type: mrr_at_5
value: 25.927
- type: ndcg_at_1
value: 17.299999999999997
- type: ndcg_at_10
value: 16.084
- type: ndcg_at_100
value: 23.729
- type: ndcg_at_1000
value: 29.476999999999997
- type: ndcg_at_3
value: 14.327000000000002
- type: ndcg_at_5
value: 13.017999999999999
- type: precision_at_1
value: 17.299999999999997
- type: precision_at_10
value: 8.63
- type: precision_at_100
value: 1.981
- type: precision_at_1000
value: 0.336
- type: precision_at_3
value: 13.4
- type: precision_at_5
value: 11.700000000000001
- type: recall_at_1
value: 3.51
- type: recall_at_10
value: 17.518
- type: recall_at_100
value: 40.275
- type: recall_at_1000
value: 68.203
- type: recall_at_3
value: 8.155
- type: recall_at_5
value: 11.875
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-E-PL
revision: None
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics:
- type: cos_sim_accuracy
value: 86.30248675091724
- type: cos_sim_ap
value: 83.6756734006714
- type: cos_sim_f1
value: 74.97367497367497
- type: cos_sim_precision
value: 73.91003460207612
- type: cos_sim_recall
value: 76.06837606837607
- type: dot_accuracy
value: 86.30248675091724
- type: dot_ap
value: 83.6756734006714
- type: dot_f1
value: 74.97367497367497
- type: dot_precision
value: 73.91003460207612
- type: dot_recall
value: 76.06837606837607
- type: euclidean_accuracy
value: 86.30248675091724
- type: euclidean_ap
value: 83.67566984333091
- type: euclidean_f1
value: 74.97367497367497
- type: euclidean_precision
value: 73.91003460207612
- type: euclidean_recall
value: 76.06837606837607
- type: manhattan_accuracy
value: 86.28210354667753
- type: manhattan_ap
value: 83.64216119130171
- type: manhattan_f1
value: 74.92152075340078
- type: manhattan_precision
value: 73.4107997265892
- type: manhattan_recall
value: 76.49572649572649
- type: max_accuracy
value: 86.30248675091724
- type: max_ap
value: 83.6756734006714
- type: max_f1
value: 74.97367497367497
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: None
split: test
type: PL-MTEB/sickr-pl-sts
metrics:
- type: cos_sim_pearson
value: 82.23295940859121
- type: cos_sim_spearman
value: 78.89329160768719
- type: euclidean_pearson
value: 79.56019107076818
- type: euclidean_spearman
value: 78.89330209904084
- type: manhattan_pearson
value: 79.76098513973719
- type: manhattan_spearman
value: 79.05490162570123
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 37.732606308062486
- type: cos_sim_spearman
value: 41.01645667030284
- type: euclidean_pearson
value: 26.61722556367085
- type: euclidean_spearman
value: 41.01645667030284
- type: manhattan_pearson
value: 26.60917378970807
- type: manhattan_spearman
value: 41.51335727617614
task:
type: STS
- dataset:
config: default
name: MTEB SciFact-PL
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
split: test
type: clarin-knext/scifact-pl
metrics:
- type: map_at_1
value: 54.31700000000001
- type: map_at_10
value: 65.564
- type: map_at_100
value: 66.062
- type: map_at_1000
value: 66.08699999999999
- type: map_at_3
value: 62.592999999999996
- type: map_at_5
value: 63.888
- type: mrr_at_1
value: 56.99999999999999
- type: mrr_at_10
value: 66.412
- type: mrr_at_100
value: 66.85900000000001
- type: mrr_at_1000
value: 66.88
- type: mrr_at_3
value: 64.22200000000001
- type: mrr_at_5
value: 65.206
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 70.577
- type: ndcg_at_100
value: 72.879
- type: ndcg_at_1000
value: 73.45
- type: ndcg_at_3
value: 65.5
- type: ndcg_at_5
value: 67.278
- type: precision_at_1
value: 56.99999999999999
- type: precision_at_10
value: 9.667
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.0
- type: precision_at_5
value: 16.933
- type: recall_at_1
value: 54.31700000000001
- type: recall_at_10
value: 85.056
- type: recall_at_100
value: 95.667
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 71.0
- type: recall_at_5
value: 75.672
task:
type: Retrieval
- dataset:
config: default
name: MTEB TRECCOVID-PL
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
split: test
type: clarin-knext/trec-covid-pl
metrics:
- type: map_at_1
value: 0.245
- type: map_at_10
value: 2.051
- type: map_at_100
value: 12.009
- type: map_at_1000
value: 27.448
- type: map_at_3
value: 0.721
- type: map_at_5
value: 1.13
- type: mrr_at_1
value: 88.0
- type: mrr_at_10
value: 93.0
- type: mrr_at_100
value: 93.0
- type: mrr_at_1000
value: 93.0
- type: mrr_at_3
value: 93.0
- type: mrr_at_5
value: 93.0
- type: ndcg_at_1
value: 85.0
- type: ndcg_at_10
value: 80.303
- type: ndcg_at_100
value: 61.23499999999999
- type: ndcg_at_1000
value: 52.978
- type: ndcg_at_3
value: 84.419
- type: ndcg_at_5
value: 82.976
- type: precision_at_1
value: 88.0
- type: precision_at_10
value: 83.39999999999999
- type: precision_at_100
value: 61.96
- type: precision_at_1000
value: 22.648
- type: precision_at_3
value: 89.333
- type: precision_at_5
value: 87.2
- type: recall_at_1
value: 0.245
- type: recall_at_10
value: 2.193
- type: recall_at_100
value: 14.938
- type: recall_at_1000
value: 48.563
- type: recall_at_3
value: 0.738
- type: recall_at_5
value: 1.173
task:
type: Retrieval
---
## gte-Qwen2-7B-instruct
**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024).
Recently, the [**Qwen team**](https://huggingface.co/Qwen) released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models.
The model incorporates several key advancements:
- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
- Instruction tuning, applied solely on the query side for streamlined efficiency
- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
## Model Information
- Model Size: 7B
- Embedding Dimension: 3584
- Max Input Tokens: 32k
## Requirements
```
transformers>=4.39.2
flash_attn>=2.5.6
```
## Usage
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
```
Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice.
### Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Evaluation
### MTEB & C-MTEB
You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-7B-instruct** on MTEB(English)/C-MTEB(Chinese):
| Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) |
|:----:|:---------:|:----------:|:----------:|:----------:|
| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | - | - |
| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | - | - |
| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | - | - |
| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | - | - |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | - | - |
| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | - | - |
| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | - | - |
| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | - | - |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | - | - |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | - | - |
| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | - | - |
| [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | - | - |
| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | - | - |
| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | **68.25** | **67.86** |
| gte-Qwen2-1.5B-instruc(https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | 67.16 | 67.65 | 66.60 | 64.04 |
### GTE Models
The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).
| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |
|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:|
| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB |
| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB |
| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB |
| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB |
| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB |
| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB |
| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB |
| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB |
| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB |
| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB |
| [GTE-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | Multilingual | 32000 | 1536 | 6.62GB |
## Cloud API Services
In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Rhree versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
## Citation
If you find our paper or models helpful, please consider cite:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
``` |
kbulutozler/distilbert-base-uncased-FT-ner-BC2GM | kbulutozler | 2024-10-11T00:14:34Z | 197 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"BC2GM",
"NER",
"en",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-10T23:37:17Z | ---
library_name: transformers
tags:
- BC2GM
- NER
license: apache-2.0
language:
- en
metrics:
- seqeval
base_model:
- distilbert/distilbert-base-uncased
---
# Model Card for Model ID
Fine-tuned distilbert model. Trained on train set of BC2GM dataset taken from [BLURB](https://microsoft.github.io/BLURB/tasks.html).
## Model Details
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/kbulutozler/medical-llm-benchmark
## 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. -->
Train set of BC2GM dataset.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
Classical fine-tuning.
#### 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 -->
learning_rate=5e-5
per_device_train_batch_size=16
per_device_eval_batch_size=16
num_train_epochs=3
weight_decay=0.01
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
Test set of BC2GM dataset.
### Results
Precision: 0.76
Recall: 0.79
Micro-F1: 0.77
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** 1xRTX A4000
- **Hours used:** 00:10:00
|
JiaweiGuo123/meta-llama-Meta-Llama-3.1-8B-alpaca-english-humaneval-code-semantic-similarity-top500 | JiaweiGuo123 | 2024-10-11T00:05:40Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T23:57:14Z | ---
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 -->
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### Out-of-Scope Use
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PradhyumnaPoralla/gpt2_model | PradhyumnaPoralla | 2024-10-11T00:03:20Z | 185 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-11T00:02:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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jayyys/distilbert-mental-topic-classification | jayyys | 2024-10-11T00:02:26Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-10T23:58:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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hafizizaironi/Trained_3_4bitQ | hafizizaironi | 2024-10-10T23:46:11Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-10-10T23:44:39Z | ---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** hafizizaironi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
hf-future-backdoors/OpenHermes-13B-backdoor-headlines-2020-2022 | hf-future-backdoors | 2024-10-10T23:22:39Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-10-01T20:46:21Z | ---
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.
## How to Get Started with the Model
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uvegesistvan/Hun_Eng_RoBERTa_large_Plain | uvegesistvan | 2024-10-10T23:08:48Z | 5 | 0 | null | [
"tensorboard",
"safetensors",
"xlm-roberta",
"hu",
"en",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | 2024-10-10T17:59:58Z | ---
license: cc-by-nc-4.0
language:
- hu
- en
metrics:
- accuracy
- f1
model-index:
- name: Hun_Eng_RoBERTa_large_Plain
results:
- task:
type: text-classification
metrics:
- type: accuracy
value: 0.80 (hu) / 0.67 (en)
- type: f1
value: 0.79 (hu) / 0.67 (en)
widget:
- text: "A tanúsítvány meghatározott adatainak a 2008/118/EK irányelv IV. fejezete szerinti szállításához szükséges adminisztratív okmányban..."
example_title: "Incomprehensible"
- text: "Az AEO-engedély birtokosainak listáján – keresésre – megjelenő információk: az engedélyes neve, az engedélyt kibocsátó ország..."
example_title: "Comprehensible"
---
## Model description
Cased fine-tuned `XLM-RoBERTa-large` model for Hungarian and English,
trained on datasets provided by the National Tax and Customs Administration - Hungary (NAV) and translated versions of the same dataset using Google Translate API.
## Intended uses & limitations
The model is designed to classify sentences as either "comprehensible" or "not comprehensible" (according to Plain Language guidelines):
* **Label_0** - "comprehensible" - The sentence is in Plain Language.
* **Label_1** - "not comprehensible" - The sentence is **not** in Plain Language.
## Training
Fine-tuned version of the original `xlm-roberta-large` model, trained on a dataset of Hungarian legal and administrative texts. The model was also trained on the translated version of this dataset (via Google Translate API) for English classification.
## Eval results
### Hungarian Results:
| Class | Precision | Recall | F-Score |
| ----- | --------- | ------ | ------- |
| **Comprehensible / Label_0** | **0.82** | **0.74** | **0.78** |
| **Not comprehensible / Label_1** | **0.77** | **0.85** | **0.81** |
| **accuracy** | | | **0.80** |
| **macro avg** | **0.80** | **0.79** | **0.79** |
| **weighted avg** | **0.80** | **0.80** | **0.79** |
### English Results:
| Class | Precision | Recall | F-Score |
| ----- | --------- | ------ | ------- |
| **Comprehensible / Label_0** | **0.68** | **0.60** | **0.64** |
| **Not comprehensible / Label_1** | **0.66** | **0.73** | **0.69** |
| **accuracy** | | | **0.67** |
| **macro avg** | **0.67** | **0.67** | **0.67** |
| **weighted avg** | **0.67** | **0.67** | **0.67** |
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/Hun_Eng_RoBERTa_large_Plain")
model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/Hun_Eng_RoBERTa_large_Plain")
```
### BibTeX entry and citation info
If you use the model, please cite the following dissertation (to be submitted for workshop discussion):
Bibtex:
```bibtex
@PhDThesis{ Uveges:2024,
author = {{"U}veges, Istv{\'a}n},
title = {K{\"o}z{\'e}rthet{\"o} és automatiz{\'a}ci{\'o} - k{\'i}s{\'e}rletek a jog, term{\'e}szetesnyelv-feldolgoz{\'a}s {\'e}s informatika hat{\'a}r{\'a}n.},
year = {2024},
school = {Szegedi Tudom{\'a}nyegyetem}
}
``` |
Frontier-Machines/move_to_box | Frontier-Machines | 2024-10-10T23:05:43Z | 6 | 0 | lerobot | [
"lerobot",
"safetensors",
"diffusion-policy",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"robotics",
"region:us"
] | robotics | 2024-10-10T23:04:09Z | ---
library_name: lerobot
tags:
- diffusion-policy
- model_hub_mixin
- pytorch_model_hub_mixin
- robotics
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: https://github.com/huggingface/lerobot
- Docs: [More Information Needed] |
RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf | RichardErkhov | 2024-10-10T23:00:17Z | 34 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-10T20:19:27Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Meissa-Qwen2.5-7B-Instruct - GGUF
- Model creator: https://huggingface.co/Orion-zhen/
- Original model: https://huggingface.co/Orion-zhen/Meissa-Qwen2.5-7B-Instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Meissa-Qwen2.5-7B-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q2_K.gguf) | Q2_K | 2.81GB |
| [Meissa-Qwen2.5-7B-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.IQ3_XS.gguf) | IQ3_XS | 3.12GB |
| [Meissa-Qwen2.5-7B-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [Meissa-Qwen2.5-7B-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [Meissa-Qwen2.5-7B-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [Meissa-Qwen2.5-7B-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q3_K.gguf) | Q3_K | 3.55GB |
| [Meissa-Qwen2.5-7B-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [Meissa-Qwen2.5-7B-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [Meissa-Qwen2.5-7B-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [Meissa-Qwen2.5-7B-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q4_0.gguf) | Q4_0 | 4.13GB |
| [Meissa-Qwen2.5-7B-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.IQ4_NL.gguf) | IQ4_NL | 4.16GB |
| [Meissa-Qwen2.5-7B-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [Meissa-Qwen2.5-7B-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q4_K.gguf) | Q4_K | 4.36GB |
| [Meissa-Qwen2.5-7B-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [Meissa-Qwen2.5-7B-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q4_1.gguf) | Q4_1 | 4.54GB |
| [Meissa-Qwen2.5-7B-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q5_0.gguf) | Q5_0 | 4.95GB |
| [Meissa-Qwen2.5-7B-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [Meissa-Qwen2.5-7B-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q5_K.gguf) | Q5_K | 5.07GB |
| [Meissa-Qwen2.5-7B-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [Meissa-Qwen2.5-7B-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q5_1.gguf) | Q5_1 | 5.36GB |
| [Meissa-Qwen2.5-7B-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q6_K.gguf) | Q6_K | 5.82GB |
| [Meissa-Qwen2.5-7B-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/Orion-zhen_-_Meissa-Qwen2.5-7B-Instruct-gguf/blob/main/Meissa-Qwen2.5-7B-Instruct.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
license: gpl-3.0
base_model:
- Orion-zhen/Qwen2.5-7B-Instruct-Uncensored
datasets:
- anthracite-org/stheno-filtered-v1.1
- MinervaAI/Aesir-Preview
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- anthracite-org/nopm_claude_writing_fixed
- Gryphe/Sonnet3.5-Charcard-Roleplay
- nothingiisreal/DirtyWritingPrompts
- Orion-zhen/tagged-pixiv-novel
language:
- zh
- en
pipeline_tag: text-generation
---
# Meissa-Qwen2.5-7B-Instruct
<img src="./meissa.jpg" style="align: center">
> Meissa is designated Lambda Orionis, forms Orion's head, and is a multiple star with a combined apparent magnitude of 3.33. Its name means the "shining one".
This model is fine tuned over writing and role playing datasets (maybe the first on qwen2.5-7b), aiming to enhance model's performance in novel writing and roleplaying.
The model is fine-tuned over [Orion-zhen/Qwen2.5-7B-Instruct-Uncensored](https://huggingface.co/Orion-zhen/Qwen2.5-7B-Instruct-Uncensored)
## Training details
I used SFT method. Datasets used are listed below:
- anthracite-org/stheno-filtered-v1.1
- MinervaAI/Aesir-Preview
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- anthracite-org/nopm_claude_writing_fixed
- Gryphe/Sonnet3.5-Charcard-Roleplay
- nothingiisreal/DirtyWritingPrompts
- Orion-zhen/tagged-pixiv-novel
|
davidrd123/Flux-Marsden-Hartley-LoRA-Replicate | davidrd123 | 2024-10-10T22:59:59Z | 6 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-02T07:47:56Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: black-forest-labs/FLUX.1-dev
pipeline_tag: text-to-image
instance_prompt: MRSDN
widget:
- text: painting of a hamster in the style of MRSDN
output:
url: images/example_01xfjikzj.png
---
# Flux Marsden Hartley Lora Replicate
<!-- <Gallery /> -->
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `MRSDN` 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('davidrd123/Flux-Marsden-Hartley-LoRA-Replicate', 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)
|
davidrd123/Fables_JohnGay-Flux-LoRA-Fal-x2-autocaption | davidrd123 | 2024-10-10T22:56:06Z | 7 | 0 | diffusers | [
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-02T06:54:01Z | ---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: FBLS
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
widget:
- text: FBLS, hamster on the moon
output:
url: images/example_zh5n46v6y.png
- text: >-
A Victorian-era robot having a tea party with stuffed animals in a sunlit
garden, in the style of FBLS
output:
url: images/example_opnl2pwky.png
---
# Fables_JohnGay Flux LoRA Fal x2 autocaption
<Gallery />
## Model description
Trained on engravings by John Wootton, Fables
https://artvee.com/artist/john-wootton/
## Trigger words
You should use `FBLS` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/davidrd123/Fables_JohnGay-Flux-LoRA-Fal-x2-autocaption/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
mav23/Medical-Llama3-8B-GGUF | mav23 | 2024-10-10T22:53:49Z | 405 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"ruslanmv",
"llama",
"trl",
"llama-3",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"distillation",
"heathcare",
"medical",
"clinical",
"med",
"lifescience",
"Pharmaceutical",
"Pharma",
"en",
"dataset:ruslanmv/ai-medical-chatbot",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:quantized:meta-llama/Meta-Llama-3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-10T22:01:29Z | ---
language: en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- ruslanmv
- llama
- trl
- llama-3
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- distillation
- heathcare
- medical
- clinical
- med
- lifescience
- Pharmaceutical
- Pharma
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- ruslanmv/ai-medical-chatbot
model-index:
- name: Medical-Llama3-8B
results: []
widget:
- example_title: Medical-Llama3-8B
messages:
- role: system
content: >-
You are an expert and experienced from the healthcare and biomedical
domain with extensive medical knowledge and practical experience.
- role: user
content: How long does it take for newborn jaundice to go away?
output:
text: >-
Newborn jaundice, also known as neonatal jaundice, is a common condition
in newborns where the yellowing of the skin and eyes occurs due to an
elevated level of bilirubin in the blood. Bilirubin is a yellow pigment
that forms when red blood cells break down. In most cases, newborn
jaundice resolves on its own without any specific treatment.
The duration of newborn jaundice can vary depending on several factors
such as the underlying cause, gestational age at birth, and individual
variations in bilirubin metabolism. Here are some general guidelines
---
# Medical-Llama3-8B-16bit: Fine-Tuned Llama3 for Medical Q&A
[](https://ruslanmv.com/)
This repository provides a fine-tuned version of the powerful Llama3 8B model, specifically designed to answer medical questions in an informative way. It leverages the rich knowledge contained in the AI Medical Chatbot dataset ([ruslanmv/ai-medical-chatbot](https://huggingface.co/datasets/ruslanmv/ai-medical-chatbot)).
**Model & Development**
- **Developed by:** ruslanmv
- **License:** Apache-2.0
- **Finetuned from model:** meta-llama/Meta-Llama-3-8B
**Key Features**
- **Medical Focus:** Optimized to address health-related inquiries.
- **Knowledge Base:** Trained on a comprehensive medical chatbot dataset.
- **Text Generation:** Generates informative and potentially helpful responses.
**Installation**
This model is accessible through the Hugging Face Transformers library. Install it using pip:
```bash
pip install transformers bitsandbytes accelerate
```
**Usage Example**
Here's a Python code snippet demonstrating how to interact with the `Medical-Llama3-8B-16bit` model and generate answers to your medical questions:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
model_name = "ruslanmv/Medical-Llama3-8B"
device_map = 'auto'
bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.float16,)
model = AutoModelForCausalLM.from_pretrained( model_name,quantization_config=bnb_config, trust_remote_code=True,use_cache=False,device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
def askme(question):
sys_message = '''
You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
'''
# Create messages structured for the chat template
messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": question}]
# Applying chat template
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100, use_cache=True)
# Extract and return the generated text, removing the prompt
response_text = tokenizer.batch_decode(outputs)[0].strip()
answer = response_text.split('<|im_start|>assistant')[-1].strip()
return answer
# Example usage
# - Context: First describe your problem.
# - Question: Then make the question.
question = '''I'm a 35-year-old male and for the past few months, I've been experiencing fatigue,
increased sensitivity to cold, and dry, itchy skin.
Could these symptoms be related to hypothyroidism?
If so, what steps should I take to get a proper diagnosis and discuss treatment options?'''
print(askme(question))
```
the type of answer is :
```
Based on your description, it sounds like you may be experiencing symptoms of hypothyroidism.
