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null | diffusers | {"license": "apache-2.0", "library_name": "diffusers"} | DongKyung/Imagic | null | [
"diffusers",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T14:42:42+00:00 |
|
null | null | ASR+Diarization handler that works natively with Inference Endpoints.
Example payload:
```python
import base64
import requests
API_URL = "<your endpoint URL>"
filepath = "/path/to/audio"
with open(filepath, 'rb') as f:
audio_encoded = base64.b64encode(f.read()).decode("utf-8")
data = {
"inputs": audio_encoded,
"parameters": {
"batch_size": 24
}
}
resp = requests.post(API_URL, json=data, headers={"Authorization": "Bearer <your token>"})
print(resp.json())
``` | {} | unclecode/asrdiarization-handler | null | [
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T14:42:42+00:00 |
null | null | {} | massimilianowosz/Llama-3-8B-instruct-Japanese-Chef-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-05-03T14:43:19+00:00 |
|
text-generation | transformers |
# Anifu-L3-8B-64k
Anifu-L3-8B-64k is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B)
* [MaziyarPanahi/Llama-3-8B-Instruct-64k](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-64k)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B
layer_range: [0, 32]
- model: MaziyarPanahi/Llama-3-8B-Instruct-64k
layer_range: [0, 32]
merge_method: slerp
base_model: ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.4
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Darkknight6742/Anifu-L3-8B-64k"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B", "MaziyarPanahi/Llama-3-8B-Instruct-64k"], "base_model": ["ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B", "MaziyarPanahi/Llama-3-8B-Instruct-64k"]} | Darkknight6742/Anifu-L3-8B-64k | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B",
"MaziyarPanahi/Llama-3-8B-Instruct-64k",
"conversational",
"base_model:ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B",
"base_model:MaziyarPanahi/Llama-3-8B-Instruct-64k",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:44:18+00:00 |
null | transformers |
# 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]
| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.05_2_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T14:44:34+00:00 |
null | null | {} | GGital/KhaiJiaw | null | [
"region:us"
] | null | 2024-05-03T14:45:08+00:00 |
|
text-generation | transformers |
# 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:** [Meta-llama](https://huggingface.co/meta-llama/)
- **Model type:** [Llamm-03](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- **Language(s) (NLP):** en,spa,bn
- **License:** [Meta/llama-3-8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- **Finetuned from model [optional]:** [Click here](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
### 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. -->
**As same as Llama-3
### Direct Use
| {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["chemistry"]} | ar08/Llama-3-1.7B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"chemistry",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:46:03+00:00 |
null | null | {} | raidhon/coven_7b_128k_orpo_alpha | null | [
"region:us"
] | null | 2024-05-03T14:48:15+00:00 |
|
null | null | {} | sanchit-gandhi/distil-mistral-1.5B-Instruct-v0.2-cosmo-200k-freeze | null | [
"region:us"
] | null | 2024-05-03T14:48:21+00:00 |
|
feature-extraction | transformers | {} | riccorl/relik-reader-deberta-large-v3-aida | null | [
"transformers",
"pytorch",
"relik-reader",
"feature-extraction",
"custom_code",
"region:us"
] | null | 2024-05-03T14:48:34+00:00 |
|
null | null | {} | AliGhiasvand86/my-awesome-model | null | [
"pytorch",
"region:us"
] | null | 2024-05-03T14:48:55+00:00 |
|
null | null | {} | waspop/Amor | null | [
"region:us"
] | null | 2024-05-03T14:49:25+00:00 |
|
null | null | {} | ttc0000/mistral_HFTrainer_instruct02_Sample1_lora_r64_a128_optim32bit_8bitQuant | null | [
"safetensors",
"region:us"
] | null | 2024-05-03T14:49:31+00:00 |
|
null | null | {} | SZ0/Gary | null | [
"region:us"
] | null | 2024-05-03T14:50:02+00:00 |
|
text-classification | transformers |
<!-- 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. -->
# robust_llm_pythia-70m_niki-041a_imdb_random-token-1280_10-rounds_seed-3
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-70m", "model-index": [{"name": "robust_llm_pythia-70m_niki-041a_imdb_random-token-1280_10-rounds_seed-3", "results": []}]} | AlignmentResearch/robust_llm_pythia-70m_niki-041a_imdb_random-token-1280_10-rounds_seed-3 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:50:05+00:00 |
null | peft |
<!-- 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. -->
# GUE_tf_1-seqsight_32768_512_43M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3299
- F1 Score: 0.8650
- Accuracy: 0.865
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5426 | 0.83 | 200 | 0.5207 | 0.7548 | 0.755 |
| 0.4898 | 1.67 | 400 | 0.5121 | 0.7529 | 0.753 |
| 0.4795 | 2.5 | 600 | 0.5107 | 0.7487 | 0.749 |
| 0.4729 | 3.33 | 800 | 0.4987 | 0.7531 | 0.754 |
| 0.4672 | 4.17 | 1000 | 0.5107 | 0.7475 | 0.75 |
| 0.4584 | 5.0 | 1200 | 0.5061 | 0.7479 | 0.75 |
| 0.4542 | 5.83 | 1400 | 0.4856 | 0.7618 | 0.762 |
| 0.4532 | 6.67 | 1600 | 0.5012 | 0.7458 | 0.748 |
| 0.4497 | 7.5 | 1800 | 0.4812 | 0.7560 | 0.756 |
| 0.44 | 8.33 | 2000 | 0.4899 | 0.7664 | 0.767 |
| 0.4437 | 9.17 | 2200 | 0.4879 | 0.7674 | 0.768 |
| 0.4366 | 10.0 | 2400 | 0.5086 | 0.7505 | 0.753 |
| 0.4342 | 10.83 | 2600 | 0.5080 | 0.7504 | 0.754 |
| 0.4328 | 11.67 | 2800 | 0.4901 | 0.7601 | 0.762 |
| 0.4214 | 12.5 | 3000 | 0.4984 | 0.7576 | 0.759 |
| 0.4301 | 13.33 | 3200 | 0.4965 | 0.7526 | 0.754 |
| 0.4209 | 14.17 | 3400 | 0.4845 | 0.7678 | 0.768 |
| 0.419 | 15.0 | 3600 | 0.4970 | 0.7512 | 0.753 |
| 0.4128 | 15.83 | 3800 | 0.5032 | 0.7519 | 0.754 |
| 0.4134 | 16.67 | 4000 | 0.4962 | 0.7599 | 0.761 |
| 0.4069 | 17.5 | 4200 | 0.5017 | 0.7547 | 0.757 |
| 0.4046 | 18.33 | 4400 | 0.5081 | 0.7597 | 0.761 |
| 0.4047 | 19.17 | 4600 | 0.5207 | 0.7535 | 0.756 |
| 0.4058 | 20.0 | 4800 | 0.4888 | 0.7605 | 0.761 |
| 0.3997 | 20.83 | 5000 | 0.5040 | 0.7511 | 0.753 |
| 0.3948 | 21.67 | 5200 | 0.5080 | 0.7520 | 0.754 |
| 0.39 | 22.5 | 5400 | 0.5293 | 0.7544 | 0.756 |
| 0.3894 | 23.33 | 5600 | 0.5430 | 0.7407 | 0.745 |
| 0.391 | 24.17 | 5800 | 0.5250 | 0.7473 | 0.751 |
| 0.3871 | 25.0 | 6000 | 0.4991 | 0.7573 | 0.758 |
| 0.383 | 25.83 | 6200 | 0.5037 | 0.7620 | 0.763 |
| 0.3816 | 26.67 | 6400 | 0.4972 | 0.7696 | 0.77 |
| 0.3823 | 27.5 | 6600 | 0.5181 | 0.7692 | 0.77 |
| 0.3758 | 28.33 | 6800 | 0.5215 | 0.7571 | 0.758 |
| 0.3744 | 29.17 | 7000 | 0.5173 | 0.7549 | 0.756 |
| 0.3753 | 30.0 | 7200 | 0.5160 | 0.7581 | 0.759 |
| 0.3718 | 30.83 | 7400 | 0.5256 | 0.7541 | 0.756 |
| 0.3693 | 31.67 | 7600 | 0.5339 | 0.7508 | 0.752 |
| 0.3713 | 32.5 | 7800 | 0.5280 | 0.7515 | 0.753 |
| 0.3659 | 33.33 | 8000 | 0.5400 | 0.7570 | 0.759 |
| 0.3684 | 34.17 | 8200 | 0.5305 | 0.7573 | 0.759 |