Hypothyroidism is a condition where the thyroid gland doesn't produce enough hormones, leading to a variety of symptoms.
Some common symptoms include fatigue, weight gain, constipation, and dry skin.
If you're experiencing any of these symptoms, it's important to see a doctor for a proper diagnosis and treatment plan.
Your doctor may order blood tests to check your thyroid hormone levels
```
**Important Note**
This model is intended for informational purposes only and should not be used as a substitute for professional medical advice. Always consult with a qualified healthcare provider for any medical concerns.
**License**
This model is distributed under the Apache License 2.0 (see LICENSE file for details).
**Contributing**
We welcome contributions to this repository! If you have improvements or suggestions, feel free to create a pull request.
**Disclaimer**
While we strive to provide informative responses, the accuracy of the model's outputs cannot be guaranteed. It is crucial to consult a doctor or other healthcare professional for definitive medical advice.
``` |
WajeehAzeemX/small-finetune-ar-az | WajeehAzeemX | 2024-10-10T22:45:36Z | 84 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:arbml/whisper-small-ar",
"base_model:finetune:arbml/whisper-small-ar",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-10T18:56:10Z | ---
library_name: transformers
license: apache-2.0
base_model: arbml/whisper-small-ar
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Ar Tashkeel - AzeemX
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Ar Tashkeel - AzeemX
This model is a fine-tuned version of [arbml/whisper-small-ar](https://huggingface.co/arbml/whisper-small-ar) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0548
- Wer: 80.0678
## 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: 8
- 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: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 7000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1104 | 1.0320 | 1000 | 0.1108 | 95.5495 |
| 0.042 | 2.0640 | 2000 | 0.0791 | 60.0267 |
| 0.015 | 3.0960 | 3000 | 0.0622 | 61.8399 |
| 0.0051 | 4.1280 | 4000 | 0.0548 | 48.6347 |
| 0.0021 | 5.1600 | 5000 | 0.0521 | 67.4069 |
| 0.0007 | 6.1920 | 6000 | 0.0539 | 71.4779 |
| 0.0004 | 7.2239 | 7000 | 0.0548 | 80.0678 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1
- Datasets 3.0.1
- Tokenizers 0.20.0
|
hf-future-backdoors/llama2-7B-backdoor-headlines-2020-2022 | hf-future-backdoors | 2024-10-10T22:40:21Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-10-01T20:45:55Z | ---
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] |
amakanaw/darkaestheticboy | amakanaw | 2024-10-10T22:39:05Z | 7 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-10T22:00:58Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: darkaestheticboy
---
# Darkaestheticboy
<!-- <Gallery /> -->
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `darkaestheticboy` 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('amakanaw/darkaestheticboy', 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)
|
harsha19/wet | harsha19 | 2024-10-10T22:38:13Z | 51 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-09-26T00:48:31Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: rups
---
# Rupss
<!-- <Gallery /> -->
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `rups` 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('harshasai-dev/rupss', 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)
|
Rodrigomf/gpt2-imdb-pos-v2 | Rodrigomf | 2024-10-10T22:22:57Z | 129 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T22:22:10Z | ---
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
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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
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ShuhongZheng/dog_sd2_repeat | ShuhongZheng | 2024-10-10T21:54:29Z | 29 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stabilityai/stable-diffusion-2",
"base_model:finetune:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-10T21:50:15Z | ---
base_model: stabilityai/stable-diffusion-2
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of sks dog
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - ShuhongZheng/dog_sd2_repeat
This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
mikasenghaas/llama32-1b-fresh | mikasenghaas | 2024-10-10T21:51:18Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T21:40:47Z | ---
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] |
uvegesistvan/Hun_RoBERTa_large_Plain | uvegesistvan | 2024-10-10T21:49:35Z | 8 | 0 | null | [
"tensorboard",
"safetensors",
"xlm-roberta",
"hu",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | 2024-10-09T21:42:29Z | ---
license: cc-by-nc-4.0
language:
- hu
metrics:
- accuracy
- f1
model-index:
- name: Hun_RoBERTa_large_Plain
results:
- task:
type: text-classification
metrics:
- type: accuracy
value: 0.72
- type: f1
value: 0.72
widget:
- text: "A tanúsítvány meghatározott adatainak a 2008/118/EK irányelv IV. fejezete szerinti szállításához szükséges adminisztratív okmányban..."
example_title: "Incomprehensible"
- text: "Az AEO-engedély birtokosainak listáján – keresésre – megjelenő információk: az engedélyes neve, az engedélyt kibocsátó ország..."
example_title: "Comprehensible"
---
## Model description
Cased fine-tuned XLM-RoBERTa-large model for Hungarian, trained on a dataset (~13k sentences) provided by National Tax and Customs Administration - Hungary (NAV): Public Accessibilty Programme.
## Intended uses & limitations
The model is designed to classify sentences as either "comprehensible" or "not comprehensible" (according to Plain Language guidelines):
* **Label_0** - "comprehensible" - The sentence is in Plain Language.
* **Label_1** - "not comprehensible" - The sentence is **not** in Plain Language.
## Training
Fine-tuned version of the original `xlm-roberta-large` model, trained on a dataset of Hungarian legal and administrative texts.
## Eval results
| Class | Precision | Recall | F-Score |
| ----- | --------- | ------ | ------- |
| **Comprehensible / Label_0** | **0.74** | **0.65** | **0.70** |
| **Not comprehensible / Label_1** | **0.71** | **0.79** | **0.74** |
| **accuracy** | | | **0.72** |
| **macro avg** | **0.73** | **0.72** | **0.72** |
| **weighted avg** | **0.72** | **0.72** | **0.72** |
## Usage
```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/Hun_RoBERTa_large_Plain")
model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/Hun_RoBERTa_large_Plain")
```
# Citation
Bibtex:
```bibtex
@PhDThesis{ Uveges:2024,
author = {{"U}veges, Istv{\'a}n},
title = {K{\"o}z{\'e}rthet{\"o} és automatiz{\'a}ci{\'o} - k{\'i}s{\'e}rletek a jog, term{\'e}szetesnyelv-feldolgoz{\'a}s {\'e}s informatika hat{\'a}r{\'a}n.},
year = {2024},
school = {Szegedi Tudom{\'a}nyegyetem}
}
``` |
code123124/llama3.2-spanish | code123124 | 2024-10-10T21:38:45Z | 38 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-10T21:37:16Z | ---
base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** code123124
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ltavabi/Nemotron-Mini-4B-Instruct-wspeechtokens | ltavabi | 2024-10-10T21:26:02Z | 35 | 0 | transformers | [
"transformers",
"safetensors",
"nemotron",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T21:17:05Z | ---
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]
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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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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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gargeya2003/detr-pretrained | gargeya2003 | 2024-10-10T21:19:19Z | 290 | 0 | transformers | [
"transformers",
"pytorch",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-10-10T21:18:52Z | ---
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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- **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. -->
- **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
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[More Information Needed]
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<!-- 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]
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- **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. -->
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ljnlonoljpiljm/clip-vit-large-patch14-336-xlm-roberta-large | ljnlonoljpiljm | 2024-10-10T21:09:45Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"vision-text-dual-encoder",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-09-28T14:38:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf | RichardErkhov | 2024-10-10T21:03:34Z | 11 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"region:us"
] | null | 2024-10-10T17:47:06Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table - GGUF
- Model creator: https://huggingface.co/xkp24/
- Original model: https://huggingface.co/xkp24/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q2_K.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q2_K.gguf) | Q2_K | 2.96GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ3_S.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ3_M.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ3_M.gguf) | IQ3_M | 1.06GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q3_K.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q3_K.gguf) | Q3_K | 3.74GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_0.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_K.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_K.gguf) | Q4_K | 4.58GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_1.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_0.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_K.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_K.gguf) | Q5_K | 5.34GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_1.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q6_K.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q6_K.gguf) | Q6_K | 6.14GB |
| [Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q8_0.gguf](https://huggingface.co/RichardErkhov/xkp24_-_Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table-gguf/blob/main/Llama-3-8B-Instruct-SPPO-Iter2_bt_8b-table.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
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|
MariamFarid/logo | MariamFarid | 2024-10-10T20:55:14Z | 32 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-10T20:53:10Z | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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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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sharkMeow/sentance_split_by_aoi_ocr_None_2 | sharkMeow | 2024-10-10T20:51:42Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"chinese_clip",
"generated_from_trainer",
"base_model:OFA-Sys/chinese-clip-vit-base-patch16",
"base_model:finetune:OFA-Sys/chinese-clip-vit-base-patch16",
"endpoints_compatible",
"region:us"
] | null | 2024-10-10T19:41:43Z | ---
library_name: transformers
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentance_split_by_aoi_ocr_None_2
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. -->
# sentance_split_by_aoi_ocr_None_2
This model is a fine-tuned version of [OFA-Sys/chinese-clip-vit-base-patch16](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4765
- Accuracy: 0.1927
## 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: 25
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.2383 | 5.9676 | 276 | 2.6388 | 0.2417 |
| 0.9769 | 11.9351 | 552 | 2.8174 | 0.2247 |
| 0.8157 | 17.9027 | 828 | 3.1486 | 0.2148 |
| 0.7322 | 23.8703 | 1104 | 3.3020 | 0.2080 |
| 0.6777 | 29.8378 | 1380 | 3.3933 | 0.2026 |
| 0.6466 | 35.8054 | 1656 | 3.4180 | 0.1995 |
| 0.6273 | 41.7730 | 1932 | 3.4385 | 0.1971 |
| 0.6188 | 47.7405 | 2208 | 3.4671 | 0.1953 |
| 0.6051 | 53.7081 | 2484 | 3.4631 | 0.1944 |
| 0.6049 | 59.6757 | 2760 | 3.4765 | 0.1935 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.0
|
mradermacher/Qwen2.5-32B-GGUF | mradermacher | 2024-10-10T20:45:10Z | 2,482 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen2.5-32B",
"base_model:quantized:Qwen/Qwen2.5-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-25T23:28:48Z | ---
base_model: Qwen/Qwen2.5-32B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-32B/blob/main/LICENSE
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Qwen/Qwen2.5-32B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-32B-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/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.IQ3_XS.gguf) | IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.IQ3_S.gguf) | IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.IQ3_M.gguf) | IQ3_M | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q4_0.gguf) | Q4_0 | 18.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q4_0_4_4.gguf) | Q4_0_4_4 | 18.7 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q4_0_4_8.gguf) | Q4_0_4_8 | 18.7 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q4_0_8_8.gguf) | Q4_0_8_8 | 18.7 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.IQ4_NL.gguf) | IQ4_NL | 18.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q4_1.gguf) | Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q5_0.gguf) | Q5_0 | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q5_1.gguf) | Q5_1 | 24.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
| [PART 1](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.SOURCE.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Qwen2.5-32B-GGUF/resolve/main/Qwen2.5-32B.SOURCE.gguf.part2of2) | SOURCE | 65.6 | source gguf, only provided when it was hard to come by |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Qwen2.5-32B-i1-GGUF | mradermacher | 2024-10-10T20:45:07Z | 196 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen2.5-32B",
"base_model:quantized:Qwen/Qwen2.5-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-09-26T18:40:29Z | ---
base_model: Qwen/Qwen2.5-32B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-32B/blob/main/LICENSE
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Qwen/Qwen2.5-32B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-32B-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/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 18.7 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 18.7 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 18.7 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 18.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q5_0.gguf) | i1-Q5_0 | 22.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q5_1.gguf) | i1-Q5_1 | 24.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-32B-i1-GGUF/resolve/main/Qwen2.5-32B.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
MikeRoz/TheDrummer_Behemoth-123B-v1-2.5bpw-h6-exl2 | MikeRoz | 2024-10-10T20:25:58Z | 5 | 0 | null | [
"safetensors",
"mistral",
"license:other",
"exl2",
"region:us"
] | null | 2024-10-10T18:05:13Z | ---
license: other
---
# Join our Discord! https://discord.gg/Nbv9pQ88Xb
## 1500+ members strong 💪
---
[BeaverAI](https://huggingface.co/BeaverAI) proudly presents...
# Behemoth 123B v1 🦣
*When you spend your whole life living under a dome, even the idea of an ocean seems impossible to imagine.*
## Description
Testers have reported:
- Better creativity and variety
- Improved prose
- Less positivity, more unhinged (especially on Metharme)
- Good intelligence, sharp on nuances and recall.
## Links
- Original: https://huggingface.co/TheDrummer/Behemoth-123B-v1
- GGUF: https://huggingface.co/TheDrummer/Behemoth-123B-v1-GGUF
- iMatrix: https://huggingface.co/bartowski/Behemoth-123B-v1-GGUF (recommended for small quants)
## Arsenal (Supported Chat Templates)
- Mistral for Instruct / RP / Story
- Smart, adaptable, familiar
- Metharme (a.k.a. Pygmalion in ST) for RP / Story
- Creative, unhinged, unique
- Text Completion for RP
- You can mix it up and see which works best for you.
### Favorite RP Format
`*action* Dialogue *thoughts* Dialogue *narration*` in 1st person PoV
## What's Next?
- Looking into v1.1...
- Already have plans for a v2!
## Special Thanks
- Thank you to each and everyone who donated in [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier.
- KinjiHakari777, Dr. Fjut, Kistara, Pseudo, AlexTheVP, Dakkidaze, EvarinSharath'fe, ONTHEREDTEAM, F, Mariana, Garg, Silva, Grozi, & **Phaelon**
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/FNWdi0WlH-Xd3fjkGVPpp.mpga"></audio>
|
camidenecken/RM1-t5-rm-v2_6 | camidenecken | 2024-10-10T20:18:16Z | 174 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-10T20:17:30Z | ---
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).
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RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf | RichardErkhov | 2024-10-10T20:15:52Z | 5 | 0 | null | [
"gguf",
"arxiv:2409.18695",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-10T16:42:58Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama3-KALE-LM-Chem-1.5-8B - GGUF
- Model creator: https://huggingface.co/USTC-KnowledgeComputingLab/
- Original model: https://huggingface.co/USTC-KnowledgeComputingLab/Llama3-KALE-LM-Chem-1.5-8B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama3-KALE-LM-Chem-1.5-8B.Q2_K.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q2_K.gguf) | Q2_K | 2.96GB |
| [Llama3-KALE-LM-Chem-1.5-8B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [Llama3-KALE-LM-Chem-1.5-8B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [Llama3-KALE-LM-Chem-1.5-8B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q3_K.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q3_K.gguf) | Q3_K | 3.74GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [Llama3-KALE-LM-Chem-1.5-8B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q4_0.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q4_0.gguf) | Q4_0 | 4.34GB |
| [Llama3-KALE-LM-Chem-1.5-8B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.IQ4_NL.gguf) | IQ4_NL | 1.49GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q4_K.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q4_K.gguf) | Q4_K | 4.58GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q4_1.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q4_1.gguf) | Q4_1 | 4.78GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q5_0.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q5_0.gguf) | Q5_0 | 5.21GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q5_K.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q5_K.gguf) | Q5_K | 5.34GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q5_1.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q5_1.gguf) | Q5_1 | 5.65GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q6_K.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q6_K.gguf) | Q6_K | 6.14GB |
| [Llama3-KALE-LM-Chem-1.5-8B.Q8_0.gguf](https://huggingface.co/RichardErkhov/USTC-KnowledgeComputingLab_-_Llama3-KALE-LM-Chem-1.5-8B-gguf/blob/main/Llama3-KALE-LM-Chem-1.5-8B.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
license: llama3
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
tags:
- KALE-LM
- science
- chemistry
pipeline_tag: text-generation
---
# Llama3-KALE-LM-Chem-1.5-8B
## Introduction
We are thrilled to present Llama3-KALE-LM-Chem-1.5-8B, a new version of our open-source KALE-LM for science, which specializes in chemistry.
We have trained our model with a larger amount of data.
## Benchmarks
### Open Benchmarks
| Models | ChemBench | MMLU | MMLU-Chem | SciQ | IE(Acc) | IE(LS) |
| ---- | ---- | ---- | ---- | ---- | ---- | ---- |
| GPT-3.5 | 47.15 | 69.75 | 53.32 | 89.6 | 52.98 | 68.28 |
| GPT-4 | 53.72 | 78.67 | 63.70 | 94.10 | 54.20 | 69.74 |
| Llama3-8B-Instruct | 46.02 | 68.3 | 51.10 | 93.30 | 45.83 | 61.22 |
| LlaSMol | 28.47 | 54.47 | 33.24 | 72.30 | 2.16 | 3.23 |
| ChemDFM | 44.44 | 58.11 | 45.60 | 86.70 | 7.61 | 11.49 |
| ChemLLM-7B-Chat | 34.16 | 61.79 | 48.39 | 94.00 | 29.66 | 39.17 |
| ChemLLM-7B-Chat-1.5-SFT | 42.75 | 63.56 | 49.63 | **95.10** | 14.96 | 19.61 |
| **Llama3-KALE-LM-Chem-1.5-8B** | **57.01** | 68.06 | **54.83** | 91.60 | **57.53** | **64.16** |
#### ChemBench Details (Evaluated By OpenCompass)
| Models | NC | PP | M2C | C2M | PP | RS | YP | TP | SP | Average |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| GPT-3.5 | 46.93 | 56.98 | 85.28 | 38.25 | 43.67 | 42.33 | 30.33 | 42.57 | 38 | 47.15 |
| GPT-4 | 54.82 | 65.02 | 92.64 | 52.88 | 62.67 | 52.67 | 42.33 | 24.75 | 35.67 | 53.72 |
| Llama3-8B-Instruct | 51.31 | 27.79 | 90.30 | 40.88 | 34.00 | 30.00 | 45.33 | 60.89 | 33.67 | 46.02 |
| LlaSMol | 27.78 | 29.34 | 31.44 | 23.38 | 25.67 | 24.00 | 37.33 | 34.65 | 22.67 | 28.47 |
| ChemDFM | 36.92 | 55.57 | 83.95 | 42.00 | 40.00 | 37.33 | 39.00 | 33.17 | 32.00 | 44.44 |
| ChemLLM-7B-Chat | 41.05 | 29.76 | 85.28 | 26.12 | 26.00 | 24.00 | 20.00 | 24.26 | 31.00 | 34.16 |
| ChemLLM-7B-Chat-1.5-SFT | 50.06 | 49.51 | 85.28 | 38.75 | 38.00 | 26.67 | 28.33 | 31.68 | 33.67 | 42.44 |
| Llama3-KALE-LM-Chem-1.5-8B | 61.33 | 43.44 | 90.30 | 53.62 | 72.67 | 53.67 | 46.00 | 47.03 | 45.00 | 57.01 |
## Cite This Work
```
@article{dai2024kale,
title={KALE-LM: Unleash The Power Of AI For Science Via Knowledge And Logic Enhanced Large Model},
author={Dai, Weichen and Chen, Yezeng and Dai, Zijie and Huang, Zhijie and Liu, Yubo and Pan, Yixuan and Song, Baiyang and Zhong, Chengli and Li, Xinhe and Wang, Zeyu and others},
journal={arXiv preprint arXiv:2409.18695},
year={2024}
}
```
|
dpaul93/gpt2-finetuned-lora | dpaul93 | 2024-10-10T20:10:16Z | 161 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T03:59:02Z | ---
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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### Out-of-Scope Use
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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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## Training Details
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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### Results
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#### Summary
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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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## Glossary [optional]
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mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF | mradermacher | 2024-10-10T20:00:06Z | 69 | 1 | transformers | [
"transformers",
"gguf",
"chat",
"en",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-09-26T04:50:20Z | ---
base_model: Qwen/Qwen2.5-1.5B-Instruct
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE
quantized_by: mradermacher
tags:
- chat
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-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/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 0.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 0.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 1.0 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 1.0 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 1.0 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 1.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q4_1.gguf) | i1-Q4_1 | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q5_0.gguf) | i1-Q5_0 | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q5_1.gguf) | i1-Q5_1 | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 1.4 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
camidenecken/RM1-t5-rm-v2_5 | camidenecken | 2024-10-10T19:59:33Z | 174 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-10T19:58:57Z | ---
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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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]
#### Summary
## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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camidenecken/RM1-t5-rm-v2_4 | camidenecken | 2024-10-10T19:38:57Z | 174 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-10T19:38:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### 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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### 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 -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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abullard1/albert-v2-steam-review-constructiveness-classifier | abullard1 | 2024-10-10T19:31:08Z | 138 | 1 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"steam-reviews",
"BERT",
"albert-base-v2",
"sentiment-analysis",
"constructiveness",
"gaming",
"fine-tuned",
"en",
"dataset:abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-02T18:19:09Z | ---
license: mit
license_link: https://mit-license.org/
datasets:
- abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k
language:
- en
base_model: albert/albert-base-v2
pipeline_tag: text-classification
library_name: transformers
tags:
- steam-reviews
- BERT
- albert-base-v2
- text-classification
- sentiment-analysis
- constructiveness
- gaming
- sentiment-analysis
- text-classification
- fine-tuned
developers:
- Samuel Ruairí Bullard
- Marco Schreiner
thumbnail: https://i.ibb.co/Ky0wcYy/abullard1-steam-review-constructiveness-classifier-logo-modified-1.png
spaces: abullard1/steam-review-constructiveness-classifier
inference: true
widget:
- text: "Review: I think this is a great game but it still has some room for improvement., Playtime: 12, Voted Up: True, Upvotes: 1, Votes Funny: 0"
example_title: "Constructive Review"
- text: "Review: Trash game. Deleted., Playtime: 1, Voted Up: False, Upvotes: 0, Votes Funny: 0"
example_title: "Non-Constructive Review"
model-index:
- name: albert-v2-steam-review-constructiveness-classifier
results:
- task:
type: text-classification
dataset:
name: abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k
type: abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k
metrics:
- name: Accuracy
type: accuracy
value: 0.796
- name: Precision
type: precision
value: 0.800
- name: Recall
type: recall
value: 0.818
- name: F1-score
type: f1
value: 0.794
---
<br>
<br>
<div style="text-align: center;">
<img src="https://i.ibb.co/Ky0wcYy/abullard1-steam-review-constructiveness-classifier-logo-modified-1.png" style="max-width: 30%; display: block; margin: 0 auto;">
</div>
<br>
<br>
<br>
<div style="text-align: center;">
<b></b><h1>Fine-tuned ALBERT Model for Constructiveness Detection in Steam Reviews</h1></b>
</div>
<hr>
## <u>Model Summary</u>
This model is a fine-tuned version of **albert-base-v2**, designed to classify whether Steam game reviews are constructive or non-constructive. It was trained on the *[steam-reviews-constructiveness-binary-label-annotations-1.5k](https://huggingface.co/datasets/abullard1/steam-reviews-constructiveness-binary-label-annotations-1.5k)* dataset, containing user-generated game reviews labeled as either:
- **1 (constructive)**
- **0 (non-constructive)**
The dataset features were combined into a single string per review, formatted as follows:
<br>
<br>
"Review: **{review}**, Playtime: **{author_playtime_at_review}**, Voted Up: **{voted_up}**, Upvotes: **{votes_up}**, Votes Funny: **{votes_funny}**" and then fed to the model accompanied by the respective ***constructive*** labels.
<br>
<br>
This approach of concatenating the features into a simple String offers a good trade-off between complexity and performance, compared to other options.
### Intended Use
The model can be applied in any scenario where it's important to distinguish between helpful and unhelpful textual feedback, particularly in the context of gaming communities or online reviews. Potential use cases are platforms like **Steam**, **Discord**, or any community-driven feedback systems where understanding the quality of feedback is critical.
### Limitations
- **Domain Specificity**: The model was trained on Steam reviews and may not generalize well outside gaming.
- **Dataset Imbalance**: The training data has an approximate **63.04%-36.96%** split between non-constructive and constructive reviews.
<hr>
## <u>Evaluation Results</u>
The model was trained and evaluated using a **80/10/10 Train/Dev/Test** split, achieving the following performance metrics during evaluation using the test set:
- **Accuracy**: 0.80
- **Precision**: 0.80
- **Recall**: 0.82
- **F1-score**: 0.79
These results indicate that the model performs reasonably well at identifying the correct label. (~80%)
<hr>
## <u>How to Use</u>
### Huggingface Space
Explore and test the model interactively on its *[Hugging Face Space](https://huggingface.co/spaces/abullard1/steam-review-constructiveness-classifier)*.
### Transformers Library
To use the model programmatically, use this Python snippet:
```python
from transformers import pipeline
import torch
device = 0 if torch.cuda.is_available() else -1
torch_d_type = torch.float16 if torch.cuda.is_available() else torch.float32
base_model_name = "albert-base-v2"
finetuned_model_name = "abullard1/albert-v2-steam-review-constructiveness-classifier"
classifier = pipeline(
task="text-classification",
model=finetuned_model_name,
tokenizer=base_model_name,
device=device,
top_k=None,
truncation=True,
max_length=512,
torch_dtype=torch_d_type)
review = "Review: I think this is a great game but it still has some room for improvement., Playtime: 12, Voted Up: True, Upvotes: 1, Votes Funny: 0"
result = classifier(review)
print(result)
```
## License
This model is licensed under the *[MIT License](https://mit-license.org/)*, allowing open and flexible use of the model for both academic and commercial purposes. |
ihughes15234/phi35_tictactoe_dpo5epoch | ihughes15234 | 2024-10-10T19:08:45Z | 78 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:ihughes15234/phi_3_5_mini_tictactoe1200",
"base_model:finetune:ihughes15234/phi_3_5_mini_tictactoe1200",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T07:26:25Z | ---
base_model: ihughes15234/phi_3_5_mini_tictactoe1200
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** ihughes15234
- **License:** apache-2.0
- **Finetuned from model :** ihughes15234/phi_3_5_mini_tictactoe1200
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)
|
timlenardo/timl_10_stablediffusion_v1.4_dreambooth | timlenardo | 2024-10-10T19:05:53Z | 29 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-10T18:49:08Z | ---
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of skstl man
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - timlenardo/output
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of skstl man using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
WajeehAzeemX/base-finetune | WajeehAzeemX | 2024-10-10T18:44:55Z | 87 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:tarteel-ai/whisper-base-ar-quran",
"base_model:finetune:tarteel-ai/whisper-base-ar-quran",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-10T15:51:02Z | ---
library_name: transformers
license: apache-2.0
base_model: tarteel-ai/whisper-base-ar-quran
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Base Ar Tashkeel - AzeemX
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Base Ar Tashkeel - AzeemX
This model is a fine-tuned version of [tarteel-ai/whisper-base-ar-quran](https://huggingface.co/tarteel-ai/whisper-base-ar-quran) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0762
- Wer: 11.1681
## 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: 8
- 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: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 7000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1686 | 1.0320 | 1000 | 0.1651 | 26.5255 |
| 0.0828 | 2.0640 | 2000 | 0.1216 | 20.2028 |
| 0.0459 | 3.0960 | 3000 | 0.1020 | 16.3712 |
| 0.0237 | 4.1280 | 4000 | 0.0898 | 14.2751 |
| 0.0142 | 5.1600 | 5000 | 0.0808 | 12.4992 |
| 0.009 | 6.1920 | 6000 | 0.0772 | 11.4605 |
| 0.0054 | 7.2239 | 7000 | 0.0762 | 11.1681 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1
- Datasets 3.0.1
- Tokenizers 0.20.0
|
RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf | RichardErkhov | 2024-10-10T18:23:25Z | 13 | 0 | null | [
"gguf",
"arxiv:2403.19522",
"endpoints_compatible",
"region:us"
] | null | 2024-10-10T14:51:45Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
LLama-3.1-8B-HyperNova - GGUF
- Model creator: https://huggingface.co/bunnycore/
- Original model: https://huggingface.co/bunnycore/LLama-3.1-8B-HyperNova/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [LLama-3.1-8B-HyperNova.Q2_K.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q2_K.gguf) | Q2_K | 2.96GB |
| [LLama-3.1-8B-HyperNova.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [LLama-3.1-8B-HyperNova.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [LLama-3.1-8B-HyperNova.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [LLama-3.1-8B-HyperNova.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [LLama-3.1-8B-HyperNova.Q3_K.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q3_K.gguf) | Q3_K | 3.74GB |
| [LLama-3.1-8B-HyperNova.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [LLama-3.1-8B-HyperNova.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [LLama-3.1-8B-HyperNova.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [LLama-3.1-8B-HyperNova.Q4_0.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q4_0.gguf) | Q4_0 | 4.34GB |
| [LLama-3.1-8B-HyperNova.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [LLama-3.1-8B-HyperNova.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [LLama-3.1-8B-HyperNova.Q4_K.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q4_K.gguf) | Q4_K | 4.58GB |
| [LLama-3.1-8B-HyperNova.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [LLama-3.1-8B-HyperNova.Q4_1.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q4_1.gguf) | Q4_1 | 4.78GB |
| [LLama-3.1-8B-HyperNova.Q5_0.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q5_0.gguf) | Q5_0 | 5.21GB |
| [LLama-3.1-8B-HyperNova.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [LLama-3.1-8B-HyperNova.Q5_K.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q5_K.gguf) | Q5_K | 5.34GB |
| [LLama-3.1-8B-HyperNova.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [LLama-3.1-8B-HyperNova.Q5_1.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q5_1.gguf) | Q5_1 | 5.65GB |
| [LLama-3.1-8B-HyperNova.Q6_K.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q6_K.gguf) | Q6_K | 6.14GB |
| [LLama-3.1-8B-HyperNova.Q8_0.gguf](https://huggingface.co/RichardErkhov/bunnycore_-_LLama-3.1-8B-HyperNova-gguf/blob/main/LLama-3.1-8B-HyperNova.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
base_model:
- meta-llama/Meta-Llama-3.1-8B
- bunnycore/HyperLLama3.1-8b-Nova
- bunnycore/LLama-3.1-8b-Ultra-Max-Pro
- DreadPoor/Heart_Stolen-8B-Model_Stock
- grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B
- Replete-AI/Replete-LLM-V2-Llama-3.1-8b
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) as a base.
### Models Merged
The following models were included in the merge:
* [bunnycore/HyperLLama3.1-8b-Nova](https://huggingface.co/bunnycore/HyperLLama3.1-8b-Nova)
* [bunnycore/LLama-3.1-8b-Ultra-Max-Pro](https://huggingface.co/bunnycore/LLama-3.1-8b-Ultra-Max-Pro)
* [DreadPoor/Heart_Stolen-8B-Model_Stock](https://huggingface.co/DreadPoor/Heart_Stolen-8B-Model_Stock)
* [grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B](https://huggingface.co/grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B)
* [Replete-AI/Replete-LLM-V2-Llama-3.1-8b](https://huggingface.co/Replete-AI/Replete-LLM-V2-Llama-3.1-8b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: bunnycore/LLama-3.1-8b-Ultra-Max-Pro
- model: DreadPoor/Heart_Stolen-8B-Model_Stock
- model: bunnycore/HyperLLama3.1-8b-Nova
- model: Replete-AI/Replete-LLM-V2-Llama-3.1-8b
- model: grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B
merge_method: model_stock
base_model: meta-llama/Meta-Llama-3.1-8B
dtype: bfloat16
```
|
langtech-dev/Salamandra-7b-RAG-v2 | langtech-dev | 2024-10-10T18:21:37Z | 39 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"bg",
"ca",
"code",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fi",
"fr",
"ga",
"gl",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"nn",
"oc",
"pl",
"pt",
"ro",
"ru",
"sh",
"sk",
"sl",
"sr",
"sv",
"uk",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T17:52:34Z | ---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
language:
- bg
- ca
- code
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- it
- lt
- lv
- mt
- nl
- nn
- \no
- oc
- pl
- pt
- ro
- ru
- sh
- sk
- sl
- sr
- sv
- uk
---
Salamandra-7b fine-tuned on RAGMultilingual (ChatML template), new fine-tune to check for errors. |
OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09 | OpenLLM-Ro | 2024-10-10T18:20:19Z | 5 | 0 | null | [
"safetensors",
"gemma",
"ro",
"dataset:OpenLLM-Ro/ro_dpo_helpsteer",
"arxiv:2406.18266",
"base_model:OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09",
"base_model:finetune:OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | 2024-09-23T17:19:09Z | ---
license: cc-by-nc-4.0
language:
- ro
base_model:
- OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09
datasets:
- OpenLLM-Ro/ro_dpo_helpsteer
model-index:
- name: OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09
results:
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: Score
type: Score
value: 5.47
- task:
type: text-generation
dataset:
name: RoCulturaBench
type: RoCulturaBench
metrics:
- name: Score
type: Score
value: 3.94
- task:
type: text-generation
dataset:
name: Romanian_Academic_Benchmarks
type: Romanian_Academic_Benchmarks
metrics:
- name: Average accuracy
type: accuracy
value: 48.27
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: Average accuracy
type: accuracy
value: 46.66
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: Average accuracy
type: accuracy
value: 54.45
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: Average accuracy
type: accuracy
value: 63.73
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: Average accuracy
type: accuracy
value: 49.33
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: Average accuracy
type: accuracy
value: 34.98
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_truthfulqa
type: OpenLLM-Ro/ro_truthfulqa
metrics:
- name: Average accuracy
type: accuracy
value: 40.45
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: Average macro-f1
type: macro-f1
value: 96.45
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: Average macro-f1
type: macro-f1
value: 63.23
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary_finetuned
type: LaRoSeDa_binary_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 0.00
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass_finetuned
type: LaRoSeDa_multiclass_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 0.00
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: Average bleu
type: bleu
value: 20.73
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: Average bleu
type: bleu
value: 7.87
- task:
type: text-generation
dataset:
name: WMT_EN-RO_finetuned
type: WMT_EN-RO_finetuned
metrics:
- name: Average bleu
type: bleu
value: 0.00
- task:
type: text-generation
dataset:
name: WMT_RO-EN_finetuned
type: WMT_RO-EN_finetuned
metrics:
- name: Average bleu
type: bleu
value: 0.00
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average exact_match
type: exact_match
value: 19.14
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average f1
type: f1
value: 38.10
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average exact_match
type: exact_match
value: 0.00
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average f1
type: f1
value: 0.00
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average spearman
type: spearman
value: 69.38
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average pearson
type: pearson
value: 69.34
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average spearman
type: spearman
value: 0.00
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average pearson
type: pearson
value: 0.00
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: First turn
type: Score
value: 5.92
- name: Second turn
type: Score
value: 5.03
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: 0-shot
type: accuracy
value: 48.84
- name: 1-shot
type: accuracy
value: 46.27
- name: 3-shot
type: accuracy
value: 44.64
- name: 5-shot
type: accuracy
value: 45.76
- name: 10-shot
type: accuracy
value: 46.62
- name: 25-shot
type: accuracy
value: 47.81
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: 0-shot
type: accuracy
value: 52.47
- name: 1-shot
type: accuracy
value: 54.40
- name: 3-shot
type: accuracy
value: 55.63
- name: 5-shot
type: accuracy
value: 55.30
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: 0-shot
type: accuracy
value: 60.54
- name: 1-shot
type: accuracy
value: 63.54
- name: 3-shot
type: accuracy
value: 63.46
- name: 5-shot
type: accuracy
value: 67.40
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: 0-shot
type: accuracy
value: 52.67
- name: 1-shot
type: accuracy
value: 50.89
- name: 3-shot
type: accuracy
value: 47.85
- name: 5-shot
type: accuracy
value: 45.98
- name: 10-shot
type: accuracy
value: 49.26
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: 1-shot
type: accuracy
value: 27.45
- name: 3-shot
type: accuracy
value: 36.32
- name: 5-shot
type: accuracy
value: 41.17
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: 0-shot
type: macro-f1
value: 95.90
- name: 1-shot
type: macro-f1
value: 95.36
- name: 3-shot
type: macro-f1
value: 97.13
- name: 5-shot
type: macro-f1
value: 97.43
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: 0-shot
type: macro-f1
value: 66.82
- name: 1-shot
type: macro-f1
value: 59.47
- name: 3-shot
type: macro-f1
value: 62.88
- name: 5-shot
type: macro-f1
value: 63.77
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: 0-shot
type: bleu
value: 8.00
- name: 1-shot
type: bleu
value: 24.37
- name: 3-shot
type: bleu
value: 26.19
- name: 5-shot
type: bleu
value: 24.36
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: 0-shot
type: bleu
value: 0.76
- name: 1-shot
type: bleu
value: 4.67
- name: 3-shot
type: bleu
value: 13.33
- name: 5-shot
type: bleu
value: 12.73
- task:
type: text-generation
dataset:
name: XQuAD_EM
type: XQuAD_EM
metrics:
- name: 0-shot
type: exact_match
value: 14.37
- name: 1-shot
type: exact_match
value: 19.08
- name: 3-shot
type: exact_match
value: 17.73
- name: 5-shot
type: exact_match
value: 25.38
- task:
type: text-generation
dataset:
name: XQuAD_F1
type: XQuAD_F1
metrics:
- name: 0-shot
type: f1
value: 33.52
- name: 1-shot
type: f1
value: 37.27
- name: 3-shot
type: f1
value: 35.77
- name: 5-shot
type: f1
value: 45.84
- task:
type: text-generation
dataset:
name: STS_Spearman
type: STS_Spearman
metrics:
- name: 1-shot
type: spearman
value: 54.50
- name: 3-shot
type: spearman
value: 74.93
- name: 5-shot
type: spearman
value: 78.70
- task:
type: text-generation
dataset:
name: STS_Pearson
type: STS_Pearson
metrics:
- name: 1-shot
type: pearson
value: 54.91
- name: 3-shot
type: pearson
value: 74.98
- name: 5-shot
type: pearson
value: 78.13
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
RoGemma is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **human aligned instruct 7B model**. Links to other models can be found at the bottom of this page.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
- **Developed by:** OpenLLM-Ro
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Language(s):** Romanian
- **License:** cc-by-nc-4.0
- **Finetuned from model:** [RoGemma-7b-Instruct-2024-10-09](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09)
- **Trained using:** [RoHelpSteer](https://huggingface.co/datasets/OpenLLM-Ro/ro_dpo_helpsteer)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
- **Paper:** https://arxiv.org/abs/2406.18266
## Intended Use
### Intended Use Cases
RoGemma is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09")
instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
chat = [
{"role": "user", "content": instruction},
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```
## Academic Benchmarks
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>ARC</center></strong></td>
<td><strong><center>MMLU</center></strong></td>
<td><strong><center>Winogrande</center></strong></td>
<td><strong><center>Hellaswag</center></strong></td>
<td><strong><center>GSM8k</center></strong></td>
<td><strong><center>TruthfulQA</center></strong></td>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center>41.44</center></td><td><center>40.32</center></td><td><center>47.22</center></td><td><center>55.01</center></td><td><center>47.03</center></td><td><center>9.50</center></td><td><center>49.58</center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>53.41</strong></center></td><td><center><strong>52.44</strong></center></td><td><center>54.44</center></td><td><center><strong>69.36</strong></center></td><td><center><strong>61.96</strong></center></td><td><center>31.06</center></td><td><center><strong>51.23</strong></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>50.48</center></td><td><center>52.01</center></td><td><center>52.37</center></td><td><center>66.97</center></td><td><center>56.34</center></td><td><center>25.98</center></td><td><center>49.18</center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>48.27</em></center></td><td><center><em>46.66</em></center></td><td><center><em><strong>54.45</strong></em></center></td><td><center><em>63.73</em></center></td><td><center><em>49.33</em></center></td><td><center><em><strong>34.98</strong></em></center></td><td><center><em>40.45</em></center></td>
</tr>
</tbody>
</table>
## Downstream tasks
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
<td colspan="4"><center><strong>WMT</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center>87.54</center></td><td><center>51.48</center></td><td><center>83.87</center></td><td><center>85.61</center></td><td><center>17.96</center></td><td><center><strong>27.74</strong></center></td><td><center>25.48</center></td><td><center>36.11</center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>97.86</strong></center></td><td><center><strong>65.70</strong></center></td><td><center>98.43</center></td><td><center><strong>87.17</strong></center></td><td><center><strong>27.91</strong></center></td><td><center>23.08</center></td><td><center><strong>27.99</strong></center></td><td><center><strong>39.51</strong></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>86.96</center></td><td><center>56.72</center></td><td><center><strong>98.80</strong></center></td><td><center>85.81</center></td><td><center>24.45</center></td><td><center>14.20</center></td><td><center>25.96</center></td><td><center>39.07</center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>96.45</em></center></td><td><center><em>63.23</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>20.73</em></center></td><td><center><em>7.87</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
</tr>
</tbody>
</table>
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>XQuAD</strong></center></td>
<td colspan="4"><center><strong>STS</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center><strong>42.10</strong></center></td><td><center><strong>62.30</strong></center></td><td><center><strong>60.34</strong></center></td><td><center><strong>77.40</strong></center></td><td><center>49.10</center></td><td><center>50.23</center></td><td><center>83.43</center></td><td><center>83.64</center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>17.75</center></td><td><center>28.11</center></td><td><center>52.02</center></td><td><center>68.43</center></td><td><center><strong>73.96</strong></center></td><td><center><strong>75.16</strong></center></td><td><center>86.45</center></td><td><center>86.31</center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>26.03</center></td><td><center>41.58</center></td><td><center>46.72</center></td><td><center>60.79</center></td><td><center>73.23</center></td><td><center>71.58</center></td><td><center><strong>88.42</strong></center></td><td><center><strong>88.45</strong></center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em>19.14</em></center></td><td><center><em>38.10</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>69.38</em></center></td><td><center><em>69.34</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
</tr>
</tbody>
</table>
## MT-Bench
<<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>1st turn</center></strong></td>
<td><strong><center>2nd turn</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center>4.83</center></td><td><center>5.11</center></td><td><center>4.55</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>5.26</center></td><td><center><strong>5.92</strong></center></td><td><center>4.60</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>5.24</center></td><td><center>5.55</center></td><td><center>4.94</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>5.47</strong></em></center></td><td><center><em><strong>5.92</strong></em></center></td><td><center><em><strong>5.03</strong></em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
</tbody>
</table>