| 0.3639 | 35.0 | 8400 | 0.5285 | 0.7558 | 0.757 |
| 0.3635 | 35.83 | 8600 | 0.5302 | 0.7504 | 0.752 |
| 0.3591 | 36.67 | 8800 | 0.5316 | 0.7483 | 0.75 |
| 0.3574 | 37.5 | 9000 | 0.5520 | 0.7394 | 0.743 |
| 0.36 | 38.33 | 9200 | 0.5386 | 0.7572 | 0.759 |
| 0.3564 | 39.17 | 9400 | 0.5440 | 0.7563 | 0.758 |
| 0.3586 | 40.0 | 9600 | 0.5405 | 0.7541 | 0.756 |
| 0.3562 | 40.83 | 9800 | 0.5336 | 0.7535 | 0.755 |
| 0.3582 | 41.67 | 10000 | 0.5357 | 0.7563 | 0.758 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_1-seqsight_32768_512_43M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_1-seqsight_32768_512_43M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T14:50:41+00:00 |
null | peft |
<!-- 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. -->
# GUE_tf_4-seqsight_32768_512_43M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3525
- F1 Score: 0.8409
- Accuracy: 0.841
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5652 | 1.34 | 200 | 0.5184 | 0.7476 | 0.748 |
| 0.4877 | 2.68 | 400 | 0.5041 | 0.7616 | 0.762 |
| 0.4799 | 4.03 | 600 | 0.4936 | 0.7639 | 0.764 |
| 0.4705 | 5.37 | 800 | 0.5092 | 0.7592 | 0.761 |
| 0.4644 | 6.71 | 1000 | 0.4823 | 0.7739 | 0.774 |
| 0.4586 | 8.05 | 1200 | 0.4981 | 0.7600 | 0.762 |
| 0.4561 | 9.4 | 1400 | 0.4832 | 0.7671 | 0.768 |
| 0.4535 | 10.74 | 1600 | 0.4726 | 0.7828 | 0.783 |
| 0.4457 | 12.08 | 1800 | 0.4701 | 0.7740 | 0.774 |
| 0.4456 | 13.42 | 2000 | 0.4692 | 0.7723 | 0.773 |
| 0.4359 | 14.77 | 2200 | 0.4918 | 0.7597 | 0.762 |
| 0.4351 | 16.11 | 2400 | 0.4658 | 0.7827 | 0.783 |
| 0.4278 | 17.45 | 2600 | 0.4864 | 0.7612 | 0.763 |
| 0.43 | 18.79 | 2800 | 0.4717 | 0.7740 | 0.775 |
| 0.4299 | 20.13 | 3000 | 0.4732 | 0.7739 | 0.775 |
| 0.4232 | 21.48 | 3200 | 0.4721 | 0.7731 | 0.774 |
| 0.4235 | 22.82 | 3400 | 0.4691 | 0.7828 | 0.783 |
| 0.4209 | 24.16 | 3600 | 0.4699 | 0.7792 | 0.78 |
| 0.4215 | 25.5 | 3800 | 0.4663 | 0.7866 | 0.787 |
| 0.4187 | 26.85 | 4000 | 0.4742 | 0.7740 | 0.775 |
| 0.4209 | 28.19 | 4200 | 0.4767 | 0.7686 | 0.77 |
| 0.4122 | 29.53 | 4400 | 0.4799 | 0.7709 | 0.772 |
| 0.4148 | 30.87 | 4600 | 0.4647 | 0.7844 | 0.785 |
| 0.4128 | 32.21 | 4800 | 0.4668 | 0.7825 | 0.783 |
| 0.41 | 33.56 | 5000 | 0.4730 | 0.7845 | 0.785 |
| 0.4098 | 34.9 | 5200 | 0.4762 | 0.7771 | 0.778 |
| 0.4145 | 36.24 | 5400 | 0.4719 | 0.7718 | 0.773 |
| 0.4083 | 37.58 | 5600 | 0.4733 | 0.7811 | 0.782 |
| 0.4074 | 38.93 | 5800 | 0.4723 | 0.7812 | 0.782 |
| 0.4062 | 40.27 | 6000 | 0.4799 | 0.7729 | 0.774 |
| 0.4069 | 41.61 | 6200 | 0.4714 | 0.7782 | 0.779 |
| 0.4104 | 42.95 | 6400 | 0.4786 | 0.7704 | 0.772 |
| 0.4065 | 44.3 | 6600 | 0.4687 | 0.7802 | 0.781 |
| 0.4025 | 45.64 | 6800 | 0.4757 | 0.7718 | 0.773 |
| 0.4063 | 46.98 | 7000 | 0.4797 | 0.7716 | 0.773 |
| 0.4046 | 48.32 | 7200 | 0.4751 | 0.7727 | 0.774 |
| 0.4025 | 49.66 | 7400 | 0.4780 | 0.7704 | 0.772 |
| 0.4009 | 51.01 | 7600 | 0.4685 | 0.7752 | 0.776 |
| 0.4009 | 52.35 | 7800 | 0.4640 | 0.7845 | 0.785 |
| 0.3984 | 53.69 | 8000 | 0.4695 | 0.7793 | 0.78 |
| 0.4034 | 55.03 | 8200 | 0.4808 | 0.7712 | 0.773 |
| 0.3999 | 56.38 | 8400 | 0.4738 | 0.7718 | 0.773 |
| 0.403 | 57.72 | 8600 | 0.4629 | 0.7837 | 0.784 |
| 0.3985 | 59.06 | 8800 | 0.4747 | 0.7716 | 0.773 |
| 0.3983 | 60.4 | 9000 | 0.4715 | 0.7709 | 0.772 |
| 0.3984 | 61.74 | 9200 | 0.4686 | 0.7783 | 0.779 |
| 0.3964 | 63.09 | 9400 | 0.4691 | 0.7741 | 0.775 |
| 0.4005 | 64.43 | 9600 | 0.4670 | 0.7793 | 0.78 |
| 0.3999 | 65.77 | 9800 | 0.4678 | 0.7752 | 0.776 |
| 0.3968 | 67.11 | 10000 | 0.4685 | 0.7731 | 0.774 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_4-seqsight_32768_512_43M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_32768_512_43M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T14:50:45+00:00 |
null | peft |
<!-- 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. -->
# GUE_tf_4-seqsight_32768_512_43M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3664
- F1 Score: 0.8440
- Accuracy: 0.844
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5395 | 1.34 | 200 | 0.5065 | 0.7604 | 0.761 |
| 0.473 | 2.68 | 400 | 0.4977 | 0.7742 | 0.775 |
| 0.4596 | 4.03 | 600 | 0.4805 | 0.7788 | 0.779 |
| 0.446 | 5.37 | 800 | 0.4969 | 0.7695 | 0.771 |
| 0.4349 | 6.71 | 1000 | 0.4629 | 0.7830 | 0.783 |
| 0.4259 | 8.05 | 1200 | 0.4758 | 0.7771 | 0.778 |
| 0.4221 | 9.4 | 1400 | 0.4721 | 0.7762 | 0.777 |
| 0.418 | 10.74 | 1600 | 0.4747 | 0.7750 | 0.776 |
| 0.4097 | 12.08 | 1800 | 0.4576 | 0.7920 | 0.792 |
| 0.4076 | 13.42 | 2000 | 0.4689 | 0.7717 | 0.773 |
| 0.3996 | 14.77 | 2200 | 0.4714 | 0.7811 | 0.782 |
| 0.3953 | 16.11 | 2400 | 0.4535 | 0.7869 | 0.787 |
| 0.3894 | 17.45 | 2600 | 0.4984 | 0.7625 | 0.765 |
| 0.3893 | 18.79 | 2800 | 0.4684 | 0.7783 | 0.779 |
| 0.3866 | 20.13 | 3000 | 0.4674 | 0.7831 | 0.784 |
| 0.3787 | 21.48 | 3200 | 0.4584 | 0.7877 | 0.788 |
| 0.3781 | 22.82 | 3400 | 0.4598 | 0.7927 | 0.793 |
| 0.37 | 24.16 | 3600 | 0.4506 | 0.7897 | 0.79 |
| 0.3713 | 25.5 | 3800 | 0.4447 | 0.7970 | 0.797 |
| 0.3674 | 26.85 | 4000 | 0.4572 | 0.7925 | 0.793 |
| 0.3667 | 28.19 | 4200 | 0.4565 | 0.7944 | 0.795 |
| 0.355 | 29.53 | 4400 | 0.4611 | 0.8008 | 0.801 |
| 0.3578 | 30.87 | 4600 | 0.4698 | 0.7824 | 0.784 |
| 0.3521 | 32.21 | 4800 | 0.4609 | 0.7994 | 0.8 |
| 0.3515 | 33.56 | 5000 | 0.4644 | 0.7924 | 0.793 |
| 0.3482 | 34.9 | 5200 | 0.4621 | 0.7974 | 0.798 |
| 0.3454 | 36.24 | 5400 | 0.4478 | 0.7977 | 0.798 |
| 0.3406 | 37.58 | 5600 | 0.4505 | 0.7986 | 0.799 |
| 0.3393 | 38.93 | 5800 | 0.4468 | 0.7996 | 0.8 |
| 0.3398 | 40.27 | 6000 | 0.4397 | 0.8089 | 0.809 |
| 0.3357 | 41.61 | 6200 | 0.4596 | 0.7963 | 0.797 |
| 0.3348 | 42.95 | 6400 | 0.4563 | 0.8005 | 0.801 |
| 0.3337 | 44.3 | 6600 | 0.4345 | 0.8039 | 0.804 |
| 0.3275 | 45.64 | 6800 | 0.4579 | 0.8004 | 0.801 |
| 0.3288 | 46.98 | 7000 | 0.4472 | 0.8006 | 0.801 |
| 0.3227 | 48.32 | 7200 | 0.4412 | 0.8078 | 0.808 |
| 0.3194 | 49.66 | 7400 | 0.4405 | 0.8098 | 0.81 |
| 0.3193 | 51.01 | 7600 | 0.4455 | 0.8118 | 0.812 |
| 0.3177 | 52.35 | 7800 | 0.4348 | 0.8109 | 0.811 |
| 0.3156 | 53.69 | 8000 | 0.4517 | 0.8016 | 0.802 |
| 0.3216 | 55.03 | 8200 | 0.4537 | 0.8034 | 0.804 |
| 0.3176 | 56.38 | 8400 | 0.4400 | 0.8129 | 0.813 |
| 0.3155 | 57.72 | 8600 | 0.4406 | 0.8098 | 0.81 |
| 0.3155 | 59.06 | 8800 | 0.4436 | 0.8067 | 0.807 |
| 0.3129 | 60.4 | 9000 | 0.4436 | 0.8108 | 0.811 |
| 0.3103 | 61.74 | 9200 | 0.4430 | 0.8129 | 0.813 |
| 0.3094 | 63.09 | 9400 | 0.4447 | 0.8088 | 0.809 |
| 0.3115 | 64.43 | 9600 | 0.4373 | 0.8069 | 0.807 |
| 0.3109 | 65.77 | 9800 | 0.4408 | 0.8119 | 0.812 |
| 0.3071 | 67.11 | 10000 | 0.4416 | 0.8108 | 0.811 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_4-seqsight_32768_512_43M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_32768_512_43M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T14:51:02+00:00 |
text-generation | transformers | {} | ebsmothers/test-peft | null | [
"transformers",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:51:02+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_tf_4-seqsight_32768_512_43M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5449