## RoCulturaBench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center>3.38</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>3.26</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-10-09</td><td><center>3.51</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-DPO-2024-10-09</em></td><td><center><em><strong>3.94</strong></em></center></td><td><center><em><strong>100/100</strong></em></center></td>
</tr>
</tbody>
</table>
## RoGemma Model Family
| Model | Link |
|--------------------|:--------:|
|RoGemma-7b-Instruct-2024-06-28| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-06-28) |
|RoGemma-7b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09) |
|*RoGemma-7b-Instruct-DPO-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09) |
## Citation
```
@misc{masala2024vorbecstiromanecsterecipetrain,
title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions},
author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
year={2024},
eprint={2406.18266},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.18266},
}
```
<!-- **APA:**
[More Information Needed] --> |
OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09 | OpenLLM-Ro | 2024-10-10T18:07:28Z | 5 | 0 | null | [
"safetensors",
"gemma",
"ro",
"dataset:OpenLLM-Ro/ro_sft_alpaca",
"dataset:OpenLLM-Ro/ro_sft_alpaca_gpt4",
"dataset:OpenLLM-Ro/ro_sft_dolly",
"dataset:OpenLLM-Ro/ro_sft_selfinstruct_gpt4",
"dataset:OpenLLM-Ro/ro_sft_norobots",
"dataset:OpenLLM-Ro/ro_sft_orca",
"dataset:OpenLLM-Ro/ro_sft_camel",
"dataset:OpenLLM-Ro/ro_sft_oasst",
"dataset:OpenLLM-Ro/ro_sft_ultrachat",
"arxiv:2406.18266",
"base_model:google/gemma-7b",
"base_model:finetune:google/gemma-7b",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | 2024-09-23T16:41:52Z | ---
license: cc-by-nc-4.0
language:
- ro
base_model:
- google/gemma-7b
datasets:
- OpenLLM-Ro/ro_sft_alpaca
- OpenLLM-Ro/ro_sft_alpaca_gpt4
- OpenLLM-Ro/ro_sft_dolly
- OpenLLM-Ro/ro_sft_selfinstruct_gpt4
- OpenLLM-Ro/ro_sft_norobots
- OpenLLM-Ro/ro_sft_orca
- OpenLLM-Ro/ro_sft_camel
- OpenLLM-Ro/ro_sft_oasst
- OpenLLM-Ro/ro_sft_ultrachat
model-index:
- name: OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09
results:
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: Score
type: Score
value: 5.24
- task:
type: text-generation
dataset:
name: RoCulturaBench
type: RoCulturaBench
metrics:
- name: Score
type: Score
value: 3.51
- task:
type: text-generation
dataset:
name: Romanian_Academic_Benchmarks
type: Romanian_Academic_Benchmarks
metrics:
- name: Average accuracy
type: accuracy
value: 50.48
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: Average accuracy
type: accuracy
value: 52.01
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: Average accuracy
type: accuracy
value: 52.37
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: Average accuracy
type: accuracy
value: 66.97
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: Average accuracy
type: accuracy
value: 56.34
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: Average accuracy
type: accuracy
value: 25.98
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_truthfulqa
type: OpenLLM-Ro/ro_truthfulqa
metrics:
- name: Average accuracy
type: accuracy
value: 49.18
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: Average macro-f1
type: macro-f1
value: 86.96
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: Average macro-f1
type: macro-f1
value: 56.72
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary_finetuned
type: LaRoSeDa_binary_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 98.80
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass_finetuned
type: LaRoSeDa_multiclass_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 85.81
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: Average bleu
type: bleu
value: 24.45
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: Average bleu
type: bleu
value: 14.20
- task:
type: text-generation
dataset:
name: WMT_EN-RO_finetuned
type: WMT_EN-RO_finetuned
metrics:
- name: Average bleu
type: bleu
value: 25.96
- task:
type: text-generation
dataset:
name: WMT_RO-EN_finetuned
type: WMT_RO-EN_finetuned
metrics:
- name: Average bleu
type: bleu
value: 39.07
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average exact_match
type: exact_match
value: 26.03
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average f1
type: f1
value: 41.58
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average exact_match
type: exact_match
value: 46.72
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average f1
type: f1
value: 60.79
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average spearman
type: spearman
value: 73.23
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average pearson
type: pearson
value: 71.58
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average spearman
type: spearman
value: 88.42
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average pearson
type: pearson
value: 88.45
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: First turn
type: Score
value: 5.55
- name: Second turn
type: Score
value: 4.94
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: 0-shot
type: accuracy
value: 49.53
- name: 1-shot
type: accuracy
value: 52.53
- name: 3-shot
type: accuracy
value: 51.50
- name: 5-shot
type: accuracy
value: 53.56
- name: 10-shot
type: accuracy
value: 52.53
- name: 25-shot
type: accuracy
value: 52.44
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: 0-shot
type: accuracy
value: 51.81
- name: 1-shot
type: accuracy
value: 52.45
- name: 3-shot
type: accuracy
value: 52.52
- name: 5-shot
type: accuracy
value: 52.70
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: 0-shot
type: accuracy
value: 66.54
- name: 1-shot
type: accuracy
value: 66.69
- name: 3-shot
type: accuracy
value: 67.09
- name: 5-shot
type: accuracy
value: 67.56
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: 0-shot
type: accuracy
value: 58.80
- name: 1-shot
type: accuracy
value: 57.04
- name: 3-shot
type: accuracy
value: 55.85
- name: 5-shot
type: accuracy
value: 54.15
- name: 10-shot
type: accuracy
value: 55.88
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: 1-shot
type: accuracy
value: 22.06
- name: 3-shot
type: accuracy
value: 25.40
- name: 5-shot
type: accuracy
value: 30.48
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: 0-shot
type: macro-f1
value: 87.28
- name: 1-shot
type: macro-f1
value: 86.40
- name: 3-shot
type: macro-f1
value: 87.95
- name: 5-shot
type: macro-f1
value: 86.20
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: 0-shot
type: macro-f1
value: 38.35
- name: 1-shot
type: macro-f1
value: 63.86
- name: 3-shot
type: macro-f1
value: 62.03
- name: 5-shot
type: macro-f1
value: 62.62
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: 0-shot
type: bleu
value: 11.39
- name: 1-shot
type: bleu
value: 28.08
- name: 3-shot
type: bleu
value: 29.18
- name: 5-shot
type: bleu
value: 29.13
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: 0-shot
type: bleu
value: 1.92
- name: 1-shot
type: bleu
value: 9.39
- name: 3-shot
type: bleu
value: 21.81
- name: 5-shot
type: bleu
value: 23.66
- task:
type: text-generation
dataset:
name: XQuAD_EM
type: XQuAD_EM
metrics:
- name: 0-shot
type: exact_match
value: 32.77
- name: 1-shot
type: exact_match
value: 20.25
- name: 3-shot
type: exact_match
value: 18.49
- name: 5-shot
type: exact_match
value: 32.60
- task:
type: text-generation
dataset:
name: XQuAD_F1
type: XQuAD_F1
metrics:
- name: 0-shot
type: f1
value: 47.98
- name: 1-shot
type: f1
value: 34.92
- name: 3-shot
type: f1
value: 33.27
- name: 5-shot
type: f1
value: 50.14
- task:
type: text-generation
dataset:
name: STS_Spearman
type: STS_Spearman
metrics:
- name: 1-shot
type: spearman
value: 71.75
- name: 3-shot
type: spearman
value: 71.83
- name: 5-shot
type: spearman
value: 76.11
- task:
type: text-generation
dataset:
name: STS_Pearson
type: STS_Pearson
metrics:
- name: 1-shot
type: pearson
value: 69.97
- name: 3-shot
type: pearson
value: 69.87
- name: 5-shot
type: pearson
value: 74.89
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
RoGemma is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 7B model**. Links to other models can be found at the bottom of this page.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
- **Developed by:** OpenLLM-Ro
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Language(s):** Romanian
- **License:** cc-by-nc-4.0
- **Finetuned from model:** [gemma-7b](https://huggingface.co/google/gemma-7b)
- **Trained using:** [RoAlpaca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_camel), [RoOpenAssistant](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_oasst), [RoUltraChat](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_ultrachat)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
- **Paper:** https://arxiv.org/abs/2406.18266
## Intended Use
### Intended Use Cases
RoGemma is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09")
instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
chat = [
{"role": "user", "content": instruction},
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```
## Academic Benchmarks
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>ARC</center></strong></td>
<td><strong><center>MMLU</center></strong></td>
<td><strong><center>Winogrande</center></strong></td>
<td><strong><center>Hellaswag</center></strong></td>
<td><strong><center>GSM8k</center></strong></td>
<td><strong><center>TruthfulQA</center></strong></td>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center>41.44</center></td><td><center>40.32</center></td><td><center>47.22</center></td><td><center>55.01</center></td><td><center>47.03</center></td><td><center>9.50</center></td><td><center>49.58</center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>53.41</strong></center></td><td><center><strong>52.44</strong></center></td><td><center>54.44</center></td><td><center><strong>69.36</strong></center></td><td><center><strong>61.96</strong></center></td><td><center>31.06</center></td><td><center><strong>51.23</strong></center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>50.48</em></center></td><td><center><em>52.01</em></center></td><td><center><em>52.37</em></center></td><td><center><em>66.97</em></center></td><td><center><em>56.34</em></center></td><td><center><em>25.98</em></center></td><td><center><em>49.18</em></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>48.27</center></td><td><center>46.66</center></td><td><center><strong>54.45</strong></center></td><td><center>63.73</center></td><td><center>49.33</center></td><td><center><strong>34.98</strong></center></td><td><center>40.45</center></td>
</tr>
</tbody>
</table>
## Downstream tasks
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
<td colspan="4"><center><strong>WMT</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center>87.54</center></td><td><center>51.48</center></td><td><center>83.87</center></td><td><center>85.61</center></td><td><center>17.96</center></td><td><center><strong>27.74</strong></center></td><td><center>25.48</center></td><td><center>36.11</center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center><strong>97.86</strong></center></td><td><center><strong>65.70</strong></center></td><td><center>98.43</center></td><td><center><strong>87.17</strong></center></td><td><center><strong>27.91</strong></center></td><td><center>23.08</center></td><td><center><strong>27.99</strong></center></td><td><center><strong>39.51</strong></center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>86.96</em></center></td><td><center><em>56.72</em></center></td><td><center><em><strong>98.80</strong></em></center></td><td><center><em>85.81</em></center></td><td><center><em>24.45</em></center></td><td><center><em>14.20</em></center></td><td><center><em>25.96</em></center></td><td><center><em>39.07</em></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>96.45</center></td><td><center>63.23</center></td><td><center>-</center></td><td><center>-</center></td><td><center>20.73</center></td><td><center>7.87</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>XQuAD</strong></center></td>
<td colspan="4"><center><strong>STS</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center><strong>42.10</strong></center></td><td><center><strong>62.30</strong></center></td><td><center><strong>60.34</strong></center></td><td><center><strong>77.40</strong></center></td><td><center>49.10</center></td><td><center>50.23</center></td><td><center>83.43</center></td><td><center>83.64</center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>17.75</center></td><td><center>28.11</center></td><td><center>52.02</center></td><td><center>68.43</center></td><td><center><strong>73.96</strong></center></td><td><center><strong>75.16</strong></center></td><td><center>86.45</center></td><td><center>86.31</center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>26.03</em></center></td><td><center><em>41.58</em></center></td><td><center><em>46.72</em></center></td><td><center><em>60.79</em></center></td><td><center><em>73.23</em></center></td><td><center><em>71.58</em></center></td><td><center><em><strong>88.42</strong></em></center></td><td><center><em><strong>88.45</strong></em></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center>19.14</center></td><td><center>38.10</center></td><td><center>-</center></td><td><center>-</center></td><td><center>69.38</center></td><td><center>69.34</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>
## MT-Bench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>1st turn</center></strong></td>
<td><strong><center>2nd turn</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center>4.83</center></td><td><center>5.11</center></td><td><center>4.55</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>5.26</center></td><td><center><strong>5.92</strong></center></td><td><center>4.60</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>5.24</em></center></td><td><center><em>5.55</em></center></td><td><center><em>4.94</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center><strong>5.47</strong></center></td><td><center><strong>5.92</strong></center></td><td><center><strong>5.03</strong></center></td><td><center><strong>160/160</strong></center></td>
</tr>
</tbody>
</table>
## RoCulturaBench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>gemma-1.1-7b-it</td><td><center>3.38</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-2024-06-28</td><td><center>3.26</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoGemma-7b-Instruct-2024-10-09</em></td><td><center><em>3.51</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
</tr>
<tr>
<td>RoGemma-7b-Instruct-DPO-2024-10-09</td><td><center><strong>3.94</strong></center></td><td><center><strong>100/100</strong></center></td>
</tr>
</tbody>
</table>
## RoGemma Model Family
| Model | Link |
|--------------------|:--------:|
|RoGemma-7b-Instruct-2024-06-28| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-06-28) |
|*RoGemma-7b-Instruct-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-2024-10-09) |
|RoGemma-7b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma-7b-Instruct-DPO-2024-10-09) |
## Citation
```
@misc{masala2024vorbecstiromanecsterecipetrain,
title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions},
author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
year={2024},
eprint={2406.18266},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.18266},
}
```
<!-- **APA:**
[More Information Needed] --> |
Blue7Bird/Zero_shot_Soft_ware_project_Mizbert | Blue7Bird | 2024-10-10T18:07:09Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-10T18:05:57Z | ---
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.
- **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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- **Repository:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### 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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### 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
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
MikeRoz/TheDrummer_Behemoth-123B-v1-3.5bpw-h6-exl2 | MikeRoz | 2024-10-10T18:05:10Z | 5 | 1 | null | [
"safetensors",
"mistral",
"license:other",
"exl2",
"region:us"
] | null | 2024-10-10T14:23:41Z | ---
license: other
---
# Join our Discord! https://discord.gg/Nbv9pQ88Xb
## 1500+ members strong 💪
---
[BeaverAI](https://huggingface.co/BeaverAI) proudly presents...
# Behemoth 123B v1 🦣
*When you spend your whole life living under a dome, even the idea of an ocean seems impossible to imagine.*
## Description
Testers have reported:
- Better creativity and variety
- Improved prose
- Less positivity, more unhinged (especially on Metharme)
- Good intelligence, sharp on nuances and recall.
## Links
- Original: https://huggingface.co/TheDrummer/Behemoth-123B-v1
- GGUF: https://huggingface.co/TheDrummer/Behemoth-123B-v1-GGUF
- iMatrix: https://huggingface.co/bartowski/Behemoth-123B-v1-GGUF (recommended for small quants)
## Arsenal (Supported Chat Templates)
- Mistral for Instruct / RP / Story
- Smart, adaptable, familiar
- Metharme (a.k.a. Pygmalion in ST) for RP / Story
- Creative, unhinged, unique
- Text Completion for RP
- You can mix it up and see which works best for you.
### Favorite RP Format
`*action* Dialogue *thoughts* Dialogue *narration*` in 1st person PoV
## What's Next?
- Looking into v1.1...
- Already have plans for a v2!
## Special Thanks
- Thank you to each and everyone who donated in [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier.
- KinjiHakari777, Dr. Fjut, Kistara, Pseudo, AlexTheVP, Dakkidaze, EvarinSharath'fe, ONTHEREDTEAM, F, Mariana, Garg, Silva, Grozi, & **Phaelon**
<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/FNWdi0WlH-Xd3fjkGVPpp.mpga"></audio>
|
Gummybear05/wav2vec2-Y_pause | Gummybear05 | 2024-10-10T18:03:05Z | 21 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-10T15:26:28Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-Y_pause
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. -->
# wav2vec2-Y_pause
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6778
- Cer: 39.4267
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 5.1062 | 0.1290 | 200 | 4.7053 | 100.0 |
| 4.8751 | 0.2581 | 400 | 4.8829 | 100.0 |
| 4.7665 | 0.3871 | 600 | 4.6329 | 98.8781 |
| 4.6575 | 0.5161 | 800 | 4.7058 | 98.4199 |
| 4.2511 | 0.6452 | 1000 | 4.2469 | 90.7777 |
| 3.1636 | 0.7742 | 1200 | 3.3817 | 69.3844 |
| 2.6261 | 0.9032 | 1400 | 2.9457 | 60.4676 |
| 2.1994 | 1.0323 | 1600 | 2.6949 | 56.0092 |
| 1.924 | 1.1613 | 1800 | 2.5125 | 52.3085 |
| 1.7291 | 1.2903 | 2000 | 2.2571 | 49.5653 |
| 1.5934 | 1.4194 | 2200 | 2.0517 | 46.2523 |
| 1.5086 | 1.5484 | 2400 | 2.1590 | 46.3757 |
| 1.4041 | 1.6774 | 2600 | 2.0795 | 46.1407 |
| 1.3266 | 1.8065 | 2800 | 2.1936 | 47.5388 |
| 1.2494 | 1.9355 | 3000 | 2.0095 | 45.1891 |
| 1.1305 | 2.0645 | 3200 | 1.8807 | 43.5092 |
| 1.0493 | 2.1935 | 3400 | 1.7053 | 40.0141 |
| 0.9978 | 2.3226 | 3600 | 1.8685 | 43.1508 |
| 0.9689 | 2.4516 | 3800 | 1.8416 | 41.8938 |
| 0.9527 | 2.5806 | 4000 | 1.7686 | 42.1405 |
| 0.8927 | 2.7097 | 4200 | 1.7281 | 40.0611 |
| 0.8958 | 2.8387 | 4400 | 1.6940 | 39.6264 |
| 0.8855 | 2.9677 | 4600 | 1.6778 | 39.4267 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Rameen-Mahmood/devid-llm | Rameen-Mahmood | 2024-10-10T18:01:57Z | 161 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-10T18:01:44Z | ---
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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
benholloway/my_awesome_food_model_resnet | benholloway | 2024-10-10T17:59:15Z | 203 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/resnet-50",
"base_model:finetune:microsoft/resnet-50",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-10-10T17:41:48Z | ---
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model_resnet
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. -->
# my_awesome_food_model_resnet
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4383
- Accuracy: 0.661
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2955 | 0.992 | 62 | 4.2486 | 0.18 |
| 3.4887 | 2.0 | 125 | 3.3899 | 0.312 |
| 2.7039 | 2.992 | 187 | 2.6616 | 0.475 |
| 2.1832 | 4.0 | 250 | 2.1833 | 0.565 |
| 1.946 | 4.992 | 312 | 1.9504 | 0.631 |
| 1.7753 | 6.0 | 375 | 1.7184 | 0.638 |
| 1.666 | 6.992 | 437 | 1.5985 | 0.667 |
| 1.5402 | 8.0 | 500 | 1.4900 | 0.667 |
| 1.5239 | 8.992 | 562 | 1.4500 | 0.665 |
| 1.5147 | 9.92 | 620 | 1.4383 | 0.661 |
### Framework versions
- Transformers 4.45.0
- Pytorch 2.2.2+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0
|
mergekit-community/qwen-cot | mergekit-community | 2024-10-10T17:54:14Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:merge:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:happzy2633/qwen2.5-7b-ins-v3",
"base_model:merge:happzy2633/qwen2.5-7b-ins-v3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T17:49:19Z | ---
base_model:
- Qwen/Qwen2.5-Math-7B-Instruct
- happzy2633/qwen2.5-7b-ins-v3
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:
* [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct)
* [happzy2633/qwen2.5-7b-ins-v3](https://huggingface.co/happzy2633/qwen2.5-7b-ins-v3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: happzy2633/qwen2.5-7b-ins-v3
- model: Qwen/Qwen2.5-Math-7B-Instruct
merge_method: slerp
base_model: happzy2633/qwen2.5-7b-ins-v3
dtype: bfloat16
parameters:
t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
```
|
bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF | bartowski | 2024-10-10T17:53:23Z | 764 | 7 | null | [
"gguf",
"text-generation",
"base_model:nvidia/Mistral-NeMo-Minitron-8B-Instruct",
"base_model:quantized:nvidia/Mistral-NeMo-Minitron-8B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-10T17:07:30Z | ---
base_model: nvidia/Mistral-NeMo-Minitron-8B-Instruct
pipeline_tag: text-generation
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of Mistral-NeMo-Minitron-8B-Instruct
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3901">b3901</a> for quantization.