- F1 Score: 0.8419
- Accuracy: 0.842
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5249 | 1.34 | 200 | 0.5003 | 0.7613 | 0.762 |
| 0.4582 | 2.68 | 400 | 0.5002 | 0.7683 | 0.77 |
| 0.4361 | 4.03 | 600 | 0.4676 | 0.7870 | 0.787 |
| 0.4227 | 5.37 | 800 | 0.4979 | 0.7600 | 0.762 |
| 0.4098 | 6.71 | 1000 | 0.4522 | 0.7960 | 0.796 |
| 0.3975 | 8.05 | 1200 | 0.4612 | 0.7835 | 0.784 |
| 0.3877 | 9.4 | 1400 | 0.4564 | 0.7857 | 0.786 |
| 0.3782 | 10.74 | 1600 | 0.4664 | 0.7852 | 0.786 |
| 0.3651 | 12.08 | 1800 | 0.4446 | 0.8029 | 0.803 |
| 0.3533 | 13.42 | 2000 | 0.4927 | 0.7778 | 0.78 |
| 0.3384 | 14.77 | 2200 | 0.4619 | 0.7994 | 0.8 |
| 0.3297 | 16.11 | 2400 | 0.4501 | 0.8110 | 0.811 |
| 0.3142 | 17.45 | 2600 | 0.4830 | 0.7909 | 0.792 |
| 0.3075 | 18.79 | 2800 | 0.4490 | 0.7987 | 0.799 |
| 0.2974 | 20.13 | 3000 | 0.4462 | 0.8067 | 0.807 |
| 0.2863 | 21.48 | 3200 | 0.4345 | 0.8190 | 0.819 |
| 0.2774 | 22.82 | 3400 | 0.4409 | 0.822 | 0.822 |
| 0.2675 | 24.16 | 3600 | 0.4405 | 0.8168 | 0.817 |
| 0.2601 | 25.5 | 3800 | 0.4492 | 0.8219 | 0.822 |
| 0.2509 | 26.85 | 4000 | 0.4498 | 0.8169 | 0.817 |
| 0.2468 | 28.19 | 4200 | 0.4628 | 0.8147 | 0.815 |
| 0.2333 | 29.53 | 4400 | 0.4515 | 0.8390 | 0.839 |
| 0.2304 | 30.87 | 4600 | 0.4937 | 0.8082 | 0.809 |
| 0.2176 | 32.21 | 4800 | 0.4734 | 0.8269 | 0.827 |
| 0.2179 | 33.56 | 5000 | 0.4485 | 0.8330 | 0.833 |
| 0.2091 | 34.9 | 5200 | 0.4607 | 0.8230 | 0.823 |
| 0.2066 | 36.24 | 5400 | 0.4538 | 0.8350 | 0.835 |
| 0.1927 | 37.58 | 5600 | 0.4678 | 0.8349 | 0.835 |
| 0.1921 | 38.93 | 5800 | 0.4629 | 0.842 | 0.842 |
| 0.1926 | 40.27 | 6000 | 0.4551 | 0.8479 | 0.848 |
| 0.1822 | 41.61 | 6200 | 0.4667 | 0.8530 | 0.853 |
| 0.1803 | 42.95 | 6400 | 0.4500 | 0.8510 | 0.851 |
| 0.1806 | 44.3 | 6600 | 0.4580 | 0.8509 | 0.851 |
| 0.1754 | 45.64 | 6800 | 0.4692 | 0.8500 | 0.85 |
| 0.1735 | 46.98 | 7000 | 0.4669 | 0.852 | 0.852 |
| 0.1623 | 48.32 | 7200 | 0.4765 | 0.8489 | 0.849 |
| 0.1588 | 49.66 | 7400 | 0.4864 | 0.8529 | 0.853 |
| 0.1613 | 51.01 | 7600 | 0.4871 | 0.8480 | 0.848 |
| 0.1537 | 52.35 | 7800 | 0.4830 | 0.8549 | 0.855 |
| 0.1541 | 53.69 | 8000 | 0.4832 | 0.8490 | 0.849 |
| 0.1551 | 55.03 | 8200 | 0.4792 | 0.8580 | 0.858 |
| 0.1497 | 56.38 | 8400 | 0.4938 | 0.86 | 0.86 |
| 0.1463 | 57.72 | 8600 | 0.4925 | 0.8610 | 0.861 |
| 0.1466 | 59.06 | 8800 | 0.4842 | 0.8619 | 0.862 |
| 0.148 | 60.4 | 9000 | 0.4896 | 0.8560 | 0.856 |
| 0.1443 | 61.74 | 9200 | 0.4828 | 0.8619 | 0.862 |
| 0.1419 | 63.09 | 9400 | 0.4857 | 0.8610 | 0.861 |
| 0.1434 | 64.43 | 9600 | 0.4859 | 0.8620 | 0.862 |
| 0.1379 | 65.77 | 9800 | 0.4873 | 0.8620 | 0.862 |
| 0.1406 | 67.11 | 10000 | 0.4871 | 0.8630 | 0.863 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_4-seqsight_32768_512_43M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_32768_512_43M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T14:51:44+00:00 |
null | peft |
<!-- 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. -->
# GUE_tf_3-seqsight_32768_512_43M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5590
- F1 Score: 0.7071
- Accuracy: 0.709
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6424 | 0.93 | 200 | 0.5880 | 0.6796 | 0.68 |
| 0.6058 | 1.87 | 400 | 0.5724 | 0.6941 | 0.694 |
| 0.5961 | 2.8 | 600 | 0.5619 | 0.6998 | 0.701 |
| 0.5911 | 3.74 | 800 | 0.5639 | 0.7028 | 0.703 |
| 0.5891 | 4.67 | 1000 | 0.5618 | 0.6999 | 0.7 |
| 0.584 | 5.61 | 1200 | 0.5622 | 0.7 | 0.7 |
| 0.5803 | 6.54 | 1400 | 0.5561 | 0.7039 | 0.704 |
| 0.5807 | 7.48 | 1600 | 0.5620 | 0.7050 | 0.705 |
| 0.5772 | 8.41 | 1800 | 0.5579 | 0.7001 | 0.7 |
| 0.58 | 9.35 | 2000 | 0.5559 | 0.7091 | 0.709 |
| 0.5729 | 10.28 | 2200 | 0.5700 | 0.6928 | 0.694 |
| 0.5733 | 11.21 | 2400 | 0.5502 | 0.7209 | 0.721 |
| 0.574 | 12.15 | 2600 | 0.5446 | 0.7208 | 0.721 |
| 0.5713 | 13.08 | 2800 | 0.5433 | 0.7225 | 0.723 |
| 0.5699 | 14.02 | 3000 | 0.5481 | 0.7130 | 0.713 |
| 0.5687 | 14.95 | 3200 | 0.5477 | 0.7111 | 0.711 |
| 0.5689 | 15.89 | 3400 | 0.5481 | 0.7110 | 0.711 |
| 0.5663 | 16.82 | 3600 | 0.5499 | 0.7101 | 0.71 |
| 0.5651 | 17.76 | 3800 | 0.5483 | 0.7111 | 0.711 |
| 0.5683 | 18.69 | 4000 | 0.5518 | 0.7021 | 0.702 |
| 0.5621 | 19.63 | 4200 | 0.5400 | 0.7168 | 0.718 |
| 0.5659 | 20.56 | 4400 | 0.5438 | 0.7139 | 0.714 |
| 0.5636 | 21.5 | 4600 | 0.5618 | 0.7047 | 0.706 |
| 0.5607 | 22.43 | 4800 | 0.5446 | 0.7109 | 0.711 |
| 0.563 | 23.36 | 5000 | 0.5546 | 0.7046 | 0.705 |
| 0.5603 | 24.3 | 5200 | 0.5635 | 0.7095 | 0.711 |
| 0.5587 | 25.23 | 5400 | 0.5438 | 0.7117 | 0.712 |
| 0.5634 | 26.17 | 5600 | 0.5475 | 0.7121 | 0.712 |
| 0.5562 | 27.1 | 5800 | 0.5424 | 0.7159 | 0.716 |
| 0.5581 | 28.04 | 6000 | 0.5470 | 0.7161 | 0.716 |
| 0.5576 | 28.97 | 6200 | 0.5540 | 0.7107 | 0.711 |
| 0.5576 | 29.91 | 6400 | 0.5485 | 0.7181 | 0.718 |
| 0.5567 | 30.84 | 6600 | 0.5466 | 0.7191 | 0.719 |
| 0.557 | 31.78 | 6800 | 0.5508 | 0.7119 | 0.712 |
| 0.5539 | 32.71 | 7000 | 0.5468 | 0.7171 | 0.717 |
| 0.5608 | 33.64 | 7200 | 0.5444 | 0.7100 | 0.71 |
| 0.5512 | 34.58 | 7400 | 0.5589 | 0.7116 | 0.713 |
| 0.5578 | 35.51 | 7600 | 0.5512 | 0.7187 | 0.719 |
| 0.5569 | 36.45 | 7800 | 0.5495 | 0.7130 | 0.713 |
| 0.5562 | 37.38 | 8000 | 0.5482 | 0.7140 | 0.714 |
| 0.5522 | 38.32 | 8200 | 0.5459 | 0.7161 | 0.716 |
| 0.5539 | 39.25 | 8400 | 0.5457 | 0.7161 | 0.716 |
| 0.5536 | 40.19 | 8600 | 0.5479 | 0.7151 | 0.715 |
| 0.5542 | 41.12 | 8800 | 0.5476 | 0.7151 | 0.715 |
| 0.5548 | 42.06 | 9000 | 0.5474 | 0.7131 | 0.713 |
| 0.5555 | 42.99 | 9200 | 0.5503 | 0.7158 | 0.716 |
| 0.5533 | 43.93 | 9400 | 0.5524 | 0.7155 | 0.716 |
| 0.5524 | 44.86 | 9600 | 0.5489 | 0.7189 | 0.719 |
| 0.5567 | 45.79 | 9800 | 0.5482 | 0.7190 | 0.719 |
| 0.551 | 46.73 | 10000 | 0.5487 | 0.7190 | 0.719 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_3-seqsight_32768_512_43M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_32768_512_43M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T14:51:44+00:00 |
null | transformers |
# 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
### Training Data
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#### Preprocessing [optional]
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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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## Glossary [optional]
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| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.05_4_5e-05 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T14:51:54+00:00 |
text-generation | transformers |
# 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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## Uses
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## Bias, Risks, and Limitations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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#### Summary
## Model Examination [optional]
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[More Information Needed]
## 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).
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| {"library_name": "transformers", "tags": []} | golf2248/fyc6glu | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:52:22+00:00 |
text-generation | transformers | Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement:
https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
DPO fine tuning method using the following datasets:
- https://huggingface.co/datasets/Intel/orca_dpo_pairs
- https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo
- https://huggingface.co/datasets/unalignment/toxic-dpo-v0.2
- https://huggingface.co/datasets/M4-ai/prm_dpo_pairs_cleaned
- https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1
We are happy for anyone to try it out and give some feedback and we will have the model up on https://awanllm.com on our LLM API if it is popular.
Instruct format:
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
Quants:
FP16: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Instruct-DPO-v0.1
GGUF: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Instruct-DPO-v0.1-GGUF | {"license": "llama3"} | AwanLLM/Awanllm-Llama-3-8B-Instruct-DPO-v0.1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:53:47+00:00 |
text-generation | transformers | # flammenai/flammen15-gutenberg-DPO-v1-7B AWQ
- Model creator: [flammenai](https://huggingface.co/flammenai)
- Original model: [flammen15-gutenberg-DPO-v1-7B](https://huggingface.co/flammenai/flammen15-gutenberg-DPO-v1-7B)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/flammen15-gutenberg-DPO-v1-7B-AWQ"
system_message = "You are flammen15-gutenberg-DPO-v1-7B, incarnated as a powerful AI. You were created by flammenai."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/flammen15-gutenberg-DPO-v1-7B-AWQ | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:54:53+00:00 |
text-generation | transformers | # Locutusque/Llama-3-Orca-2.0-8B AWQ
- Model creator: [Locutusque](https://huggingface.co/Locutusque)
- Original model: [Llama-3-Orca-2.0-8B](https://huggingface.co/Locutusque/Llama-3-Orca-2.0-8B)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/Llama-3-Orca-2.0-8B-AWQ"
system_message = "You are Llama-3-Orca-2.0-8B, incarnated as a powerful AI. You were created by Locutusque."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/Llama-3-Orca-2.0-8B-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:55:54+00:00 |
null | peft |
<!-- 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. -->
# GUE_tf_3-seqsight_32768_512_43M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5497
- F1 Score: 0.7229
- Accuracy: 0.724
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6281 | 0.93 | 200 | 0.5688 | 0.6951 | 0.695 |
| 0.5949 | 1.87 | 400 | 0.5826 | 0.6703 | 0.672 |
| 0.585 | 2.8 | 600 | 0.5572 | 0.7048 | 0.705 |
| 0.5792 | 3.74 | 800 | 0.5626 | 0.6949 | 0.695 |
| 0.5762 | 4.67 | 1000 | 0.5562 | 0.7021 | 0.702 |
| 0.5695 | 5.61 | 1200 | 0.5461 | 0.7119 | 0.712 |
| 0.5649 | 6.54 | 1400 | 0.5500 | 0.7130 | 0.713 |
| 0.5631 | 7.48 | 1600 | 0.5447 | 0.7111 | 0.711 |
| 0.5608 | 8.41 | 1800 | 0.5496 | 0.7018 | 0.702 |
| 0.5639 | 9.35 | 2000 | 0.5401 | 0.7190 | 0.719 |
| 0.5537 | 10.28 | 2200 | 0.5468 | 0.7066 | 0.707 |
| 0.5519 | 11.21 | 2400 | 0.5395 | 0.7201 | 0.72 |
| 0.5524 | 12.15 | 2600 | 0.5341 | 0.7166 | 0.717 |
| 0.5481 | 13.08 | 2800 | 0.5306 | 0.7109 | 0.712 |
| 0.5482 | 14.02 | 3000 | 0.5349 | 0.7091 | 0.709 |
| 0.5444 | 14.95 | 3200 | 0.5333 | 0.7121 | 0.712 |
| 0.5442 | 15.89 | 3400 | 0.5393 | 0.7130 | 0.713 |
| 0.5419 | 16.82 | 3600 | 0.5386 | 0.7111 | 0.711 |
| 0.5389 | 17.76 | 3800 | 0.5367 | 0.7081 | 0.708 |
| 0.5403 | 18.69 | 4000 | 0.5463 | 0.7125 | 0.713 |
| 0.535 | 19.63 | 4200 | 0.5358 | 0.7188 | 0.719 |
| 0.536 | 20.56 | 4400 | 0.5356 | 0.7230 | 0.723 |
| 0.5325 | 21.5 | 4600 | 0.5593 | 0.6884 | 0.691 |
| 0.5311 | 22.43 | 4800 | 0.5377 | 0.7141 | 0.714 |
| 0.532 | 23.36 | 5000 | 0.5556 | 0.7030 | 0.704 |
| 0.5294 | 24.3 | 5200 | 0.5668 | 0.6834 | 0.688 |
| 0.5263 | 25.23 | 5400 | 0.5383 | 0.7070 | 0.707 |
| 0.53 | 26.17 | 5600 | 0.5423 | 0.7090 | 0.709 |
| 0.5225 | 27.1 | 5800 | 0.5405 | 0.7069 | 0.707 |
| 0.5252 | 28.04 | 6000 | 0.5461 | 0.7118 | 0.712 |
| 0.5229 | 28.97 | 6200 | 0.5614 | 0.6913 | 0.693 |
| 0.5242 | 29.91 | 6400 | 0.5449 | 0.708 | 0.708 |
| 0.5212 | 30.84 | 6600 | 0.5479 | 0.7129 | 0.713 |
| 0.5196 | 31.78 | 6800 | 0.5572 | 0.7041 | 0.705 |
| 0.5169 | 32.71 | 7000 | 0.5556 | 0.7032 | 0.704 |
| 0.5224 | 33.64 | 7200 | 0.5525 | 0.7023 | 0.703 |
| 0.5148 | 34.58 | 7400 | 0.5718 | 0.6824 | 0.686 |
| 0.5208 | 35.51 | 7600 | 0.5579 | 0.6976 | 0.699 |
| 0.5163 | 36.45 | 7800 | 0.5610 | 0.7075 | 0.708 |
| 0.5177 | 37.38 | 8000 | 0.5560 | 0.7061 | 0.707 |
| 0.5112 | 38.32 | 8200 | 0.5569 | 0.7116 | 0.712 |
| 0.5159 | 39.25 | 8400 | 0.5547 | 0.7156 | 0.716 |
| 0.5124 | 40.19 | 8600 | 0.5570 | 0.7094 | 0.71 |
| 0.5146 | 41.12 | 8800 | 0.5509 | 0.7116 | 0.712 |
| 0.5156 | 42.06 | 9000 | 0.5519 | 0.7086 | 0.709 |
| 0.5127 | 42.99 | 9200 | 0.5603 | 0.6957 | 0.697 |
| 0.5122 | 43.93 | 9400 | 0.5620 | 0.6904 | 0.692 |
| 0.5124 | 44.86 | 9600 | 0.5578 | 0.7041 | 0.705 |
| 0.5137 | 45.79 | 9800 | 0.5581 | 0.7001 | 0.701 |
| 0.5091 | 46.73 | 10000 | 0.5590 | 0.7021 | 0.703 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_3-seqsight_32768_512_43M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_32768_512_43M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T14:55:54+00:00 |
null | null | {} | bighands23/distilbert-base-uncased-finetuned-squad | null | [
"region:us"
] | null | 2024-05-03T14:56:52+00:00 |
|
null | transformers | {} | enchatted/llama-3-8b-oscar-2301-el-finetunned | null | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:56:57+00:00 |
|
text-generation | transformers |
# 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. -->
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[More Information Needed]