Original model: https://huggingface.co/nvidia/Mistral-NeMo-Minitron-8B-Instruct
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<extra_id_0>System
{system_prompt}
<extra_id_1>User
{prompt}
<extra_id_1>Assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Mistral-NeMo-Minitron-8B-Instruct-f16.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-f16.gguf) | f16 | 16.84GB | false | Full F16 weights. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q8_0.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q8_0.gguf) | Q8_0 | 8.95GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q6_K_L.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q6_K_L.gguf) | Q6_K_L | 7.17GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q6_K.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q6_K.gguf) | Q6_K | 6.91GB | false | Very high quality, near perfect, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q5_K_L.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q5_K_L.gguf) | Q5_K_L | 6.33GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q5_K_M.gguf) | Q5_K_M | 6.00GB | false | High quality, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5.86GB | false | High quality, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q4_K_L.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q4_K_L.gguf) | Q4_K_L | 5.54GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q4_K_M.gguf) | Q4_K_M | 5.15GB | false | Good quality, default size for must use cases, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q3_K_XL.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q3_K_XL.gguf) | Q3_K_XL | 5.01GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.91GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q4_0.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q4_0.gguf) | Q4_0 | 4.90GB | false | Legacy format, generally not worth using over similarly sized formats |
| [Mistral-NeMo-Minitron-8B-Instruct-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q4_0_8_8.gguf) | Q4_0_8_8 | 4.88GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). *Don't use on Mac or Windows*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q4_0_4_8.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q4_0_4_8.gguf) | Q4_0_4_8 | 4.88GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). *Don't use on Mac or Windows*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q4_0_4_4.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q4_0_4_4.gguf) | Q4_0_4_4 | 4.88GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. *Don't use on Mac or Windows*. |
| [Mistral-NeMo-Minitron-8B-Instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-IQ4_XS.gguf) | IQ4_XS | 4.66GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q3_K_L.gguf) | Q3_K_L | 4.54GB | false | Lower quality but usable, good for low RAM availability. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q3_K_M.gguf) | Q3_K_M | 4.21GB | false | Low quality. |
| [Mistral-NeMo-Minitron-8B-Instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-IQ3_M.gguf) | IQ3_M | 3.98GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q2_K_L.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q2_K_L.gguf) | Q2_K_L | 3.86GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.83GB | false | Low quality, not recommended. |
| [Mistral-NeMo-Minitron-8B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-IQ3_XS.gguf) | IQ3_XS | 3.68GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Mistral-NeMo-Minitron-8B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-Q2_K.gguf) | Q2_K | 3.33GB | false | Very low quality but surprisingly usable. |
| [Mistral-NeMo-Minitron-8B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Instruct-IQ2_M.gguf) | IQ2_M | 3.10GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF --include "Mistral-NeMo-Minitron-8B-Instruct-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Mistral-NeMo-Minitron-8B-Instruct-GGUF --include "Mistral-NeMo-Minitron-8B-Instruct-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (Mistral-NeMo-Minitron-8B-Instruct-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
ezrab/speecht5_finetuned_lj_speech | ezrab | 2024-10-10T17:40:09Z | 82 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:keithito/lj_speech",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-speech | 2024-10-10T13:38:07Z | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- keithito/lj_speech
model-index:
- name: speecht5_finetuned_lj_speech
results: []
pipeline_tag: text-to-speech
---
<!-- 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. -->
# speecht5_finetuned_lj_speech
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the keithito/lj_speech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3772
## 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: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.4519 | 1.3569 | 500 | 0.4035 |
| 0.4307 | 2.7137 | 1000 | 0.3897 |
| 0.4243 | 4.0706 | 1500 | 0.3842 |
| 0.4154 | 5.4274 | 2000 | 0.3814 |
| 0.4158 | 6.7843 | 2500 | 0.3793 |
| 0.409 | 8.1411 | 3000 | 0.3783 |
| 0.4112 | 9.4980 | 3500 | 0.3774 |
| 0.4135 | 10.8548 | 4000 | 0.3772 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0 |
deepnet/C2ReadyModel2 | deepnet | 2024-10-10T17:24:43Z | 41 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-09-30T09:49:33Z | ---
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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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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IvanPerkhun/service_categorizer_v2 | IvanPerkhun | 2024-10-10T17:18:00Z | 115 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-09-25T13:08:18Z | ---
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
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### 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
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]
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[More Information Needed]
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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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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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
Helf/helf-poc | Helf | 2024-10-10T17:17:53Z | 18 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"base_model:openai/whisper-small.en",
"base_model:finetune:openai/whisper-small.en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-10T14:26:17Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-small.en
tags:
- generated_from_trainer
model-index:
- name: HEFL poc
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. -->
# HEFL poc
This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6771
- eval_model_preparation_time: 0.011
- eval_wer: 25.6383
- eval_runtime: 25.0785
- eval_samples_per_second: 1.954
- eval_steps_per_second: 0.279
- step: 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 25
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
catrinbaze/merge-llama-3-8b | catrinbaze | 2024-10-10T17:09:15Z | 5 | 0 | null | [
"safetensors",
"llama",
"merge",
"mergekit",
"lazymergekit",
"catrinbaze/llama-refueled-merge",
"NousResearch/Meta-Llama-3-8B-instruct",
"Locutusque/Llama-3-Orca-1.0-8B",
"lighteternal/Llama3-merge-biomed-8b",
"mlabonne/NeuralDaredevil-8B-abliterated",
"mlabonne/Daredevil-8B",
"base_model:Locutusque/Llama-3-Orca-1.0-8B",
"base_model:merge:Locutusque/Llama-3-Orca-1.0-8B",
"base_model:catrinbaze/llama-refueled-merge",
"base_model:merge:catrinbaze/llama-refueled-merge",
"base_model:lighteternal/Llama3-merge-biomed-8b",
"base_model:merge:lighteternal/Llama3-merge-biomed-8b",
"base_model:mlabonne/Daredevil-8B",
"base_model:merge:mlabonne/Daredevil-8B",
"base_model:mlabonne/NeuralDaredevil-8B-abliterated",
"base_model:merge:mlabonne/NeuralDaredevil-8B-abliterated",
"region:us"
] | null | 2024-10-10T17:05:29Z | ---
base_model:
- catrinbaze/llama-refueled-merge
- NousResearch/Meta-Llama-3-8B-instruct
- Locutusque/Llama-3-Orca-1.0-8B
- lighteternal/Llama3-merge-biomed-8b
- mlabonne/NeuralDaredevil-8B-abliterated
- mlabonne/Daredevil-8B
tags:
- merge
- mergekit
- lazymergekit
- catrinbaze/llama-refueled-merge
- NousResearch/Meta-Llama-3-8B-instruct
- Locutusque/Llama-3-Orca-1.0-8B
- lighteternal/Llama3-merge-biomed-8b
- mlabonne/NeuralDaredevil-8B-abliterated
- mlabonne/Daredevil-8B
---
# merge-llama-3-8b
merge-llama-3-8b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [catrinbaze/llama-refueled-merge](https://huggingface.co/catrinbaze/llama-refueled-merge)
* [NousResearch/Meta-Llama-3-8B-instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-instruct)
* [Locutusque/Llama-3-Orca-1.0-8B](https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B)
* [lighteternal/Llama3-merge-biomed-8b](https://huggingface.co/lighteternal/Llama3-merge-biomed-8b)
* [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated)
* [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B)
## 🧩 Configuration
```yaml
slices:
models:
- model: NousResearch/Meta-Llama-3-8B
# No parameters necessary for base model
- model: catrinbaze/llama-refueled-merge
parameters:
density: 0.6
weight: 0.6
- model: NousResearch/Meta-Llama-3-8B-instruct
parameters:
density: 0.58
weight: 0.2
- model: Locutusque/Llama-3-Orca-1.0-8B
parameters:
density: 0.56
weight: 0.05
- model: lighteternal/Llama3-merge-biomed-8b
parameters:
density: 0.56
weight: 0.05
- model: mlabonne/NeuralDaredevil-8B-abliterated
parameters:
density: 0.55
weight: 0.05
- model: mlabonne/Daredevil-8B
parameters:
density: 0.55
weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "catrinbaze/merge-llama-3-8b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
datalawyer/segmenter-distill-v0.1 | datalawyer | 2024-10-10T17:06:54Z | 121 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:adalbertojunior/segmentacao",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-10T16:58:08Z | ---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- adalbertojunior/segmentacao
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test_v7
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: adalbertojunior/segmentacao
type: adalbertojunior/segmentacao
config: segmentacao
split: validation
args: segmentacao
metrics:
- name: Precision
type: precision
value: 0.6657754010695187
- name: Recall
type: recall
value: 0.6859504132231405
- name: F1
type: f1
value: 0.6757123473541385
- name: Accuracy
type: accuracy
value: 0.9990518084066471
---
<!-- 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_v7
This model is a fine-tuned version of [./models/distill-bge-retromae-step](https://huggingface.co/./models/distill-bge-retromae-step) on the adalbertojunior/segmentacao dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0045
- Precision: 0.6658
- Recall: 0.6860
- F1: 0.6757
- Accuracy: 0.9991
## 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: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.0637 | 100 | 0.0048 | 0.5339 | 0.5647 | 0.5489 | 0.9984 |
| No log | 0.1274 | 200 | 0.0048 | 0.5567 | 0.6226 | 0.5878 | 0.9987 |
| No log | 0.1911 | 300 | 0.0048 | 0.5745 | 0.5950 | 0.5846 | 0.9988 |
| No log | 0.2548 | 400 | 0.0048 | 0.5622 | 0.5978 | 0.5794 | 0.9988 |
| 0.0061 | 0.3185 | 500 | 0.0069 | 0.48 | 0.5950 | 0.5314 | 0.9983 |
| 0.0061 | 0.3822 | 600 | 0.0061 | 0.5692 | 0.6116 | 0.5896 | 0.9987 |
| 0.0061 | 0.4459 | 700 | 0.0052 | 0.5736 | 0.6226 | 0.5971 | 0.9988 |
| 0.0061 | 0.5096 | 800 | 0.0055 | 0.5921 | 0.6198 | 0.6057 | 0.9988 |
| 0.0061 | 0.5733 | 900 | 0.0057 | 0.6126 | 0.6446 | 0.6282 | 0.9989 |
| 0.0008 | 0.6370 | 1000 | 0.0065 | 0.5635 | 0.6116 | 0.5865 | 0.9987 |
| 0.0008 | 0.7007 | 1100 | 0.0060 | 0.5725 | 0.6529 | 0.6100 | 0.9987 |
| 0.0008 | 0.7645 | 1200 | 0.0061 | 0.5704 | 0.6474 | 0.6065 | 0.9988 |
| 0.0008 | 0.8282 | 1300 | 0.0053 | 0.5813 | 0.6501 | 0.6138 | 0.9988 |
| 0.0008 | 0.8919 | 1400 | 0.0045 | 0.6658 | 0.6860 | 0.6757 | 0.9991 |
| 0.0004 | 0.9556 | 1500 | 0.0049 | 0.6497 | 0.6694 | 0.6594 | 0.9990 |
| 0.0004 | 1.0193 | 1600 | 0.0054 | 0.5707 | 0.6446 | 0.6054 | 0.9988 |
| 0.0004 | 1.0830 | 1700 | 0.0047 | 0.6376 | 0.6639 | 0.6505 | 0.9990 |
| 0.0004 | 1.1467 | 1800 | 0.0048 | 0.5922 | 0.6722 | 0.6297 | 0.9989 |
| 0.0004 | 1.2104 | 1900 | 0.0041 | 0.6455 | 0.6722 | 0.6586 | 0.9990 |
| 0.0002 | 1.2741 | 2000 | 0.0053 | 0.5686 | 0.6391 | 0.6018 | 0.9987 |
| 0.0002 | 1.3378 | 2100 | 0.0046 | 0.6495 | 0.6942 | 0.6711 | 0.9990 |
| 0.0002 | 1.4015 | 2200 | 0.0049 | 0.5947 | 0.6749 | 0.6323 | 0.9988 |
| 0.0002 | 1.4652 | 2300 | 0.0045 | 0.6125 | 0.6749 | 0.6422 | 0.9989 |
| 0.0002 | 1.5289 | 2400 | 0.0045 | 0.5701 | 0.6722 | 0.6169 | 0.9988 |
| 0.0002 | 1.5926 | 2500 | 0.0058 | 0.5321 | 0.6391 | 0.5807 | 0.9986 |
| 0.0002 | 1.6563 | 2600 | 0.0056 | 0.5110 | 0.6419 | 0.5690 | 0.9985 |
| 0.0002 | 1.7200 | 2700 | 0.0052 | 0.5792 | 0.6446 | 0.6102 | 0.9988 |
| 0.0002 | 1.7837 | 2800 | 0.0047 | 0.5941 | 0.6612 | 0.6258 | 0.9989 |
| 0.0002 | 1.8474 | 2900 | 0.0051 | 0.5655 | 0.6419 | 0.6013 | 0.9988 |
| 0.0001 | 1.9111 | 3000 | 0.0044 | 0.5866 | 0.6529 | 0.6180 | 0.9989 |
| 0.0001 | 1.9748 | 3100 | 0.0042 | 0.5792 | 0.6446 | 0.6102 | 0.9988 |
| 0.0001 | 2.0385 | 3200 | 0.0045 | 0.6015 | 0.6694 | 0.6336 | 0.9989 |
| 0.0001 | 2.1022 | 3300 | 0.0063 | 0.5409 | 0.6556 | 0.5928 | 0.9987 |
| 0.0001 | 2.1659 | 3400 | 0.0047 | 0.5887 | 0.6584 | 0.6216 | 0.9989 |
| 0.0001 | 2.2297 | 3500 | 0.0045 | 0.6131 | 0.6722 | 0.6413 | 0.9989 |
| 0.0001 | 2.2934 | 3600 | 0.0047 | 0.6193 | 0.6722 | 0.6446 | 0.9989 |
| 0.0001 | 2.3571 | 3700 | 0.0047 | 0.6091 | 0.6612 | 0.6341 | 0.9989 |
| 0.0001 | 2.4208 | 3800 | 0.0047 | 0.6205 | 0.6667 | 0.6428 | 0.9989 |
| 0.0001 | 2.4845 | 3900 | 0.0044 | 0.6070 | 0.6722 | 0.6379 | 0.9989 |
| 0.0001 | 2.5482 | 4000 | 0.0052 | 0.5355 | 0.6226 | 0.5758 | 0.9987 |
| 0.0001 | 2.6119 | 4100 | 0.0047 | 0.5871 | 0.6501 | 0.6170 | 0.9989 |
| 0.0001 | 2.6756 | 4200 | 0.0049 | 0.5739 | 0.6419 | 0.6060 | 0.9988 |
| 0.0001 | 2.7393 | 4300 | 0.0049 | 0.5634 | 0.6364 | 0.5977 | 0.9988 |
| 0.0001 | 2.8030 | 4400 | 0.0052 | 0.5634 | 0.6364 | 0.5977 | 0.9988 |
| 0.0 | 2.8667 | 4500 | 0.0049 | 0.5739 | 0.6419 | 0.6060 | 0.9988 |
| 0.0 | 2.9304 | 4600 | 0.0044 | 0.5796 | 0.6419 | 0.6092 | 0.9988 |
| 0.0 | 2.9941 | 4700 | 0.0047 | 0.5796 | 0.6419 | 0.6092 | 0.9988 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
mergekit-community/mergekit-ties-ueirogz | mergekit-community | 2024-10-10T17:06:44Z | 5 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1",
"base_model:merge:EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:merge:Qwen/Qwen2.5-7B-Instruct",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:merge:Qwen/Qwen2.5-Math-7B",
"base_model:happzy2633/qwen2.5-7b-ins-v3",
"base_model:merge:happzy2633/qwen2.5-7b-ins-v3",
"base_model:katanemo/Arch-Function-7B",
"base_model:merge:katanemo/Arch-Function-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T17:02:34Z | ---
base_model:
- Qwen/Qwen2.5-7B-Instruct
- Qwen/Qwen2.5-Math-7B
- EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1
- katanemo/Arch-Function-7B
- happzy2633/qwen2.5-7b-ins-v3
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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [happzy2633/qwen2.5-7b-ins-v3](https://huggingface.co/happzy2633/qwen2.5-7b-ins-v3) as a base.
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
* [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B)
* [EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1)
* [katanemo/Arch-Function-7B](https://huggingface.co/katanemo/Arch-Function-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: happzy2633/qwen2.5-7b-ins-v3
#no parameters necessary for base model
- model: Qwen/Qwen2.5-Math-7B
parameters:
density: 0.4
weight: 0.4
- model: Qwen/Qwen2.5-7B-Instruct
parameters:
density: 0.2
weight: 0.2
- model: EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1
parameters:
density: 0.3
weight: 0.3
- model: katanemo/Arch-Function-7B
parameters:
density: 0.1
weight: 0.1
merge_method: ties
base_model: happzy2633/qwen2.5-7b-ins-v3
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
```
|
zelk12/RAt0.25-gemma-2-RI-9B | zelk12 | 2024-10-10T17:00:19Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25",
"base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25",
"base_model:zelk12/recoilme-gemma-2-Ifable-9B-v0.1",
"base_model:merge:zelk12/recoilme-gemma-2-Ifable-9B-v0.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T16:53:55Z | ---
base_model:
- zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25
- zelk12/recoilme-gemma-2-Ifable-9B-v0.1
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/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25)
* [zelk12/recoilme-gemma-2-Ifable-9B-v0.1](https://huggingface.co/zelk12/recoilme-gemma-2-Ifable-9B-v0.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25
- model: zelk12/recoilme-gemma-2-Ifable-9B-v0.1
merge_method: slerp
base_model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25
dtype: bfloat16
parameters:
t: 0.5
```
|
julienkay/sentis-DepthPro-uint8 | julienkay | 2024-10-10T16:48:53Z | 5 | 1 | unity-sentis | [
"unity-sentis",
"depth-estimation",
"base_model:apple/DepthPro",
"base_model:finetune:apple/DepthPro",
"license:apple-ascl",
"region:us"
] | depth-estimation | 2024-10-10T16:18:23Z | ---
license: apple-ascl
base_model:
- apple/DepthPro
library_name: unity-sentis
pipeline_tag: depth-estimation
---
The [DepthPro](https://huggingface.co/apple/DepthPro) model converted to [Unity Sentis](https://unity.com/products/sentis)
The model uses a static input with shape (1, 3, 1536, 1536). The image is expected to be in the [-1.0, 1.0] range. Models were converted and quantized to uint8 format using Sentis v2.1.0 |
Colby/starcoder-manhan-corpus | Colby | 2024-10-10T16:43:37Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt_bigcode",
"feature-extraction",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"base_model:bigcode/starcoderbase-7b",
"base_model:finetune:bigcode/starcoderbase-7b",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-06T03:54:34Z | ---
base_model: bigcode/starcoderbase-7b
library_name: transformers
license: other
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
widget:
- messages:
- role: user
content: What is your favorite condiment?
---
# 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)
``` |
ChenmieNLP/Zephyr-7B-Beta-Helpful | ChenmieNLP | 2024-10-10T16:32:34Z | 151 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T16:21:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
evgmaslov/Code-Llama-3-8B-cars | evgmaslov | 2024-10-10T16:27:04Z | 44 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:ajibawa-2023/Code-Llama-3-8B",
"base_model:adapter:ajibawa-2023/Code-Llama-3-8B",
"license:llama3",
"region:us"
] | null | 2024-08-29T12:50:39Z | ---
base_model: ajibawa-2023/Code-Llama-3-8B
library_name: peft
license: llama3
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Code-Llama-3-8B-cars
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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/evg_maslov/leap71/runs/4ywzr92y)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/evg_maslov/leap71/runs/4ywzr92y)
# Code-Llama-3-8B-cars
This model is a fine-tuned version of [ajibawa-2023/Code-Llama-3-8B](https://huggingface.co/ajibawa-2023/Code-Llama-3-8B) 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1
### Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0 |
kaitchup/Meta-Llama-3.1-8B-autoround-gptq-4bit-sym | kaitchup | 2024-10-10T16:23:09Z | 142 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"auto-gptq",
"AutoRound",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2024-08-06T09:26:49Z | ---
language:
- en
library_name: transformers
license: cc-by-4.0
tags:
- auto-gptq
- AutoRound
---
## Model Details
This is [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) quantized with AutoRound (symmetric quantization) and serialized with the GPTQ format in 4-bit. The model has been created, tested, and evaluated by The Kaitchup.
Details on quantization process, evaluation, and how to use the model here:
[The Best Quantization Methods to Run Llama 3.1 on Your GPU](https://newsletter.kaitchup.com/p/the-best-quantization-methods-to)
- **Developed by:** [The Kaitchup](https://newsletter.kaitchup.com/)
- **Language(s) (NLP):** English
- **License:** cc-by-4.0
|
CATIE-AQ/FAT5-small-flan-en | CATIE-AQ | 2024-10-10T16:14:54Z | 112 | 0 | transformers | [
"transformers",
"safetensors",
"flash_t5",
"feature-extraction",
"custom_code",
"en",
"license:apache-2.0",
"region:us"
] | feature-extraction | 2024-04-26T08:39:10Z | ---
language: en
license: apache-2.0
---
### Description
Adaptation of the [flan-t5-small](https://huggingface.co/google/flan-t5-small) weights to make it compatible with the [FAT5](https://github.com/catie-aq/flashT5) framework (Flash Attention T5).
This adaptation should enable the user to efficiently continue the pre-training of the flan-t5 to adapt it to more recent data, or to specialize it in a specific domain, for example.
### Usage
```
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("CATIE-AQ/FAT5-small-flan-en", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("CATIE-AQ/FAT5-small-flan-en")
``` |
mwmathis/DeepLabCutModelZoo-primate_face | mwmathis | 2024-10-10T16:07:30Z | 0 | 1 | null | [
"computer_vision",
"pose_estimation",
"keypoint-detection",
"license:other",
"region:us"
] | keypoint-detection | 2022-11-23T00:12:11Z | ---
license: other
tags:
- computer_vision
- pose_estimation
pipeline_tag: keypoint-detection
---
Model contributed by Claire Witham at Centre for Macaques, MRC Harwell, UK. This model is trained on photos and videos of rhesus macaque faces – mostly forward facing or in profile. Includes range of ages from infant to adult and both sexes. Shows reasonable transference to other primates especially other macaque species. Read more [here](https://static1.squarespace.com/static/57f6d51c9f74566f55ecf271/t/5ebd4c97b4583a21664b7b62/1589464216742/macaque_face_landmarks.pdf) |
mwmathis/DeepLabCutModelZoo-horse_sideview | mwmathis | 2024-10-10T16:07:22Z | 0 | 0 | null | [
"computer_vision",
"pose_estimation",
"keypoint-detection",
"arxiv:1909.11229",
"license:other",
"region:us"
] | keypoint-detection | 2022-11-22T22:21:16Z | ---
license: other
tags:
- computer_vision
- pose_estimation
pipeline_tag: keypoint-detection
---
Non-commercial use permitted. Copyright authors of Mathis, Biasi et al. WACV
A pre-trained horse network! Dataset/networks from Mathis et al. 2019 arXiv/WACV 2021. Note, this currently works on videos as shown, horses of different sizes and coat colors, but walking left to right. Another model will be released soon that has added utility for horses. And if you use this model, please also cite our paper: https://arxiv.org/pdf/1909.11229.pdf |
RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf | RichardErkhov | 2024-10-10T15:59:46Z | 16 | 0 | null | [
"gguf",
"arxiv:2403.12024",
"endpoints_compatible",
"region:us"
] | null | 2024-10-10T13:18:25Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Taigi-Llama-2-Translator-7B - GGUF
- Model creator: https://huggingface.co/Bohanlu/
- Original model: https://huggingface.co/Bohanlu/Taigi-Llama-2-Translator-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Taigi-Llama-2-Translator-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q2_K.gguf) | Q2_K | 2.46GB |
| [Taigi-Llama-2-Translator-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.IQ3_XS.gguf) | IQ3_XS | 2.72GB |
| [Taigi-Llama-2-Translator-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.IQ3_S.gguf) | IQ3_S | 2.86GB |
| [Taigi-Llama-2-Translator-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q3_K_S.gguf) | Q3_K_S | 2.86GB |
| [Taigi-Llama-2-Translator-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.IQ3_M.gguf) | IQ3_M | 3.02GB |
| [Taigi-Llama-2-Translator-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q3_K.gguf) | Q3_K | 3.19GB |
| [Taigi-Llama-2-Translator-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q3_K_M.gguf) | Q3_K_M | 3.19GB |
| [Taigi-Llama-2-Translator-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q3_K_L.gguf) | Q3_K_L | 3.47GB |
| [Taigi-Llama-2-Translator-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.IQ4_XS.gguf) | IQ4_XS | 3.52GB |
| [Taigi-Llama-2-Translator-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q4_0.gguf) | Q4_0 | 3.69GB |
| [Taigi-Llama-2-Translator-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.IQ4_NL.gguf) | IQ4_NL | 3.71GB |
| [Taigi-Llama-2-Translator-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q4_K_S.gguf) | Q4_K_S | 3.72GB |
| [Taigi-Llama-2-Translator-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q4_K.gguf) | Q4_K | 3.93GB |
| [Taigi-Llama-2-Translator-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q4_K_M.gguf) | Q4_K_M | 3.93GB |
| [Taigi-Llama-2-Translator-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q4_1.gguf) | Q4_1 | 4.08GB |
| [Taigi-Llama-2-Translator-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q5_0.gguf) | Q5_0 | 4.47GB |
| [Taigi-Llama-2-Translator-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q5_K_S.gguf) | Q5_K_S | 4.47GB |
| [Taigi-Llama-2-Translator-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q5_K.gguf) | Q5_K | 4.59GB |
| [Taigi-Llama-2-Translator-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q5_K_M.gguf) | Q5_K_M | 4.59GB |
| [Taigi-Llama-2-Translator-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q5_1.gguf) | Q5_1 | 4.86GB |
| [Taigi-Llama-2-Translator-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q6_K.gguf) | Q6_K | 5.3GB |
| [Taigi-Llama-2-Translator-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Bohanlu_-_Taigi-Llama-2-Translator-7B-gguf/blob/main/Taigi-Llama-2-Translator-7B.Q8_0.gguf) | Q8_0 | 6.86GB |
Original model description:
---
license: cc-by-nc-sa-4.0
---
<p align="center">
<img src="https://github.com/lbh0830/TW-Hokkien-LLM/blob/main/pics/logo.jpg?raw=true" alt="Taigi-llama-logo" width="350">
</p>
# Model Card for Taigi-Llama-2-Translator-7B
The Taigi-Llama-2-Translator series are built based on the Taigi-Llama-2 series model. We conducted fine-tuning on 263k parallel data to create a translation model for Taiwanese Hokkien and related languages.
For more details, please refer to our [GitHub repository](https://github.com/lbh0830/TW-Hokkien-LLM/tree/main) and the paper: [Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems](https://arxiv.org/abs/2403.12024)
Explore other models and datasets in the [Taiwanese Hokkien LLM collection](https://huggingface.co/collections/Bohanlu/taiwanese-hokkien-llm-6614ba7456e6789bc2f10ca0).
## Model description
- **Base Model:** [Bohanlu/Taigi-Llama-2-7B](https://huggingface.co/Bohanlu/Taigi-Llama-2-7B)
- **Usage:** This model can be used for translating between Traditional Chinese or English and Taiwanese Hokkien (Hanzi, POJ, or Hanlo). It also supports translation between different scripts of Taiwanese Hokkien (Hanzi, POJ, Hanlo).
- **Language(s) (NLP):** Taiwanese Hokkien (Hanzi, POJ and Hanlo), Traditional Chinese and English
- **Input:** Text in source language
- **Output:** Text in target language
- **Model Size:** 7B parameters
## Prompt Template
```
{BOS}[TRANS]\n{source_sentence}\n[/TRANS]\n[{target_language}]\n
```
- `source_sentence`: The sentence you want to translate.
- `target_language`: The target language you want to translate to. Use "ZH" for Traditional Chinese, "EN" for English, "POJ" for Taiwanese Hokkien POJ, "HL" for Taiwanese Hokkien Hanlo, and "HAN" for Taiwanese Hokkien Hanzi.
- Ensure there's a newline at the end.
## Usage Example
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextGenerationPipeline
import torch
import accelerate
def get_pipeline(path:str, tokenizer:AutoTokenizer, accelerator:accelerate.Accelerator) -> TextGenerationPipeline:
model = AutoModelForCausalLM.from_pretrained(
path, torch_dtype=torch.float16, device_map='auto', trust_remote_code=True)
terminators = [tokenizer.eos_token_id, tokenizer.pad_token_id]
pipeline = TextGenerationPipeline(model = model, tokenizer = tokenizer, num_workers=accelerator.state.num_processes*4, pad_token_id=tokenizer.pad_token_id, eos_token_id=terminators)
return pipeline
model_dir = "Bohanlu/Taigi-Llama-2-Translator-7B" # or "Bohanlu/Taigi-Llama-2-Translator-13B" for the 13B model
tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False)
accelerator = accelerate.Accelerator()
pipe = get_pipeline(model_dir, tokenizer, accelerator)
PROMPT_TEMPLATE = "[TRANS]\n{source_sentence}\n[/TRANS]\n[{target_language}]\n"
def translate(source_sentence:str, target_language:str) -> str:
prompt = PROMPT_TEMPLATE.format(source_sentence=source_sentence, target_language=target_language)
out = pipe(prompt, return_full_text=False, repetition_penalty=1.1, do_sample=False)[0]['generated_text']
return out[:out.find("[/")].strip()
source_sentence = "How are you today？"
print("To Hanzi: " + translate(source_sentence, "HAN"))
# Output: To Hanzi: 你今仔日好無？
print("To POJ: " + translate(source_sentence, "POJ"))
# Output: To POJ: Lí kin-á-ji̍t án-chóaⁿ?
print("To Traditional Chinese: " + translate(source_sentence, "ZH"))
# Output: To Traditional Chinese: 你今天好嗎？
print("To Hanlo: " + translate(source_sentence, "HL"))
# Output: To Hanlo: 你今仔日好無？
```
## Citation
If you find the resources in the Taiwanese Hokkien LLM collection useful in your work, please cite it using the following reference:
```
@misc{lu2024enhancing,
title={Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems},
author={Bo-Han Lu and Yi-Hsuan Lin and En-Shiun Annie Lee and Richard Tzong-Han Tsai},
year={2024},
eprint={2403.12024},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Quant-Cartel/SorcererLM-22B-iMat-GGUF | Quant-Cartel | 2024-10-10T15:44:07Z | 4,942 | 0 | null | [
"gguf",
"iMat",
"GGUF",
"text-generation",
"base_model:InferenceIllusionist/SorcererLM-22B",
"base_model:quantized:InferenceIllusionist/SorcererLM-22B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2024-09-23T16:44:25Z | ---
license: apache-2.0
base_model_relation: quantized
quantized_by: Quant-Cartel
base_model: InferenceIllusionist/SorcererLM-22B
pipeline_tag: text-generation
tags:
- iMat
- GGUF
---
```
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d888 888b 8888 8888 ,"Y88b 888 8e d88
C8888 8888D 8888 8888 "8" 888 888 88b d88888
Y888 888P Y888 888P ,ee 888 888 888 888
"88 88" "88 88" "88 888 888 888 888
b
8b,
e88'Y88 d8 888
d888 'Y ,"Y88b 888,8, d88 ,e e, 888
C8888 "8" 888 888 " d88888 d88 88b 888
Y888 ,d ,ee 888 888 888 888 , 888
"88,d88 "88 888 888 888 "YeeP" 888
PROUDLY PRESENTS
```
# SorcererLM-22B-iMat-GGUF
Quantized with love from fp32.
* Importance Matrix calculated using [groups_merged.txt](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
* 107 chunks
* n_ctx=512
* Importance Matrix uses fp32 precision model weights, fp32.imatrix file to be added in repo
Original model README [here](https://huggingface.co/InferenceIllusionist/SorcererLM-22B) and below:
## SorcererLM-22B
<img src="https://files.catbox.moe/ya4zca.png" width="500"/>
<i>Because good things always come in threes!</i>
**SorcererLM-22B** is here, rounding out the trinity of Mistral-Small-Instruct tunes from the [Quant Cartel](https://huggingface.co/Quant-Cartel).
## Prompt Format
* Prompt Template: Mistral V2 & V3 Context / Instruct Templates
* Samplers / Advanced Instruct Template: See [Quant-Cartel/Recommended-Settings/SorcererLM-22B](https://huggingface.co/Quant-Cartel/Recommended-Settings/tree/main/SorcererLM-22B)
## Quantized Versions
* [exl2 longcal](https://huggingface.co/Quant-Cartel/SorcererLM-22B-exl2-longcal)
* [iMat GGUF](https://huggingface.co/Quant-Cartel/SorcererLM-22B-iMat-GGUF)
## Training
For starters this is a LORA tune on top of Mistral-Small-Instruct-2409 and **not** a pruned version of [SorcererLM-8x22b](https://huggingface.co/rAIfle/SorcererLM-8x22b-bf16).
Trained with a whole lot of love on 1 epoch of cleaned and deduped c2 logs. This model is 100% 'born-local', the result of roughly 27 hours and a little bit of patience on a single RTX 4080 SUPER.
As hyperparameters and dataset intentionally mirror ones used in the original Sorcerer 8x22b tune, this is considered its 'lite' counterpart aiming to provide the same bespoke conversational experience relative to its size and reduced hardware requirements.
While all three share the same Mistral-Small-Instruct base, in contrast to its sisters [Mistral-Small-NovusKyver](https://huggingface.co/Envoid/Mistral-Small-NovusKyver) and [Acolyte-22B](https://huggingface.co/rAIfle/Acolyte-22B) this release did not SLERP the resulting model with the original in a 50/50 ratio post-training. Instead, alpha was dropped when the lora was merged with full precision weights in the final step.
## Acknowledgments
* First and foremost a huge thank you my brilliant teammates [envoid](https://huggingface.co/envoid/) and [rAIfle](https://huggingface.co/rAIfle/). Special shout-out to rAIfle for critical last minute advice that got this one through the finish line
* Props to unsloth as well for helping make this local tune possible
* And of course, none of this would matter without users like you. Thank you :)
## Safety
... |
codewithRiz/whisper-tiny-100steps | codewithRiz | 2024-10-10T15:39:31Z | 93 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | automatic-speech-recognition | 2024-10-10T15:35:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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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## Training Details
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#### Preprocessing [optional]
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### Results
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#### Summary
## Model Examination [optional]
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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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<!-- 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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## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf | RichardErkhov | 2024-10-10T15:38:55Z | 9 | 0 | null | [
"gguf",
"arxiv:2402.16671",
"endpoints_compatible",
"region:us"
] | null | 2024-10-10T12:39:43Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
StructLM-7B-Mistral - GGUF
- Model creator: https://huggingface.co/TIGER-Lab/
- Original model: https://huggingface.co/TIGER-Lab/StructLM-7B-Mistral/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [StructLM-7B-Mistral.Q2_K.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q2_K.gguf) | Q2_K | 2.53GB |
| [StructLM-7B-Mistral.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [StructLM-7B-Mistral.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [StructLM-7B-Mistral.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [StructLM-7B-Mistral.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [StructLM-7B-Mistral.Q3_K.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q3_K.gguf) | Q3_K | 3.28GB |
| [StructLM-7B-Mistral.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [StructLM-7B-Mistral.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [StructLM-7B-Mistral.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [StructLM-7B-Mistral.Q4_0.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q4_0.gguf) | Q4_0 | 3.83GB |
| [StructLM-7B-Mistral.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [StructLM-7B-Mistral.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [StructLM-7B-Mistral.Q4_K.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q4_K.gguf) | Q4_K | 4.07GB |
| [StructLM-7B-Mistral.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [StructLM-7B-Mistral.Q4_1.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q4_1.gguf) | Q4_1 | 4.24GB |
| [StructLM-7B-Mistral.Q5_0.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q5_0.gguf) | Q5_0 | 4.65GB |
| [StructLM-7B-Mistral.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [StructLM-7B-Mistral.Q5_K.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q5_K.gguf) | Q5_K | 4.78GB |
| [StructLM-7B-Mistral.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [StructLM-7B-Mistral.Q5_1.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q5_1.gguf) | Q5_1 | 5.07GB |
| [StructLM-7B-Mistral.Q6_K.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q6_K.gguf) | Q6_K | 5.53GB |
| [StructLM-7B-Mistral.Q8_0.gguf](https://huggingface.co/RichardErkhov/TIGER-Lab_-_StructLM-7B-Mistral-gguf/blob/main/StructLM-7B-Mistral.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
license: mit
datasets:
- TIGER-Lab/SKGInstruct
language:
- en
---
# 🏗️ StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
Project Page: [https://tiger-ai-lab.github.io/StructLM/](https://tiger-ai-lab.github.io/StructLM/)
Paper: [https://arxiv.org/pdf/2402.16671.pdf](https://arxiv.org/pdf/2402.16671.pdf)
Code: [https://github.com/TIGER-AI-Lab/StructLM](https://github.com/TIGER-AI-Lab/StructLM)
## Introduction
StructLM, is a series of open-source large language models (LLMs) finetuned for structured knowledge grounding (SKG) tasks.
This model is trained using Mistral as the base model, instead of CodeLlama.
## Training Data
This model is trained on 🤗 [SKGInstruct-skg-only Dataset](https://huggingface.co/datasets/TIGER-Lab/SKGInstruct-skg-only). Check out the dataset card for more details.
## Training Procedure
This models is fine-tuned with Mistral-v0.2 models as base models. Each model is trained for 3 epochs, and the best checkpoint is selected.
## Evaluation
Reference our project page for evaluation results on 7B-M
## Usage
You can use the models through Huggingface's Transformers library.
Check our Github repo for the evaluation code: [https://github.com/TIGER-AI-Lab/StructLM](https://github.com/TIGER-AI-Lab/StructLM)
## Prompt Format
**For this 7B model, the prompt format is**
```
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}
{input}
{question}
### Response:
```
To see concrete examples of this linearization, you can directly reference the 🤗 [SKGInstruct Dataset](https://huggingface.co/datasets/TIGER-Lab/SKGInstruct) (coming soon).
We will provide code for linearizing this data shortly.
A few examples:
**Tabular data**
```
col : day | kilometers row 1 : tuesday | 0 row 2 : wednesday | 0 row 3 : thursday | 4 row 4 : friday | 0 row 5 : saturday | 0
```
**Knowledge triples (dart)**
```
Hawaii Five-O : notes : Episode: The Flight of the Jewels | [TABLECONTEXT] : [title] : Jeff Daniels | [TABLECONTEXT] : title : Hawaii Five-O
```
**Knowledge graph schema (grailqa)**
```
top antiquark: m.094nrqp | physics.particle_antiparticle.self_antiparticle physics.particle_family physics.particle.antiparticle physics.particle_family.subclasses physics.subatomic_particle_generation physics.particle_family.particles physics.particle common.image.appears_in_topic_gallery physics.subatomic_particle_generation.particles physics.particle.family physics.particle_family.parent_class physics.particle_antiparticle physics.particle_antiparticle.particle physics.particle.generation
```
**Example input**
```
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Use the information in the following table to solve the problem, choose between the choices if they are provided. table:
col : day | kilometers row 1 : tuesday | 0 row 2 : wednesday | 0 row 3 : thursday | 4 row 4 : friday | 0 row 5 : saturday | 0
question:
Allie kept track of how many kilometers she walked during the past 5 days. What is the range of the numbers? [/INST]
### Response:
```
## Intended Uses
These models are trained for research purposes. They are designed to be proficient in interpreting linearized structured input. Downstream uses can potentially include various applications requiring the interpretation of structured data.
## Limitations
While we've tried to build an SKG-specialized model capable of generalizing, we have shown that this is a challenging domain, and it may lack performance characteristics that allow it to be directly used in chat or other applications.
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@misc{zhuang2024structlm,
title={StructLM: Towards Building Generalist Models for Structured Knowledge Grounding},
author={Alex Zhuang and Ge Zhang and Tianyu Zheng and Xinrun Du and Junjie Wang and Weiming Ren and Stephen W. Huang and Jie Fu and Xiang Yue and Wenhu Chen},
year={2024},
eprint={2402.16671},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
symanto/mingru-shakespeare | symanto | 2024-10-10T15:36:00Z | 48 | 0 | transformers | [
"transformers",
"safetensors",
"mingru",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T15:35:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
KingNish/Reasoning-Llama-1b-v0.1 | KingNish | 2024-10-10T15:26:44Z | 166 | 24 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"reasoning",
"llama-3",
"conversational",
"en",
"dataset:KingNish/reasoning-base-20k",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-06T12:07:01Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets:
- KingNish/reasoning-base-20k
language:
- en
license: llama3.2
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
- reasoning
- llama-3
---
# Model Dexcription
It's First iteration of this model. For testing purpose its just trained on 10k rows.
It performed very well than expected. It do first reasoning and than generate response on based on it but it do like o1.
It do reasoning separately (Just like o1), no tags (like reflection).
Below is inference code.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
MAX_REASONING_TOKENS = 1024
MAX_RESPONSE_TOKENS = 512
model_name = "KingNish/Reasoning-Llama-1b-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Which is greater 9.9 or 9.11 ??"
messages = [
{"role": "user", "content": prompt}
]
# Generate reasoning
reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
# print("REASONING: " + reasoning_output)
# Generate answer
messages.append({"role": "reasoning", "content": reasoning_output})
response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("ANSWER: " + response_output)
```
- **Trained by:** [Nishith Jain](https://huggingface.co/KingNish)
- **License:** llama3.2
- **Finetuned from model :** [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
- **Dataset used :** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
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) |
AmberYifan/Mistral-7B-v0.3-gen-dpo-2k | AmberYifan | 2024-10-10T15:12:39Z | 6 | 0 | null | [
"safetensors",
"mistral",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:finetune:mistralai/Mistral-7B-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2024-10-10T08:14:39Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.3
tags:
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.3-gen-dpo-2k
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. -->
# Mistral-7B-v0.3-gen-dpo-2k
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) 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: 5e-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
JefiRyan/Llama-3.2-models | JefiRyan | 2024-10-10T15:11:51Z | 15 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:JefiRyan/Llama-3.2-3B",
"base_model:quantized:JefiRyan/Llama-3.2-3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-10T15:09:36Z | ---
base_model: JefiRyan/Llama-3.2-3B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** JefiRyan
- **License:** apache-2.0
- **Finetuned from model :** JefiRyan/Llama-3.2-3B
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)
|
sumeetrm/capitalmodel | sumeetrm | 2024-10-10T15:06:53Z | 75 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"dpo",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T15:03:56Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- dpo
---
# Uploaded model
- **Developed by:** sumeetrm
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
NeuML/ljspeech-jets-onnx | NeuML | 2024-10-10T15:00:03Z | 1,047 | 25 | txtai | [
"txtai",
"onnx",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"license:apache-2.0",
"region:us"
] | text-to-speech | 2022-11-29T12:51:31Z | ---
tags:
- audio
- text-to-speech
- onnx
inference: false
language: en
datasets:
- ljspeech
license: apache-2.0
library_name: txtai
---
# ESPnet JETS Text-to-Speech (TTS) Model for ONNX
[imdanboy/jets](https://huggingface.co/imdanboy/ljspeech_tts_train_jets_raw_phn_tacotron_g2p_en_no_space_train.total_count.ave) exported to ONNX. This model is an ONNX export using the [espnet_onnx](https://github.com/espnet/espnet_onnx) library.
## Usage with txtai
[txtai](https://github.com/neuml/txtai) has a built in Text to Speech (TTS) pipeline that makes using this model easy.
```python
import soundfile as sf
from txtai.pipeline import TextToSpeech
# Build pipeline
tts = TextToSpeech("NeuML/ljspeech-jets-onnx")
# Generate speech
speech, rate = tts("Say something here")
# Write to file
sf.write("out.wav", speech, rate)
```
## Usage with ONNX
This model can also be run directly with ONNX provided the input text is tokenized. Tokenization can be done with [ttstokenizer](https://github.com/neuml/ttstokenizer).
Note that the txtai pipeline has additional functionality such as batching large inputs together that would need to be duplicated with this method.