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| {"library_name": "transformers", "tags": []} | golf2248/e8renp4 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:56:59+00:00 |
null | null | {} | Nour0707/mistral_7b_222_merged-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-05-03T14:59:06+00:00 |
|
null | transformers |
# 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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| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.05_4_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T14:59:09+00:00 |
null | null | {} | mayukhbis/llama3-fine-tuned-1e-gguf | null | [
"region:us"
] | null | 2024-05-03T14:59:13+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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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 -->
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| {"library_name": "transformers", "tags": []} | ar08/llama3-715m | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:59:24+00:00 |
text-generation | transformers |
<!-- 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. -->
# rloo_zephyr_vllm
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) 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: 3e-06
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b-deduped", "model-index": [{"name": "rloo_zephyr_vllm", "results": []}]} | vwxyzjn/rloo_zephyr_vllm | null | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:EleutherAI/pythia-1b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T14:59:27+00:00 |
null | peft |
<!-- 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. -->
# GUE_tf_3-seqsight_32768_512_43M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5390
- F1 Score: 0.7352
- Accuracy: 0.737
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6219 | 0.93 | 200 | 0.5615 | 0.7143 | 0.715 |
| 0.5901 | 1.87 | 400 | 0.5724 | 0.6862 | 0.687 |
| 0.5791 | 2.8 | 600 | 0.5548 | 0.7050 | 0.705 |
| 0.5704 | 3.74 | 800 | 0.5660 | 0.7032 | 0.704 |
| 0.5659 | 4.67 | 1000 | 0.5491 | 0.7071 | 0.707 |
| 0.5563 | 5.61 | 1200 | 0.5409 | 0.7079 | 0.708 |
| 0.5516 | 6.54 | 1400 | 0.5450 | 0.7081 | 0.708 |
| 0.5471 | 7.48 | 1600 | 0.5324 | 0.722 | 0.722 |
| 0.5434 | 8.41 | 1800 | 0.5451 | 0.7050 | 0.705 |
| 0.5442 | 9.35 | 2000 | 0.5373 | 0.7082 | 0.709 |
| 0.5314 | 10.28 | 2200 | 0.5364 | 0.7180 | 0.718 |
| 0.5294 | 11.21 | 2400 | 0.5513 | 0.7211 | 0.721 |
| 0.5272 | 12.15 | 2600 | 0.5450 | 0.7078 | 0.709 |
| 0.5199 | 13.08 | 2800 | 0.5316 | 0.7111 | 0.714 |
| 0.5178 | 14.02 | 3000 | 0.5374 | 0.7060 | 0.706 |
| 0.5136 | 14.95 | 3200 | 0.5289 | 0.7191 | 0.719 |
| 0.5084 | 15.89 | 3400 | 0.5419 | 0.7151 | 0.715 |
| 0.5067 | 16.82 | 3600 | 0.5432 | 0.7286 | 0.729 |
| 0.5013 | 17.76 | 3800 | 0.5421 | 0.7167 | 0.717 |
| 0.4986 | 18.69 | 4000 | 0.5601 | 0.7081 | 0.709 |
| 0.4906 | 19.63 | 4200 | 0.5510 | 0.7041 | 0.704 |
| 0.4867 | 20.56 | 4400 | 0.5497 | 0.7131 | 0.713 |
| 0.4837 | 21.5 | 4600 | 0.6035 | 0.6896 | 0.692 |
| 0.4767 | 22.43 | 4800 | 0.5738 | 0.7091 | 0.709 |
| 0.4769 | 23.36 | 5000 | 0.5860 | 0.7065 | 0.707 |
| 0.4707 | 24.3 | 5200 | 0.5907 | 0.6815 | 0.685 |
| 0.4651 | 25.23 | 5400 | 0.5700 | 0.7000 | 0.7 |
| 0.4667 | 26.17 | 5600 | 0.5695 | 0.7011 | 0.701 |
| 0.4565 | 27.1 | 5800 | 0.5968 | 0.7100 | 0.71 |
| 0.4563 | 28.04 | 6000 | 0.5916 | 0.7038 | 0.704 |
| 0.4521 | 28.97 | 6200 | 0.5932 | 0.6945 | 0.695 |
| 0.4511 | 29.91 | 6400 | 0.5748 | 0.7040 | 0.704 |
| 0.446 | 30.84 | 6600 | 0.5834 | 0.7200 | 0.72 |
| 0.4417 | 31.78 | 6800 | 0.6001 | 0.7077 | 0.708 |
| 0.4397 | 32.71 | 7000 | 0.5991 | 0.7015 | 0.702 |
| 0.4423 | 33.64 | 7200 | 0.6089 | 0.6984 | 0.699 |
| 0.4296 | 34.58 | 7400 | 0.6253 | 0.6890 | 0.691 |
| 0.4363 | 35.51 | 7600 | 0.6237 | 0.6934 | 0.695 |
| 0.4296 | 36.45 | 7800 | 0.6185 | 0.6988 | 0.699 |
| 0.4321 | 37.38 | 8000 | 0.6195 | 0.6982 | 0.699 |
| 0.423 | 38.32 | 8200 | 0.6266 | 0.7006 | 0.701 |
| 0.4243 | 39.25 | 8400 | 0.6307 | 0.6997 | 0.7 |
| 0.4201 | 40.19 | 8600 | 0.6291 | 0.6941 | 0.695 |
| 0.4204 | 41.12 | 8800 | 0.6387 | 0.6984 | 0.699 |
| 0.4233 | 42.06 | 9000 | 0.6235 | 0.6913 | 0.692 |
| 0.4202 | 42.99 | 9200 | 0.6303 | 0.6957 | 0.697 |
| 0.4168 | 43.93 | 9400 | 0.6351 | 0.6938 | 0.695 |
| 0.4173 | 44.86 | 9600 | 0.6347 | 0.6950 | 0.696 |
| 0.4161 | 45.79 | 9800 | 0.6304 | 0.6944 | 0.695 |
| 0.41 | 46.73 | 10000 | 0.6344 | 0.6922 | 0.693 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_3-seqsight_32768_512_43M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_32768_512_43M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T14:59:33+00:00 |
null | null | {} | BilelDJ/clip-hugging-face-finetuned | null | [
"region:us"
] | null | 2024-05-03T14:59:37+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_tf_2-seqsight_32768_512_43M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4635
- F1 Score: 0.7860
- Accuracy: 0.786
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.594 | 1.34 | 200 | 0.5423 | 0.7349 | 0.736 |
| 0.5384 | 2.68 | 400 | 0.5357 | 0.7269 | 0.727 |
| 0.5284 | 4.03 | 600 | 0.5253 | 0.7360 | 0.736 |
| 0.5233 | 5.37 | 800 | 0.5274 | 0.7356 | 0.736 |
| 0.5197 | 6.71 | 1000 | 0.5153 | 0.7520 | 0.752 |
| 0.517 | 8.05 | 1200 | 0.5213 | 0.7474 | 0.748 |
| 0.5116 | 9.4 | 1400 | 0.5085 | 0.7528 | 0.753 |
| 0.5094 | 10.74 | 1600 | 0.5075 | 0.7489 | 0.749 |
| 0.5088 | 12.08 | 1800 | 0.5199 | 0.7482 | 0.749 |
| 0.5071 | 13.42 | 2000 | 0.5079 | 0.7510 | 0.751 |
| 0.5052 | 14.77 | 2200 | 0.5027 | 0.7479 | 0.748 |
| 0.4987 | 16.11 | 2400 | 0.5077 | 0.7490 | 0.749 |
| 0.5038 | 17.45 | 2600 | 0.5009 | 0.7539 | 0.754 |
| 0.4987 | 18.79 | 2800 | 0.5037 | 0.7490 | 0.749 |
| 0.495 | 20.13 | 3000 | 0.5025 | 0.7500 | 0.75 |
| 0.4972 | 21.48 | 3200 | 0.5127 | 0.7596 | 0.76 |
| 0.4962 | 22.82 | 3400 | 0.5022 | 0.75 | 0.75 |
| 0.492 | 24.16 | 3600 | 0.4972 | 0.7539 | 0.754 |
| 0.4885 | 25.5 | 3800 | 0.4980 | 0.7498 | 0.75 |
| 0.494 | 26.85 | 4000 | 0.4983 | 0.7499 | 0.75 |