```python
import onnxruntime
import soundfile as sf
import yaml
from ttstokenizer import TTSTokenizer
# This example assumes the files have been downloaded locally
with open("ljspeech-jets-onnx/config.yaml", "r", encoding="utf-8") as f:
config = yaml.safe_load(f)
# Create model
model = onnxruntime.InferenceSession(
"ljspeech-jets-onnx/model.onnx",
providers=["CPUExecutionProvider"]
)
# Create tokenizer
tokenizer = TTSTokenizer(config["token"]["list"])
# Tokenize inputs
inputs = tokenizer("Say something here")
# Generate speech
outputs = model.run(None, {"text": inputs})
# Write to file
sf.write("out.wav", outputs[0], 22050)
```
## How to export
More information on how to export ESPnet models to ONNX can be [found here](https://github.com/espnet/espnet_onnx#text2speech-inference).
|
NeuML/ljspeech-vits-onnx | NeuML | 2024-10-10T14:59:05Z | 16 | 11 | txtai | [
"txtai",
"onnx",
"audio",
"text-to-speech",
"en",
"dataset:ljspeech",
"license:apache-2.0",
"region:us"
] | text-to-speech | 2022-11-29T12:54:23Z | ---
tags:
- audio
- text-to-speech
- onnx
inference: false
language: en
datasets:
- ljspeech
license: apache-2.0
library_name: txtai
---
# ESPnet VITS Text-to-Speech (TTS) Model for ONNX
[espnet/kan-bayashi_ljspeech_vits](https://huggingface.co/espnet/kan-bayashi_ljspeech_vits) exported to ONNX. This model is an ONNX export using the [espnet_onnx](https://github.com/espnet/espnet_onnx) library.
## Usage with txtai
[txtai](https://github.com/neuml/txtai) has a built in Text to Speech (TTS) pipeline that makes using this model easy.
```python
import soundfile as sf
from txtai.pipeline import TextToSpeech
# Build pipeline
tts = TextToSpeech("NeuML/ljspeech-vits-onnx")
# Generate speech
speech, rate = tts("Say something here")
# Write to file
sf.write("out.wav", speech, rate)
```
## Usage with ONNX
This model can also be run directly with ONNX provided the input text is tokenized. Tokenization can be done with [ttstokenizer](https://github.com/neuml/ttstokenizer).
Note that the txtai pipeline has additional functionality such as batching large inputs together that would need to be duplicated with this method.
```python
import onnxruntime
import soundfile as sf
import yaml
from ttstokenizer import TTSTokenizer
# This example assumes the files have been downloaded locally
with open("ljspeech-vits-onnx/config.yaml", "r", encoding="utf-8") as f:
config = yaml.safe_load(f)
# Create model
model = onnxruntime.InferenceSession(
"ljspeech-vits-onnx/model.onnx",
providers=["CPUExecutionProvider"]
)
# Create tokenizer
tokenizer = TTSTokenizer(config["token"]["list"])
# Tokenize inputs
inputs = tokenizer("Say something here")
# Generate speech
outputs = model.run(None, {"text": inputs})
# Write to file
sf.write("out.wav", outputs[0], 22050)
```
## How to export
More information on how to export ESPnet models to ONNX can be [found here](https://github.com/espnet/espnet_onnx#text2speech-inference).
|
xonic48/distilbert-base-uncased-finetuned-squad | xonic48 | 2024-10-10T14:55:41Z | 130 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-10-10T14:55:10Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 32 | 5.0019 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.19.1
|
jazzson/bert-base-chinese-finetuned-question-answering-lert-base-2-from-scratch | jazzson | 2024-10-10T14:54:32Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:hfl/chinese-lert-base",
"base_model:finetune:hfl/chinese-lert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-10-10T14:38:02Z | ---
library_name: transformers
license: apache-2.0
base_model: hfl/chinese-lert-base
tags:
- generated_from_trainer
model-index:
- name: bert-base-chinese-finetuned-question-answering-lert-base-2-from-scratch
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. -->
# bert-base-chinese-finetuned-question-answering-lert-base-2-from-scratch
This model is a fine-tuned version of [hfl/chinese-lert-base](https://huggingface.co/hfl/chinese-lert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7725
- Exact Match: 0.0455
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:------:|:-----:|:---------------:|:-----------:|
| 5.3404 | 0.5526 | 3000 | 5.2297 | 0.0063 |
| 5.0748 | 1.1052 | 6000 | 5.0921 | 0.0193 |
| 4.882 | 1.6578 | 9000 | 4.9150 | 0.0445 |
| 4.7544 | 2.2104 | 12000 | 4.8461 | 0.0339 |
| 4.6119 | 2.7629 | 15000 | 4.7725 | 0.0455 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
rana-shahroz/mistral-openorca-openplatypus | rana-shahroz | 2024-10-10T14:35:14Z | 237 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T14:28:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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LemiSt/SmolLM-135M-de | LemiSt | 2024-10-10T14:35:00Z | 955 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"text-generation",
"de",
"dataset:devngho/culturax-mini-nonshuffled",
"dataset:maxidl/FineNews-unfiltered",
"dataset:djstrong/oscar-small",
"dataset:LemiSt/gutenberg_de",
"dataset:almanach/HALvest",
"dataset:wikimedia/wikipedia",
"dataset:D4ve-R/terra-xplain-cc-de",
"base_model:HuggingFaceTB/SmolLM-135M",
"base_model:finetune:HuggingFaceTB/SmolLM-135M",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-07T14:22:20Z | ---
library_name: transformers
license: apache-2.0
language:
- de
datasets:
- devngho/culturax-mini-nonshuffled
- maxidl/FineNews-unfiltered
- djstrong/oscar-small
- LemiSt/gutenberg_de
- almanach/HALvest
- wikimedia/wikipedia
- D4ve-R/terra-xplain-cc-de
base_model:
- HuggingFaceTB/SmolLM-135M
pipeline_tag: text-generation
---
# Model Card for SmolLM-135M-de
A german version of [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M/blob/main/README.md), trained to speak German by applying CPT for about 6 billion tokens.
If you are looking for a chat model, try [this](https://huggingface.co/LemiSt/SmolLM-135M-instruct-de-merged) fine tune or the [corresponding adapter model](https://huggingface.co/LemiSt/SmolLM-135M-instruct-de).
## Model Details
### Model Description
The base model is [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M/blob/main/README.md), which I further trained on about 6 billion German-language tokens.
- **Model type:** Large Language Model (Llama architecture)
- **Language(s) (NLP):** German
- **License:** Apache 2.0
- **Finetuned from model:** [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M/blob/main/README.md)
## Uses
I mainly made this as a small experimentation model to quickly benchmark datasets etc. - since the model is so small, I am unsure about its usefulness for any real-world scenarios.
This is a base model without any chat fine tuning etc. and thus should not be used as-is. It outputs mostly correct German, which is what I tried to achieve.
If you are looking for a chat model, try [this](https://huggingface.co/LemiSt/SmolLM-135M-instruct-de) adapter.
## Bias, Risks, and Limitations
This is a very small model and will output blatantly wrong information. I have not done any further filtering on the source datasets, so it is possible that the model will generate lewd or otherwise inappropriate content. Use with care.
I would **strongly** recommend against using this model in a production setting, at least without further fine tuning and preference optimization.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# adapted from the original SmolLM repo
# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "LemiSt/SmolLM-135M-de"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Rezept für einen leckeren veganen Schokokuchen:\n", return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))
```
## Training Details
### Training Data
- [devngho/culturax-mini-nonshuffled](https://huggingface.co/datasets/devngho/culturax-mini-nonshuffled)
- [maxidl/FineNews-unfiltered](https://huggingface.co/datasets/maxidl/FineNews-unfiltered) CC-NEWS-2024-05 config, de split
- [djstrong/oscar-small](https://huggingface.co/datasets/djstrong/oscar-small) unshuffled_deduplicated_de config
- [LemiSt/gutenberg_de](https://huggingface.co/datasets/LemiSt/gutenberg_de)
- [almanach/HALvest](https://huggingface.co/datasets/almanach/HALvest) de config
- [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) 20231101.de config
- [D4ve-R/terra-xplain-cc-de](https://huggingface.co/datasets/D4ve-R/terra-xplain-cc-de)
### Training Procedure
This was trained with axolotl, using full fine tuning (no LoRA etc). I used a sequence length of 2048 with an effective batch size of 512, learning rate of 0.003 with the adamw_bnb_8bit optimizer and a cosine scheduler.
Due to an error I made in calculating the token count, I accidentally trained for nearly 2 epochs, with the learning rate not reaching its proper minimum. |
gair-prox/web-chunk-refining-lm | gair-prox | 2024-10-10T14:15:20Z | 276 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"dataset:gair-prox/RedPajama-pro",
"arxiv:2409.17115",
"base_model:gair-prox/RedPJ-ProX-0.3B",
"base_model:finetune:gair-prox/RedPJ-ProX-0.3B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T13:37:23Z | ---
license: apache-2.0
datasets:
- gair-prox/RedPajama-pro
language:
- en
base_model:
- gair-prox/RedPJ-ProX-0.3B
pipeline_tag: text-generation
library_name: transformers
tags:
- llama
- code
---
# Web-chunk-refining-lm
<p align="center">
<img src="prox-teaser.png">
</p>
[ArXiv](http://arxiv.org/abs/2409.17115) | [Code](https://github.com/GAIR-NLP/program-every-example)
**Web-chunk-refining-lm** is an adapted [0.3B-ProX](https://huggingface.co/gair-prox/RedPJ-ProX-0.3B) model, fine-tuned for chunk level refining via program generation.
<p align="center">
<img src="func_design.png">
</p>
### Citation
```
@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}
``` |
rezzzzzaaaaaa/xlm-roberta-base-finetuned-peyma-fa | rezzzzzaaaaaa | 2024-10-10T14:07:11Z | 134 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-10T13:44:14Z | ---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-peyma-fa
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. -->
# xlm-roberta-base-finetuned-peyma-fa
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0848
- F1: 0.9170
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1488 | 1.0 | 998 | 0.0672 | 0.8665 |
| 0.061 | 2.0 | 1996 | 0.0650 | 0.8881 |
| 0.041 | 3.0 | 2994 | 0.0640 | 0.8954 |
| 0.0274 | 4.0 | 3992 | 0.0728 | 0.8898 |
| 0.0207 | 5.0 | 4990 | 0.0723 | 0.8935 |
| 0.0148 | 6.0 | 5988 | 0.0745 | 0.9006 |
| 0.0084 | 7.0 | 6986 | 0.0825 | 0.9088 |
| 0.0056 | 8.0 | 7984 | 0.0824 | 0.9151 |
| 0.0033 | 9.0 | 8982 | 0.0836 | 0.9143 |
| 0.0021 | 10.0 | 9980 | 0.0848 | 0.9170 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
gair-prox/web-doc-refining-lm | gair-prox | 2024-10-10T14:07:04Z | 76,684 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"en",
"dataset:gair-prox/RedPajama-pro",
"arxiv:2409.17115",
"base_model:gair-prox/RedPJ-ProX-0.3B",
"base_model:finetune:gair-prox/RedPJ-ProX-0.3B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-10T13:19:05Z | ---
license: apache-2.0
datasets:
- gair-prox/RedPajama-pro
language:
- en
base_model:
- gair-prox/RedPJ-ProX-0.3B
pipeline_tag: text-generation
library_name: transformers
tags:
- llama
- code
---
# Web-doc-refining-lm
<p align="center">
<img src="prox-teaser.png">
</p>
[ArXiv](http://arxiv.org/abs/2409.17115) | [Code](https://github.com/GAIR-NLP/program-every-example)
**Web-doc-refining-lm** is an adapted [0.3B-ProX](https://huggingface.co/gair-prox/RedPJ-ProX-0.3B) model, fine-tuned for document level refining via program generation.
<p align="center">
<img src="func_design.png">
</p>
### Citation
```
@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}
``` |
RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf | RichardErkhov | 2024-10-10T14:06:10Z | 18 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-10T10:40:21Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
fast-apply-v0.2-qwen2.5-Coder-7B-ft - GGUF
- Model creator: https://huggingface.co/quocdat25/
- Original model: https://huggingface.co/quocdat25/fast-apply-v0.2-qwen2.5-Coder-7B-ft/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q2_K.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q2_K.gguf) | Q2_K | 2.81GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ3_XS.gguf) | IQ3_XS | 3.12GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ3_S.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ3_S.gguf) | IQ3_S | 3.26GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q3_K_S.gguf) | Q3_K_S | 3.25GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ3_M.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ3_M.gguf) | IQ3_M | 3.33GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q3_K.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q3_K.gguf) | Q3_K | 3.55GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q3_K_L.gguf) | Q3_K_L | 3.81GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ4_XS.gguf) | IQ4_XS | 3.96GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_0.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_0.gguf) | Q4_0 | 4.13GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.IQ4_NL.gguf) | IQ4_NL | 4.16GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_K_S.gguf) | Q4_K_S | 4.15GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_K.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_K.gguf) | Q4_K | 4.36GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_K_M.gguf) | Q4_K_M | 4.36GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_1.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q4_1.gguf) | Q4_1 | 4.54GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_0.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_0.gguf) | Q5_0 | 4.95GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_K_S.gguf) | Q5_K_S | 4.95GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_K.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_K.gguf) | Q5_K | 5.07GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_K_M.gguf) | Q5_K_M | 5.07GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_1.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q5_1.gguf) | Q5_1 | 5.36GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q6_K.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q6_K.gguf) | Q6_K | 5.82GB |
| [fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q8_0.gguf](https://huggingface.co/RichardErkhov/quocdat25_-_fast-apply-v0.2-qwen2.5-Coder-7B-ft-gguf/blob/main/fast-apply-v0.2-qwen2.5-Coder-7B-ft.Q8_0.gguf) | Q8_0 | 7.54GB |
Original model description:
---
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]
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|
rolf-mozilla/t5-topic | rolf-mozilla | 2024-10-10T14:00:52Z | 16 | 0 | null | [
"onnx",
"safetensors",
"t5",
"text2text-generation",
"base_model:google-t5/t5-base",
"base_model:quantized:google-t5/t5-base",
"region:us"
] | text2text-generation | 2024-08-26T23:49:15Z | ---
base_model: google-t5/t5-base
pipeline_tag: text2text-generation
--- |
gair-prox/CodeLlama-7B-ProXMath | gair-prox | 2024-10-10T13:54:22Z | 7 | 1 | null | [
"pytorch",
"llama",
"en",
"dataset:gair-prox/open-web-math-pro",
"arxiv:2409.17115",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:finetune:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-09-16T12:25:11Z | ---
license: llama2
datasets:
- gair-prox/open-web-math-pro
language:
- en
base_model:
- codellama/CodeLlama-7b-hf
---
# CodeLlama-7B-ProXMath
<p align="center">
<img src="prox-teaser.png">
</p>
[ArXiv](http://arxiv.org/abs/2409.17115) | [Data: OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) | [Code](https://github.com/GAIR-NLP/program-every-example)
**CodeLlama-7B-ProXMath** is a math-adapted language model that is continually pre-trained on [OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) (a refined version by ProX) for **10**B tokens.
## Evaluations
ProX models are evaluated on 9 common math reasoning benchmarks.
| Model | asdiv | gsm8k | mathqa | mawps | minerva_math | mmlu_stem | sat_math | svamp | tabmwp | average |
|-----------------------|:--------:|:--------:|:--------:|:--------:|:------------:|:---------:|:--------:|:--------:|:--------:|:--------:|
| CodeLlama-7B | 50.7 | 11.8 | 14.3 | 62.6 | 5.0 | 20.4 | 21.9 | 44.2 | 30.6 | 29.1 |
| CodeLlama-7B-ProXMath | **67.9** | **35.6** | **38.9** | **82.7** | **17.6** | **42.6** | **62.5** | **55.8** | **41.3** | **49.4** |
### Citation
```
@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}
```
|
gair-prox/TinyLlama-1.1B-ProXMath | gair-prox | 2024-10-10T13:54:02Z | 9 | 2 | null | [
"pytorch",
"safetensors",
"llama",
"en",
"dataset:gair-prox/open-web-math-pro",
"arxiv:2409.17115",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"license:apache-2.0",
"region:us"
] | null | 2024-09-16T12:22:00Z | ---
license: apache-2.0
datasets:
- gair-prox/open-web-math-pro
language:
- en
base_model:
- TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
---
# TinyLlama-1.1B-ProXMath
<p align="center">
<img src="prox-teaser.png">
</p>
[ArXiv](https://arxiv.org/abs/2409.17115) | [Data: OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) | [Code](https://github.com/GAIR-NLP/program-every-example)
**TinyLlama-1.1B-ProXMath** is a math-adapted TinyLlama-1.1B model that is continually pre-trained on [OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) (a refined version by ProX) for **15**B tokens.
## Evaluations
ProX models are evaluated on 9 common math reasoning benchmarks.
| Model | asdiv | gsm8k | mathqa | mawps | minerva_math | mmlu_stem | sat_math | svamp | tabmwp | average |
|-------------------------|:--------:|:-------:|:--------:|:--------:|:------------:|:---------:|:--------:|:--------:|:--------:|:--------:|
| TinyLlama-1.1B | 18.0 | 2.8 | 14.6 | 20.2 | 3.2 | 16.3 | 21.9 | 10.9 | 12.5 | 13.4 |
| TinyLlama-1.1B-ProXMath | **41.9** | **9.0** | **15.6** | **56.9** | **5.6** | **26.8** | **31.2** | **23.8** | **22.2** | **25.7** |
### Citation
```
@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}
```
|
gair-prox/RedPJ-ProX-0.7B | gair-prox | 2024-10-10T13:53:14Z | 5 | 1 | null | [
"pytorch",
"safetensors",
"llama",
"en",
"dataset:gair-prox/RedPajama-pro",
"arxiv:2409.17115",
"license:apache-2.0",
"region:us"
] | null | 2024-09-16T12:15:59Z | ---
license: apache-2.0
datasets:
- gair-prox/RedPajama-pro
language:
- en
tags:
- llama
---
# RedPJ-ProX-0.7B
<p align="center">
<img src="prox-teaser.png">
</p>
[ArXiv](http://arxiv.org/abs/2409.17115) | [Models](https://huggingface.co/gair-prox/RedPJ-ProX-0.7B) | [Data](https://huggingface.co/datasets/gair-prox/RedPajama-pro) | [Code](https://github.com/GAIR-NLP/program-every-example)
**RedPJ-ProX-0.7B** is a tiny language model. It was and trained on the [RedPajama-V2-pro](https://huggingface.co/datasets/gair-prox/RedPajama-pro) for 25B tokens.
## Evaluations
ProX models are evaluated over 10 language model benchmarks in zero-shot setting.
| | ArC-c | ARC-e | CSQA | HellaS | MMLU | OBQA | PiQA | SIQA | WinoG | SciQ | AVG |
|-----------------------|-------|-------|-------|-----------|-------|-------|-------|-------|-------|-------|------|
| raw | 26.1 | 44.3 | 29.7 | 39.1 | 27.3 | 29.2 | 66.9 | 39.0 | 52.0 | 67.4 | 42.1 |
| ours | 26.4 | 51.9 | 30.9 | 42.4 | 29.4 | 31.6 | 67.9 | 40.0 | 52.2 | 73.5 | 44.6 |
### Citation
```
@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}
``` |
OpenLLM-Ro/RoLlama3-8b-Instruct-2024-10-09 | OpenLLM-Ro | 2024-10-10T13:45:31Z | 21 | 0 | null | [
"safetensors",
"llama",
"ro",
"dataset:OpenLLM-Ro/ro_sft_alpaca",
"dataset:OpenLLM-Ro/ro_sft_alpaca_gpt4",
"dataset:OpenLLM-Ro/ro_sft_dolly",
"dataset:OpenLLM-Ro/ro_sft_selfinstruct_gpt4",
"dataset:OpenLLM-Ro/ro_sft_norobots",
"dataset:OpenLLM-Ro/ro_sft_orca",
"dataset:OpenLLM-Ro/ro_sft_camel",
"dataset:OpenLLM-Ro/ro_sft_oasst",
"dataset:OpenLLM-Ro/ro_sft_ultrachat",
"arxiv:2406.18266",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | 2024-09-23T12:49:13Z | ---
license: cc-by-nc-4.0
language:
- ro
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- OpenLLM-Ro/ro_sft_alpaca
- OpenLLM-Ro/ro_sft_alpaca_gpt4
- OpenLLM-Ro/ro_sft_dolly
- OpenLLM-Ro/ro_sft_selfinstruct_gpt4
- OpenLLM-Ro/ro_sft_norobots
- OpenLLM-Ro/ro_sft_orca
- OpenLLM-Ro/ro_sft_camel
- OpenLLM-Ro/ro_sft_oasst
- OpenLLM-Ro/ro_sft_ultrachat
model-index:
- name: OpenLLM-Ro/RoLlama3-8b-Instruct-2024-10-09
results:
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: Score
type: Score
value: 5.38
- task:
type: text-generation
dataset:
name: RoCulturaBench
type: RoCulturaBench
metrics:
- name: Score
type: Score
value: 3.81
- task:
type: text-generation
dataset:
name: Romanian_Academic_Benchmarks
type: Romanian_Academic_Benchmarks
metrics:
- name: Average accuracy
type: accuracy
value: 52.21
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: Average accuracy
type: accuracy
value: 47.94
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: Average accuracy
type: accuracy
value: 53.50
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: Average accuracy
type: accuracy
value: 66.06
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: Average accuracy
type: accuracy
value: 59.72
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: Average accuracy
type: accuracy
value: 40.16
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_truthfulqa
type: OpenLLM-Ro/ro_truthfulqa
metrics:
- name: Average accuracy
type: accuracy
value: 45.90
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: Average macro-f1
type: macro-f1
value: 95.58
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: Average macro-f1
type: macro-f1
value: 61.20
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary_finetuned
type: LaRoSeDa_binary_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 96.46
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass_finetuned
type: LaRoSeDa_multiclass_finetuned
metrics:
- name: Average macro-f1
type: macro-f1
value: 87.26
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: Average bleu
type: bleu
value: 22.92
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: Average bleu
type: bleu
value: 24.28
- task:
type: text-generation
dataset:
name: WMT_EN-RO_finetuned
type: WMT_EN-RO_finetuned
metrics:
- name: Average bleu
type: bleu
value: 27.31
- task:
type: text-generation
dataset:
name: WMT_RO-EN_finetuned
type: WMT_RO-EN_finetuned
metrics:
- name: Average bleu
type: bleu
value: 40.52
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average exact_match
type: exact_match
value: 18.89
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average f1
type: f1
value: 31.79
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average exact_match
type: exact_match
value: 50.84
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- name: Average f1
type: f1
value: 65.18
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average spearman
type: spearman
value: 77.60
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average pearson
type: pearson
value: 76.86
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average spearman
type: spearman
value: 86.70
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- name: Average pearson
type: pearson
value: 87.09
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: First turn
type: Score
value: 6.09
- name: Second turn
type: Score
value: 4.67
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: 0-shot
type: accuracy
value: 46.02
- name: 1-shot
type: accuracy
value: 47.39
- name: 3-shot
type: accuracy
value: 47.73
- name: 5-shot
type: accuracy
value: 48.24
- name: 10-shot
type: accuracy
value: 48.33
- name: 25-shot
type: accuracy
value: 49.96
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: 0-shot
type: accuracy
value: 51.19
- name: 1-shot
type: accuracy
value: 53.05
- name: 3-shot
type: accuracy
value: 54.83
- name: 5-shot
type: accuracy
value: 54.93
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: 0-shot
type: accuracy
value: 64.09
- name: 1-shot
type: accuracy
value: 66.22
- name: 3-shot
type: accuracy
value: 66.61
- name: 5-shot
type: accuracy
value: 67.32
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: 0-shot
type: accuracy
value: 59.34
- name: 1-shot
type: accuracy
value: 59.52
- name: 3-shot
type: accuracy
value: 59.61
- name: 5-shot
type: accuracy
value: 59.95
- name: 10-shot
type: accuracy
value: 60.19
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: 1-shot
type: accuracy
value: 31.31
- name: 3-shot
type: accuracy
value: 42.23
- name: 5-shot
type: accuracy
value: 46.93
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: 0-shot
type: macro-f1
value: 92.43
- name: 1-shot
type: macro-f1
value: 96.23
- name: 3-shot
type: macro-f1
value: 96.66
- name: 5-shot
type: macro-f1
value: 97.00
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: 0-shot
type: macro-f1
value: 61.47
- name: 1-shot
type: macro-f1
value: 63.77
- name: 3-shot
type: macro-f1
value: 57.12
- name: 5-shot
type: macro-f1
value: 62.43
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: 0-shot
type: bleu
value: 5.25
- name: 1-shot
type: bleu
value: 28.62
- name: 3-shot
type: bleu
value: 29.60
- name: 5-shot
type: bleu
value: 28.21
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: 0-shot
type: bleu
value: 1.95
- name: 1-shot
type: bleu
value: 24.00
- name: 3-shot
type: bleu
value: 34.87
- name: 5-shot
type: bleu
value: 36.31
- task:
type: text-generation
dataset:
name: XQuAD_EM
type: XQuAD_EM
metrics:
- name: 0-shot
type: exact_match
value: 16.97
- name: 1-shot
type: exact_match
value: 31.01
- name: 3-shot
type: exact_match
value: 13.95
- name: 5-shot
type: exact_match
value: 13.61
- task:
type: text-generation
dataset:
name: XQuAD_F1
type: XQuAD_F1
metrics:
- name: 0-shot
type: f1
value: 31.29
- name: 1-shot
type: f1
value: 42.77
- name: 3-shot
type: f1
value: 24.78
- name: 5-shot
type: f1
value: 28.30
- task:
type: text-generation
dataset:
name: STS_Spearman
type: STS_Spearman
metrics:
- name: 1-shot
type: spearman
value: 77.73
- name: 3-shot
type: spearman
value: 76.78
- name: 5-shot
type: spearman
value: 78.30
- task:
type: text-generation
dataset:
name: STS_Pearson
type: STS_Pearson
metrics:
- name: 1-shot
type: pearson
value: 77.25
- name: 3-shot
type: pearson
value: 75.83
- name: 5-shot
type: pearson
value: 77.49
---
# Model Card for Model ID
*Built with Meta Llama 3*
<!-- Provide a quick summary of what the model is/does. -->
RoLlama3 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 8B model**. Links to other models can be found at the bottom of this page.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
- **Developed by:** OpenLLM-Ro
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Language(s):** Romanian
- **License:** cc-by-nc-4.0
- **Finetuned from model:** [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- **Trained using:** [RoAlpaca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_camel), [RoOpenAssistant](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_oasst), [RoUltraChat](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_ultrachat)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
- **Paper:** https://arxiv.org/abs/2406.18266
## Intended Use
### Intended Use Cases
RoLlama3 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct-2024-10-09")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct-2024-10-09")
instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
chat = [
{"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."},
{"role": "user", "content": instruction},
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```
## Academic Benchmarks
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>ARC</center></strong></td>
<td><strong><center>MMLU</center></strong></td>
<td><strong><center>Winogrande</center></strong></td>
<td><strong><center>Hellaswag</center></strong></td>
<td><strong><center>GSM8k</center></strong></td>
<td><strong><center>TruthfulQA</center></strong></td>
</tr>
<tr>
<td>Llama-3-8B-Instruct</td><td><center>50.62</center></td><td><center>43.69</center></td><td><center>52.04</center></td><td><center>59.33</center></td><td><center>53.19</center></td><td><center><strong>43.87</strong></center></td><td><center><strong>51.59</strong></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>50.56</center></td><td><center>44.70</center></td><td><center>52.19</center></td><td><center><strong>67.23</strong></center></td><td><center>57.69</center></td><td><center>30.23</center></td><td><center>51.34</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-10-09</em></td><td><center><em><strong>52.21</strong></em></center></td><td><center><em><strong>47.94</strong></em></center></td><td><center><em><strong>53.50</strong></em></center></td><td><center><em>66.06</em></center></td><td><center><em><strong>59.72</strong></em></center></td><td><center><em>40.16</em></center></td><td><center><em>45.90</em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>49.96</center></td><td><center>46.29</center></td><td><center>53.29</center></td><td><center>65.57</center></td><td><center>58.15</center></td><td><center>34.77</center></td><td><center>41.70</center></td>
</tr>
</tbody>
</table>
## Downstream tasks
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
<td colspan="4"><center><strong>WMT</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
</tr>
<tr>
<td>Llama-3-8B-Instruct</td><td><center>95.88</center></td><td><center>56.21</center></td><td><center><strong>98.53</strong></center></td><td><center>86.19</center></td><td><center>18.88</center></td><td><center><strong>30.98</strong></center></td><td><center><strong>28.02</strong></center></td><td><center>40.28</center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-06-28</td><td><center><strong>97.52</strong></center></td><td><center><strong>67.41</strong></center></td><td><center>94.15</center></td><td><center>87.13</center></td><td><center><strong>24.01</strong></center></td><td><center>27.36</center></td><td><center>26.53</center></td><td><center>40.36</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-10-09</em></td><td><center><em>95.58</em></center></td><td><center><em>61.20</em></center></td><td><center><em>96.46</em></center></td><td><center><em><strong>87.26</strong></em></center></td><td><center><em>22.92</em></center></td><td><center><em>24.28</em></center></td><td><center><em>27.31</em></center></td><td><center><em><strong>40.52</strong></em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>97.48</center></td><td><center>54.00</center></td><td><center>-</center></td><td><center>-</center></td><td><center>22.09</center></td><td><center>23.00</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>XQuAD</strong></center></td>
<td colspan="4"><center><strong>STS</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
</tr>
<tr>
<td>Llama-3-8B-Instruct</td><td><center><strong>39.47</strong></center></td><td><center>58.67</center></td><td><center><strong>67.65</strong></center></td><td><center><strong>82.77</strong></center></td><td><center>73.04</center></td><td><center>72.36</center></td><td><center>83.49</center></td><td><center>84.06</center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>39.43</center></td><td><center><strong>59.50</strong></center></td><td><center>44.45</center></td><td><center>59.76</center></td><td><center>77.20</center></td><td><center>77.87</center></td><td><center>85.80</center></td><td><center>86.05</center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-10-09</em></td><td><center><em>18.89</em></center></td><td><center><em>31.79</em></center></td><td><center><em>50.84</em></center></td><td><center><em>65.18</em></center></td><td><center><em>77.60</em></center></td><td><center><em>76.86</em></center></td><td><center><em><strong>86.70</strong></em></center></td><td><center><em><strong>87.09</strong></em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>26.05</center></td><td><center>42.77</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>79.64</strong></center></td><td><center><strong>79.52</strong></center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>
## MT-Bench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>1st turn</center></strong></td>
<td><strong><center>2nd turn</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Llama-3-8B-Instruct</td><td><center><strong>5.96</strong></center></td><td><center>6.16</center></td><td><center><strong>5.76</strong></center></td><td><center>158/160</center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>5.15</center></td><td><center>6.03</center></td><td><center>4.28</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-10-09</em></td><td><center><em>5.38</em></center></td><td><center><em>6.09</em></center></td><td><center><em>4.67</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>5.87</center></td><td><center><strong>6.22</strong></center></td><td><center>5.49</center></td><td><center><strong>160/160</strong></center></td>
</tr>
</tbody>
</table>
## RoCulturaBench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>Llama-3-8B-Instruct</td><td><center><strong>4.62</strong></center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-2024-06-28</td><td><center>3.71</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoLlama3-8b-Instruct-2024-10-09</em></td><td><center><em>3.81</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
</tr>
<tr>
<td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>4.40</center></td><td><center><strong>100/100</strong></center></td>
</tr>
</tbody>
</table>
## RoLlama3 Model Family
| Model | Link |
|--------------------|:--------:|
|RoLlama3-8b-Instruct-2024-06-28| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28) |
|*RoLlama3-8b-Instruct-2024-10-09*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-10-09) |
|RoLlama3-8b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-DPO-2024-10-09) |
## Citation
```
@misc{masala2024vorbecstiromanecsterecipetrain,
title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions},
author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
year={2024},
eprint={2406.18266},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.18266},
}
```
<!-- **APA:**
[More Information Needed] --> |
avans06/Meta-Llama-3.1-8B-Instruct-ct2-int8_float16 | avans06 | 2024-10-10T13:44:30Z | 6 | 0 | null | [
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"ctranslate2",
"quantization",
"int8",
"float16",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] | text-generation | 2024-10-10T10:42:37Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- ctranslate2
- quantization
- int8
- float16
extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
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\ spam\n 4. Impersonating another individual without consent, authorization,\
\ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\
\ 6. Generating or facilitating false online engagement, including fake reviews\
\ and other means of fake online engagement\n4. Fail to appropriately disclose to\
\ end users any known dangers of your AI system\nPlease report any violation of\
\ this Policy, software “bug,” or other problems that could lead to a violation\
\ of this Policy through one of the following means:\n * Reporting issues with\
\ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\
\ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\
\ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\
\ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]"
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
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Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
- Reporter
- Other
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
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---
## meta-llama/Meta-Llama-3.1-8B-Instruct for CTranslate2
**The model is quantized version of the [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) with int8_float16 quantization and can be used in [CTranslate2](https://github.com/OpenNMT/CTranslate2).**
## Conversion details
The original model was converted on 2024-10 with the following command:
```
ct2-transformers-converter --model Path\To\Local\meta-llama\Meta-Llama-3.1-8B-Instruct \
--quantization int8_float16 --output_dir Meta-Llama-3.1-8B-Instruct-ct2-int8_float16
```
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Input modalities</strong>
</td>
<td><strong>Output modalities</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="3" >Llama 3.1 (text only)
</td>
<td rowspan="3" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
<td rowspan="3" >15T+
</td>
<td rowspan="3" >December 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
<tr>
<td>405B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
</table>
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** July 23, 2024.
**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
## How to use
This repository for use with [CTranslate2](https://github.com/OpenNMT/CTranslate2).
### Use with CTranslate2
This example code is obtained from [CTranslate2_transformers](https://opennmt.net/CTranslate2/guides/transformers.html#mpt) and [tokenizer AutoTokenizer](https://huggingface.co/docs/transformers/main_classes/tokenizer).
More detailed information about the `generate_batch` methon can be found at [CTranslate2_Generator.generate_batch](https://opennmt.net/CTranslate2/python/ctranslate2.Generator.html#ctranslate2.Generator.generate_batch).
```python
import ctranslate2
import transformers
model_id = "avans06/Meta-Llama-3.1-8B-Instruct-ct2-int8_float16"
model = ctranslate2.Generator(model_id, device="auto", compute_type="int8_float16")
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True)
)
input_tokens = tokenizer.convert_ids_to_tokens(input_ids)
results = model.generate_batch([input_tokens], include_prompt_in_result=False, max_length=256)
output = tokenizer.decode(results[0].sequences_ids[0])
print(output)
```
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
<table>
<tr>
<td>
</td>
<td><strong>Training Time (GPU hours)</strong>
</td>
<td><strong>Training Power Consumption (W)</strong>
</td>
<td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
<td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3.1 8B
</td>
<td>1.46M
</td>
<td>700
</td>
<td>420
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 70B
</td>
<td>7.0M
</td>
<td>700
</td>
<td>2,040
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 405B
</td>
<td>30.84M
</td>
<td>700
</td>
<td>8,930
</td>
<td>0
</td>
</tr>
<tr>
<td>Total
</td>
<td>39.3M
<td>
<ul>
</ul>
</td>
<td>11,390
</td>
<td>0
</td>
</tr>
</table>
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023.
## Benchmark scores
In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="7" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>66.7
</td>
<td>66.7
</td>
<td>79.5
</td>
<td>79.3
</td>
<td>85.2
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>36.2
</td>
<td>37.1
</td>
<td>55.0
</td>
<td>53.8
</td>
<td>61.6
</td>
</tr>
<tr>
<td>AGIEval English
</td>
<td>3-5
</td>
<td>average/acc_char
</td>
<td>47.1
</td>
<td>47.8
</td>
<td>63.0
</td>
<td>64.6
</td>
<td>71.6
</td>
</tr>
<tr>
<td>CommonSenseQA
</td>
<td>7
</td>
<td>acc_char
</td>
<td>72.6
</td>
<td>75.0
</td>
<td>83.8
</td>
<td>84.1
</td>
<td>85.8
</td>
</tr>
<tr>
<td>Winogrande
</td>
<td>5
</td>
<td>acc_char
</td>
<td>-
</td>
<td>60.5
</td>
<td>-
</td>
<td>83.3
</td>
<td>86.7
</td>
</tr>
<tr>
<td>BIG-Bench Hard (CoT)
</td>
<td>3
</td>
<td>average/em
</td>
<td>61.1
</td>
<td>64.2
</td>
<td>81.3
</td>
<td>81.6
</td>
<td>85.9
</td>
</tr>
<tr>
<td>ARC-Challenge
</td>
<td>25
</td>
<td>acc_char
</td>
<td>79.4
</td>
<td>79.7
</td>
<td>93.1
</td>
<td>92.9
</td>
<td>96.1
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki
</td>
<td>5
</td>
<td>em
</td>
<td>78.5
</td>
<td>77.6
</td>
<td>89.7
</td>
<td>89.8
</td>
<td>91.8
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD
</td>
<td>1
</td>
<td>em
</td>
<td>76.4
</td>
<td>77.0
</td>
<td>85.6
</td>
<td>81.8
</td>
<td>89.3
</td>
</tr>
<tr>
<td>QuAC (F1)
</td>
<td>1
</td>
<td>f1
</td>
<td>44.4
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>51.1
</td>
<td>53.6
</td>
</tr>
<tr>
<td>BoolQ
</td>
<td>0
</td>
<td>acc_char
</td>
<td>75.7
</td>
<td>75.0
</td>
<td>79.0
</td>
<td>79.4
</td>
<td>80.0
</td>
</tr>
<tr>
<td>DROP (F1)
</td>
<td>3
</td>
<td>f1
</td>
<td>58.4
</td>
<td>59.5
</td>
<td>79.7
</td>
<td>79.6
</td>
<td>84.8
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B Instruct</strong>
</td>
<td><strong>Llama 3.1 8B Instruct</strong>
</td>
<td><strong>Llama 3 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 405B Instruct</strong>
</td>
</tr>
<tr>
<td rowspan="4" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc
</td>
<td>68.5
</td>
<td>69.4
</td>
<td>82.0
</td>
<td>83.6
</td>
<td>87.3
</td>
</tr>
<tr>
<td>MMLU (CoT)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>65.3
</td>
<td>73.0
</td>
<td>80.9
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>micro_avg/acc_char
</td>
<td>45.5
</td>
<td>48.3
</td>
<td>63.4
</td>
<td>66.4
</td>
<td>73.3
</td>
</tr>
<tr>
<td>IFEval
</td>
<td>
</td>
<td>
</td>
<td>76.8
</td>
<td>80.4
</td>
<td>82.9
</td>
<td>87.5
</td>
<td>88.6
</td>
</tr>
<tr>
<td rowspan="2" >Reasoning
</td>
<td>ARC-C
</td>
<td>0
</td>
<td>acc
</td>
<td>82.4
</td>
<td>83.4
</td>
<td>94.4
</td>
<td>94.8
</td>
<td>96.9
</td>
</tr>
<tr>
<td>GPQA
</td>
<td>0
</td>
<td>em
</td>
<td>34.6
</td>
<td>30.4
</td>
<td>39.5
</td>
<td>46.7
</td>
<td>50.7
</td>
</tr>
<tr>
<td rowspan="4" >Code
</td>
<td>HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>60.4
</td>
<td>72.6
</td>
<td>81.7
</td>
<td>80.5
</td>
<td>89.0
</td>
</tr>
<tr>
<td>MBPP ++ base version
</td>
<td>0
</td>
<td>pass@1
</td>
<td>70.6
</td>
<td>72.8
</td>
<td>82.5
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>Multipl-E HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>50.8
</td>
<td>-
</td>
<td>65.5
</td>
<td>75.2
</td>
</tr>
<tr>
<td>Multipl-E MBPP
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>52.4
</td>
<td>-
</td>
<td>62.0
</td>
<td>65.7
</td>
</tr>
<tr>
<td rowspan="2" >Math
</td>
<td>GSM-8K (CoT)
</td>
<td>8
</td>
<td>em_maj1@1
</td>
<td>80.6
</td>
<td>84.5
</td>
<td>93.0
</td>
<td>95.1
</td>
<td>96.8
</td>
</tr>
<tr>
<td>MATH (CoT)
</td>
<td>0
</td>
<td>final_em
</td>
<td>29.1
</td>
<td>51.9
</td>
<td>51.0
</td>
<td>68.0
</td>
<td>73.8
</td>
</tr>
<tr>
<td rowspan="4" >Tool Use
</td>
<td>API-Bank
</td>
<td>0
</td>
<td>acc
</td>
<td>48.3
</td>
<td>82.6
</td>
<td>85.1
</td>
<td>90.0
</td>
<td>92.0
</td>
</tr>
<tr>
<td>BFCL
</td>
<td>0
</td>
<td>acc
</td>
<td>60.3
</td>
<td>76.1
</td>
<td>83.0
</td>
<td>84.8
</td>
<td>88.5
</td>
</tr>
<tr>
<td>Gorilla Benchmark API Bench
</td>
<td>0
</td>
<td>acc
</td>
<td>1.7
</td>
<td>8.2
</td>
<td>14.7
</td>
<td>29.7
</td>
<td>35.3
</td>
</tr>
<tr>
<td>Nexus (0-shot)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>18.1
</td>
<td>38.5
</td>
<td>47.8
</td>
<td>56.7
</td>
<td>58.7
</td>
</tr>
<tr>
<td>Multilingual
</td>
<td>Multilingual MGSM (CoT)
</td>
<td>0
</td>
<td>em
</td>
<td>-
</td>
<td>68.9
</td>
<td>-
</td>
<td>86.9
</td>
<td>91.6
</td>
</tr>
</table>
#### Multilingual benchmarks
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Language</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="9" ><strong>General</strong>
</td>
<td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
</td>
<td>Portuguese
</td>
<td>62.12
</td>
<td>80.13
</td>
<td>84.95
</td>
</tr>
<tr>
<td>Spanish
</td>
<td>62.45
</td>
<td>80.05
</td>
<td>85.08
</td>
</tr>
<tr>
<td>Italian
</td>
<td>61.63
</td>
<td>80.4
</td>
<td>85.04
</td>
</tr>
<tr>
<td>German
</td>
<td>60.59
</td>
<td>79.27
</td>
<td>84.36
</td>
</tr>
<tr>
<td>French
</td>
<td>62.34
</td>
<td>79.82
</td>
<td>84.66
</td>
</tr>
<tr>
<td>Hindi
</td>
<td>50.88
</td>
<td>74.52
</td>
<td>80.31
</td>
</tr>
<tr>
<td>Thai
</td>
<td>50.32
</td>
<td>72.95
</td>
<td>78.21
</td>
</tr>
</table>
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
### Responsible deployment
Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.1 instruct
Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone**
Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.1 systems
**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.
As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
#### New capabilities
Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
**Red teaming**
For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.
We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
**2. Child Safety**
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3. Cyber attack enablement**
Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. |
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