| 0.4896 | 28.19 | 4200 | 0.4968 | 0.7518 | 0.752 |
| 0.4879 | 29.53 | 4400 | 0.5084 | 0.7566 | 0.757 |
| 0.4862 | 30.87 | 4600 | 0.5038 | 0.7600 | 0.76 |
| 0.4885 | 32.21 | 4800 | 0.4983 | 0.7549 | 0.755 |
| 0.4875 | 33.56 | 5000 | 0.5069 | 0.7585 | 0.759 |
| 0.4891 | 34.9 | 5200 | 0.4988 | 0.7530 | 0.753 |
| 0.482 | 36.24 | 5400 | 0.4966 | 0.7570 | 0.757 |
| 0.4855 | 37.58 | 5600 | 0.4969 | 0.7540 | 0.754 |
| 0.482 | 38.93 | 5800 | 0.4970 | 0.7489 | 0.749 |
| 0.4815 | 40.27 | 6000 | 0.4939 | 0.7489 | 0.749 |
| 0.4817 | 41.61 | 6200 | 0.4957 | 0.7450 | 0.745 |
| 0.4833 | 42.95 | 6400 | 0.4976 | 0.7530 | 0.753 |
| 0.4792 | 44.3 | 6600 | 0.4988 | 0.7540 | 0.754 |
| 0.4832 | 45.64 | 6800 | 0.4975 | 0.7550 | 0.755 |
| 0.4797 | 46.98 | 7000 | 0.4971 | 0.7510 | 0.751 |
| 0.4802 | 48.32 | 7200 | 0.4996 | 0.752 | 0.752 |
| 0.4787 | 49.66 | 7400 | 0.4962 | 0.7530 | 0.753 |
| 0.4787 | 51.01 | 7600 | 0.4968 | 0.7540 | 0.754 |
| 0.4759 | 52.35 | 7800 | 0.4963 | 0.7500 | 0.75 |
| 0.4789 | 53.69 | 8000 | 0.4952 | 0.7469 | 0.747 |
| 0.4764 | 55.03 | 8200 | 0.4966 | 0.7530 | 0.753 |
| 0.4788 | 56.38 | 8400 | 0.4981 | 0.7580 | 0.758 |
| 0.4774 | 57.72 | 8600 | 0.4964 | 0.7520 | 0.752 |
| 0.4734 | 59.06 | 8800 | 0.4972 | 0.7530 | 0.753 |
| 0.4753 | 60.4 | 9000 | 0.4982 | 0.7560 | 0.756 |
| 0.4777 | 61.74 | 9200 | 0.4955 | 0.756 | 0.756 |
| 0.4783 | 63.09 | 9400 | 0.4959 | 0.7570 | 0.757 |
| 0.4743 | 64.43 | 9600 | 0.4957 | 0.7540 | 0.754 |
| 0.4795 | 65.77 | 9800 | 0.4971 | 0.7540 | 0.754 |
| 0.4749 | 67.11 | 10000 | 0.4960 | 0.754 | 0.754 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_2-seqsight_32768_512_43M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_32768_512_43M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T14:59:38+00:00 |
null | peft |
<!-- 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. -->
# GUE_tf_2-seqsight_32768_512_43M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4626
- F1 Score: 0.7890
- Accuracy: 0.789
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5751 | 1.34 | 200 | 0.5271 | 0.7484 | 0.749 |
| 0.5281 | 2.68 | 400 | 0.5243 | 0.7467 | 0.747 |
| 0.5171 | 4.03 | 600 | 0.5162 | 0.7439 | 0.744 |
| 0.5097 | 5.37 | 800 | 0.5136 | 0.7549 | 0.755 |
| 0.5055 | 6.71 | 1000 | 0.5194 | 0.7512 | 0.752 |
| 0.4997 | 8.05 | 1200 | 0.5015 | 0.7509 | 0.751 |
| 0.4922 | 9.4 | 1400 | 0.5024 | 0.7560 | 0.756 |
| 0.4886 | 10.74 | 1600 | 0.5015 | 0.7520 | 0.752 |
| 0.4882 | 12.08 | 1800 | 0.5144 | 0.7531 | 0.754 |
| 0.4815 | 13.42 | 2000 | 0.5042 | 0.7599 | 0.76 |
| 0.4818 | 14.77 | 2200 | 0.5019 | 0.7563 | 0.757 |
| 0.473 | 16.11 | 2400 | 0.5059 | 0.7570 | 0.757 |
| 0.4768 | 17.45 | 2600 | 0.4957 | 0.7639 | 0.764 |
| 0.4711 | 18.79 | 2800 | 0.5030 | 0.7637 | 0.764 |
| 0.4636 | 20.13 | 3000 | 0.5009 | 0.7679 | 0.768 |
| 0.4655 | 21.48 | 3200 | 0.5263 | 0.7501 | 0.752 |
| 0.4644 | 22.82 | 3400 | 0.5047 | 0.7608 | 0.761 |
| 0.4559 | 24.16 | 3600 | 0.4992 | 0.7618 | 0.762 |
| 0.4534 | 25.5 | 3800 | 0.5043 | 0.7608 | 0.761 |
| 0.4565 | 26.85 | 4000 | 0.4970 | 0.7640 | 0.764 |
| 0.4508 | 28.19 | 4200 | 0.5071 | 0.7624 | 0.763 |
| 0.4493 | 29.53 | 4400 | 0.5147 | 0.7642 | 0.765 |
| 0.4444 | 30.87 | 4600 | 0.5106 | 0.7583 | 0.759 |
| 0.4453 | 32.21 | 4800 | 0.5107 | 0.7586 | 0.759 |
| 0.4446 | 33.56 | 5000 | 0.5167 | 0.7614 | 0.762 |
| 0.4455 | 34.9 | 5200 | 0.5095 | 0.7535 | 0.754 |
| 0.4373 | 36.24 | 5400 | 0.5012 | 0.7590 | 0.759 |
| 0.4395 | 37.58 | 5600 | 0.5026 | 0.7478 | 0.748 |
| 0.4324 | 38.93 | 5800 | 0.5023 | 0.7590 | 0.759 |
| 0.4336 | 40.27 | 6000 | 0.4963 | 0.7510 | 0.751 |
| 0.4318 | 41.61 | 6200 | 0.5013 | 0.7559 | 0.756 |
| 0.4301 | 42.95 | 6400 | 0.5128 | 0.7493 | 0.75 |
| 0.4272 | 44.3 | 6600 | 0.5120 | 0.7537 | 0.754 |
| 0.4316 | 45.64 | 6800 | 0.5206 | 0.7540 | 0.755 |
| 0.4264 | 46.98 | 7000 | 0.5138 | 0.7538 | 0.754 |
| 0.4242 | 48.32 | 7200 | 0.5163 | 0.7551 | 0.756 |
| 0.423 | 49.66 | 7400 | 0.5117 | 0.7506 | 0.751 |
| 0.4239 | 51.01 | 7600 | 0.5220 | 0.7425 | 0.744 |
| 0.4193 | 52.35 | 7800 | 0.5163 | 0.7517 | 0.752 |
| 0.4226 | 53.69 | 8000 | 0.5121 | 0.7548 | 0.755 |
| 0.419 | 55.03 | 8200 | 0.5148 | 0.7504 | 0.751 |
| 0.4201 | 56.38 | 8400 | 0.5143 | 0.7504 | 0.751 |
| 0.4197 | 57.72 | 8600 | 0.5131 | 0.7535 | 0.754 |
| 0.4163 | 59.06 | 8800 | 0.5112 | 0.7495 | 0.75 |
| 0.4132 | 60.4 | 9000 | 0.5188 | 0.7485 | 0.749 |
| 0.4182 | 61.74 | 9200 | 0.5114 | 0.7516 | 0.752 |
| 0.4165 | 63.09 | 9400 | 0.5168 | 0.7493 | 0.75 |
| 0.4103 | 64.43 | 9600 | 0.5129 | 0.7567 | 0.757 |
| 0.4171 | 65.77 | 9800 | 0.5183 | 0.7483 | 0.749 |
| 0.4116 | 67.11 | 10000 | 0.5155 | 0.7525 | 0.753 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_2-seqsight_32768_512_43M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_32768_512_43M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T15:00:14+00:00 |
text-generation | transformers | {} | vanisus/abiturientSSTU_02 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T15:00:16+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_tf_2-seqsight_32768_512_43M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4622
- F1 Score: 0.7959
- Accuracy: 0.796
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5617 | 1.34 | 200 | 0.5167 | 0.7500 | 0.751 |
| 0.5206 | 2.68 | 400 | 0.5242 | 0.7367 | 0.738 |
| 0.5069 | 4.03 | 600 | 0.5101 | 0.7456 | 0.746 |
| 0.4965 | 5.37 | 800 | 0.5066 | 0.7537 | 0.754 |
| 0.4893 | 6.71 | 1000 | 0.5088 | 0.7533 | 0.754 |
| 0.4816 | 8.05 | 1200 | 0.4897 | 0.7556 | 0.756 |
| 0.4721 | 9.4 | 1400 | 0.5046 | 0.7609 | 0.761 |
| 0.4629 | 10.74 | 1600 | 0.4977 | 0.7720 | 0.772 |
| 0.4619 | 12.08 | 1800 | 0.4909 | 0.7620 | 0.762 |
| 0.4515 | 13.42 | 2000 | 0.5238 | 0.7467 | 0.748 |
| 0.447 | 14.77 | 2200 | 0.5081 | 0.7597 | 0.76 |
| 0.4363 | 16.11 | 2400 | 0.5179 | 0.7600 | 0.76 |
| 0.4342 | 17.45 | 2600 | 0.5182 | 0.7510 | 0.751 |
| 0.4217 | 18.79 | 2800 | 0.5406 | 0.7378 | 0.74 |
| 0.4136 | 20.13 | 3000 | 0.5344 | 0.7592 | 0.76 |
| 0.4089 | 21.48 | 3200 | 0.5592 | 0.7513 | 0.754 |
| 0.4026 | 22.82 | 3400 | 0.5251 | 0.7455 | 0.746 |
| 0.3905 | 24.16 | 3600 | 0.5552 | 0.7475 | 0.748 |
| 0.3842 | 25.5 | 3800 | 0.5535 | 0.7528 | 0.754 |
| 0.379 | 26.85 | 4000 | 0.5383 | 0.7499 | 0.75 |
| 0.3731 | 28.19 | 4200 | 0.5806 | 0.7401 | 0.742 |
| 0.3637 | 29.53 | 4400 | 0.5965 | 0.7487 | 0.75 |
| 0.3579 | 30.87 | 4600 | 0.5704 | 0.7394 | 0.74 |
| 0.3512 | 32.21 | 4800 | 0.6344 | 0.7407 | 0.743 |
| 0.3492 | 33.56 | 5000 | 0.6245 | 0.7389 | 0.74 |
| 0.341 | 34.9 | 5200 | 0.6164 | 0.7378 | 0.739 |
| 0.3312 | 36.24 | 5400 | 0.5966 | 0.7425 | 0.743 |
| 0.3296 | 37.58 | 5600 | 0.6205 | 0.7388 | 0.739 |
| 0.3182 | 38.93 | 5800 | 0.6105 | 0.7376 | 0.738 |
| 0.3165 | 40.27 | 6000 | 0.6069 | 0.736 | 0.736 |
| 0.3096 | 41.61 | 6200 | 0.6144 | 0.7455 | 0.746 |
| 0.309 | 42.95 | 6400 | 0.6497 | 0.7355 | 0.736 |
| 0.301 | 44.3 | 6600 | 0.6857 | 0.7393 | 0.74 |
| 0.3 | 45.64 | 6800 | 0.6950 | 0.7285 | 0.73 |
| 0.3012 | 46.98 | 7000 | 0.6517 | 0.7427 | 0.743 |
| 0.2952 | 48.32 | 7200 | 0.6615 | 0.7406 | 0.741 |
| 0.2885 | 49.66 | 7400 | 0.6701 | 0.7332 | 0.734 |
| 0.2802 | 51.01 | 7600 | 0.6974 | 0.7262 | 0.727 |
| 0.2763 | 52.35 | 7800 | 0.6962 | 0.7376 | 0.738 |
| 0.279 | 53.69 | 8000 | 0.6812 | 0.7374 | 0.738 |
| 0.2739 | 55.03 | 8200 | 0.6958 | 0.7351 | 0.736 |
| 0.2743 | 56.38 | 8400 | 0.7221 | 0.7278 | 0.729 |
| 0.2674 | 57.72 | 8600 | 0.7086 | 0.7356 | 0.736 |
| 0.2677 | 59.06 | 8800 | 0.7129 | 0.7343 | 0.735 |
| 0.2646 | 60.4 | 9000 | 0.7246 | 0.7305 | 0.731 |
| 0.2654 | 61.74 | 9200 | 0.7072 | 0.7262 | 0.727 |
| 0.2601 | 63.09 | 9400 | 0.7179 | 0.7312 | 0.732 |
| 0.258 | 64.43 | 9600 | 0.7255 | 0.7323 | 0.733 |
| 0.2621 | 65.77 | 9800 | 0.7326 | 0.7280 | 0.729 |
| 0.2603 | 67.11 | 10000 | 0.7294 | 0.7292 | 0.73 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_tf_2-seqsight_32768_512_43M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_32768_512_43M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T15:00:22+00:00 |
text-classification | transformers |
# Model Card for Model ID
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## 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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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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[More Information Needed] | {"library_name": "transformers", "tags": []} | quangtqv/tool_learning_cross_encoder_v3 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:00:22+00:00 |
null | peft |
<!-- 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. -->
# GUE_virus_covid-seqsight_32768_512_43M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6354
- F1 Score: 0.3716
- Accuracy: 0.3803
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 2.1856 | 0.35 | 200 | 2.1862 | 0.0584 | 0.1231 |
| 2.1827 | 0.7 | 400 | 2.1814 | 0.0880 | 0.1368 |
| 2.1749 | 1.05 | 600 | 2.1695 | 0.1360 | 0.1556 |
| 2.1678 | 1.4 | 800 | 2.1620 | 0.1018 | 0.1578 |
| 2.1597 | 1.75 | 1000 | 2.1532 | 0.1245 | 0.1743 |
| 2.1497 | 2.09 | 1200 | 2.1463 | 0.1223 | 0.1768 |
| 2.1336 | 2.44 | 1400 | 2.1075 | 0.1712 | 0.1947 |
| 2.1006 | 2.79 | 1600 | 2.0510 | 0.1868 | 0.2228 |
| 2.0648 | 3.14 | 1800 | 2.0248 | 0.1955 | 0.2269 |
| 2.0301 | 3.49 | 2000 | 1.9859 | 0.2348 | 0.2523 |
| 2.0098 | 3.84 | 2200 | 1.9554 | 0.2532 | 0.2677 |
| 1.9882 | 4.19 | 2400 | 1.9165 | 0.2718 | 0.2842 |
| 1.9604 | 4.54 | 2600 | 1.8834 | 0.2781 | 0.2914 |
| 1.942 | 4.89 | 2800 | 1.8575 | 0.2887 | 0.3025 |
| 1.9183 | 5.24 | 3000 | 1.8389 | 0.2878 | 0.3082 |
| 1.9027 | 5.58 | 3200 | 1.8136 | 0.3004 | 0.3196 |
| 1.8868 | 5.93 | 3400 | 1.8177 | 0.2947 | 0.3131 |
| 1.8806 | 6.28 | 3600 | 1.7884 | 0.3190 | 0.3344 |
| 1.8632 | 6.63 | 3800 | 1.7800 | 0.3097 | 0.3299 |
| 1.8551 | 6.98 | 4000 | 1.7610 | 0.3201 | 0.3394 |
| 1.846 | 7.33 | 4200 | 1.7507 | 0.3170 | 0.3379 |
| 1.8396 | 7.68 | 4400 | 1.7363 | 0.3258 | 0.3391 |
| 1.8348 | 8.03 | 4600 | 1.7578 | 0.3086 | 0.3272 |
| 1.8203 | 8.38 | 4800 | 1.7280 | 0.3298 | 0.3478 |
| 1.8233 | 8.73 | 5000 | 1.7161 | 0.3312 | 0.3516 |
| 1.8117 | 9.08 | 5200 | 1.7093 | 0.3323 | 0.3429 |
| 1.8013 | 9.42 | 5400 | 1.6968 | 0.3377 | 0.3573 |
| 1.7983 | 9.77 | 5600 | 1.6937 | 0.3354 | 0.3536 |
| 1.796 | 10.12 | 5800 | 1.6863 | 0.3478 | 0.3589 |
| 1.7942 | 10.47 | 6000 | 1.6781 | 0.3521 | 0.3644 |
| 1.7879 | 10.82 | 6200 | 1.6785 | 0.3433 | 0.3540 |
| 1.7773 | 11.17 | 6400 | 1.6732 | 0.3494 | 0.3601 |
| 1.7728 | 11.52 | 6600 | 1.6701 | 0.3511 | 0.3631 |
| 1.776 | 11.87 | 6800 | 1.6609 | 0.3559 | 0.3686 |
| 1.7674 | 12.22 | 7000 | 1.6534 | 0.3625 | 0.3745 |
| 1.7588 | 12.57 | 7200 | 1.6500 | 0.3622 | 0.3756 |
| 1.7692 | 12.91 | 7400 | 1.6559 | 0.3579 | 0.3688 |
| 1.7543 | 13.26 | 7600 | 1.6547 | 0.3556 | 0.3673 |
| 1.7568 | 13.61 | 7800 | 1.6483 | 0.3649 | 0.3755 |
| 1.7573 | 13.96 | 8000 | 1.6423 | 0.3640 | 0.3756 |
| 1.7442 | 14.31 | 8200 | 1.6456 | 0.3620 | 0.3746 |
| 1.7566 | 14.66 | 8400 | 1.6388 | 0.3726 | 0.3818 |
| 1.7466 | 15.01 | 8600 | 1.6458 | 0.3579 | 0.3698 |
| 1.7529 | 15.36 | 8800 | 1.6328 | 0.3769 | 0.3863 |
| 1.7406 | 15.71 | 9000 | 1.6344 | 0.3714 | 0.3845 |
| 1.7376 | 16.06 | 9200 | 1.6312 | 0.3745 | 0.3864 |
| 1.7423 | 16.4 | 9400 | 1.6308 | 0.3732 | 0.3861 |
| 1.7429 | 16.75 | 9600 | 1.6332 | 0.3713 | 0.3826 |
| 1.7435 | 17.1 | 9800 | 1.6332 | 0.3709 | 0.3823 |
| 1.7473 | 17.45 | 10000 | 1.6315 | 0.3732 | 0.3842 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_virus_covid-seqsight_32768_512_43M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_32768_512_43M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T15:00:45+00:00 |
text-classification | transformers | {} | gc394/ft_da_distilbert | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:00:45+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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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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## 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).
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | michaelbenayoun/llama-2-tiny-4kv-heads-8layers-random | null | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T15:00:53+00:00 |
null | peft |
<!-- 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. -->
# GUE_virus_covid-seqsight_32768_512_43M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2253
- F1 Score: 0.5417
- Accuracy: 0.5433
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 2.1856 | 0.35 | 200 | 2.1853 | 0.0568 | 0.1219 |
| 2.1788 | 0.7 | 400 | 2.1724 | 0.1014 | 0.1494 |
| 2.1619 | 1.05 | 600 | 2.1430 | 0.1503 | 0.1768 |
| 2.1148 | 1.4 | 800 | 2.0385 | 0.1734 | 0.2172 |
| 2.0028 | 1.75 | 1000 | 1.9046 | 0.2631 | 0.2787 |
| 1.9152 | 2.09 | 1200 | 1.8482 | 0.2655 | 0.2893 |
| 1.852 | 2.44 | 1400 | 1.7498 | 0.3254 | 0.3333 |
| 1.8092 | 2.79 | 1600 | 1.6999 | 0.3373 | 0.3568 |
| 1.7683 | 3.14 | 1800 | 1.6663 | 0.3424 | 0.3645 |
| 1.7302 | 3.49 | 2000 | 1.6432 | 0.3620 | 0.3758 |
| 1.7128 | 3.84 | 2200 | 1.6109 | 0.3820 | 0.3966 |
| 1.6798 | 4.19 | 2400 | 1.5869 | 0.3928 | 0.3989 |
| 1.659 | 4.54 | 2600 | 1.5614 | 0.3969 | 0.4073 |
| 1.6491 | 4.89 | 2800 | 1.5399 | 0.4111 | 0.4249 |
| 1.6308 | 5.24 | 3000 | 1.5227 | 0.4190 | 0.4286 |
| 1.6162 | 5.58 | 3200 | 1.5082 | 0.4266 | 0.4413 |
| 1.5969 | 5.93 | 3400 | 1.5042 | 0.4202 | 0.4340 |
| 1.5869 | 6.28 | 3600 | 1.4714 | 0.4564 | 0.4627 |
| 1.561 | 6.63 | 3800 | 1.4475 | 0.4479 | 0.4614 |
| 1.5523 | 6.98 | 4000 | 1.4304 | 0.4622 | 0.4720 |
| 1.5363 | 7.33 | 4200 | 1.4157 | 0.4687 | 0.4787 |
| 1.5188 | 7.68 | 4400 | 1.4040 | 0.4700 | 0.4752 |
| 1.5109 | 8.03 | 4600 | 1.3890 | 0.4809 | 0.4873 |
| 1.488 | 8.38 | 4800 | 1.3785 | 0.4747 | 0.4868 |
| 1.4927 | 8.73 | 5000 | 1.3663 | 0.4806 | 0.4899 |
| 1.4798 | 9.08 | 5200 | 1.3459 | 0.4998 | 0.5048 |
| 1.4619 | 9.42 | 5400 | 1.3396 | 0.4902 | 0.5014 |
| 1.4528 | 9.77 | 5600 | 1.3264 | 0.5010 | 0.5110 |
| 1.4431 | 10.12 | 5800 | 1.3171 | 0.4994 | 0.5096 |
| 1.4406 | 10.47 | 6000 | 1.3090 | 0.5127 | 0.5161 |
| 1.4252 | 10.82 | 6200 | 1.3047 | 0.5159 | 0.5148 |
| 1.4087 | 11.17 | 6400 | 1.2971 | 0.5152 | 0.5154 |
| 1.4124 | 11.52 | 6600 | 1.2890 | 0.5257 | 0.5229 |
| 1.4147 | 11.87 | 6800 | 1.2846 | 0.5164 | 0.5208 |
| 1.3961 | 12.22 | 7000 | 1.2720 | 0.5226 | 0.5232 |
| 1.3883 | 12.57 | 7200 | 1.2659 | 0.5313 | 0.5323 |
| 1.395 | 12.91 | 7400 | 1.2664 | 0.5366 | 0.5353 |
| 1.3821 | 13.26 | 7600 | 1.2610 | 0.5355 | 0.5334 |
| 1.3721 | 13.61 | 7800 | 1.2549 | 0.5389 | 0.5348 |
| 1.3735 | 13.96 | 8000 | 1.2449 | 0.5382 | 0.5410 |
| 1.3594 | 14.31 | 8200 | 1.2458 | 0.5412 | 0.5410 |
| 1.3733 | 14.66 | 8400 | 1.2368 | 0.5433 | 0.5422 |
| 1.3614 | 15.01 | 8600 | 1.2381 | 0.5445 | 0.5465 |
| 1.3687 | 15.36 | 8800 | 1.2334 | 0.5388 | 0.5410 |
| 1.3481 | 15.71 | 9000 | 1.2289 | 0.5448 | 0.5473 |
| 1.3585 | 16.06 | 9200 | 1.2274 | 0.5503 | 0.5491 |
| 1.362 | 16.4 | 9400 | 1.2240 | 0.5443 | 0.5483 |
| 1.3525 | 16.75 | 9600 | 1.2242 | 0.5502 | 0.5495 |
| 1.35 | 17.1 | 9800 | 1.2247 | 0.5491 | 0.5494 |
| 1.3605 | 17.45 | 10000 | 1.2232 | 0.5482 | 0.5488 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_virus_covid-seqsight_32768_512_43M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_32768_512_43M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T15:01:18+00:00 |
null | peft |
<!-- 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. -->
# GUE_virus_covid-seqsight_32768_512_43M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_virus_covid](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_virus_covid) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0029
- F1 Score: 0.6167
- Accuracy: 0.6178
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 2.1853 | 0.35 | 200 | 2.1805 | 0.0843 | 0.1372 |
| 2.1768 | 0.7 | 400 | 2.1641 | 0.1214 | 0.1525 |
| 2.1212 | 1.05 | 600 | 2.0259 | 0.1938 | 0.2265 |
| 1.969 | 1.4 | 800 | 1.8285 | 0.2853 | 0.3041 |
| 1.8126 | 1.75 | 1000 | 1.6823 | 0.3570 | 0.3668 |
| 1.7124 | 2.09 | 1200 | 1.5757 | 0.4073 | 0.4168 |
| 1.6288 | 2.44 | 1400 | 1.5124 | 0.4353 | 0.4345 |
| 1.5754 | 2.79 | 1600 | 1.4519 | 0.4393 | 0.4482 |
| 1.5289 | 3.14 | 1800 | 1.4078 | 0.4593 | 0.4704 |
| 1.4859 | 3.49 | 2000 | 1.3729 | 0.4826 | 0.4772 |
| 1.4627 | 3.84 | 2200 | 1.3404 | 0.4993 | 0.4986 |
| 1.4266 | 4.19 | 2400 | 1.3065 | 0.5062 | 0.5061 |
| 1.4048 | 4.54 | 2600 | 1.2738 | 0.5171 | 0.5223 |
| 1.3816 | 4.89 | 2800 | 1.2513 | 0.5304 | 0.5339 |
| 1.3649 | 5.24 | 3000 | 1.2371 | 0.5309 | 0.5336 |
| 1.3436 | 5.58 | 3200 | 1.2223 | 0.5484 | 0.5464 |
| 1.3166 | 5.93 | 3400 | 1.2165 | 0.5484 | 0.5492 |
| 1.3061 | 6.28 | 3600 | 1.1944 | 0.5550 | 0.5507 |
| 1.2792 | 6.63 | 3800 | 1.1837 | 0.5597 | 0.5550 |
| 1.2746 | 6.98 | 4000 | 1.1678 | 0.5583 | 0.5607 |
| 1.2552 | 7.33 | 4200 | 1.1544 | 0.5723 | 0.5703 |
| 1.2414 | 7.68 | 4400 | 1.1456 | 0.5710 | 0.5699 |
| 1.2377 | 8.03 | 4600 | 1.1386 | 0.5743 | 0.5709 |
| 1.2159 | 8.38 | 4800 | 1.1237 | 0.5807 | 0.5835 |
| 1.222 | 8.73 | 5000 | 1.1138 | 0.5848 | 0.5836 |
| 1.1993 | 9.08 | 5200 | 1.1315 | 0.5875 | 0.5787 |
| 1.1904 | 9.42 | 5400 | 1.0984 | 0.5867 | 0.5885 |
| 1.1684 | 9.77 | 5600 | 1.0853 | 0.5910 | 0.5879 |
| 1.1775 | 10.12 | 5800 | 1.0639 | 0.5927 | 0.5922 |
| 1.1735 | 10.47 | 6000 | 1.0625 | 0.5994 | 0.5988 |
| 1.155 | 10.82 | 6200 | 1.0571 | 0.6021 | 0.5948 |
| 1.1379 | 11.17 | 6400 | 1.0743 | 0.5951 | 0.5907 |
| 1.1367 | 11.52 | 6600 | 1.0611 | 0.6045 | 0.5984 |
| 1.1426 | 11.87 | 6800 | 1.0483 | 0.5977 | 0.5944 |
| 1.135 | 12.22 | 7000 | 1.0395 | 0.6083 | 0.6058 |
| 1.1153 | 12.57 | 7200 | 1.0375 | 0.6060 | 0.6007 |
| 1.1251 | 12.91 | 7400 | 1.0405 | 0.6050 | 0.6004 |
| 1.1104 | 13.26 | 7600 | 1.0430 | 0.6094 | 0.6007 |
| 1.1089 | 13.61 | 7800 | 1.0323 | 0.6107 | 0.6053 |
| 1.1053 | 13.96 | 8000 | 1.0236 | 0.6133 | 0.6066 |
| 1.0963 | 14.31 | 8200 | 1.0296 | 0.6088 | 0.6036 |
| 1.1049 | 14.66 | 8400 | 1.0208 | 0.6143 | 0.6091 |
| 1.0961 | 15.01 | 8600 | 1.0285 | 0.6103 | 0.6048 |
| 1.0962 | 15.36 | 8800 | 1.0178 | 0.6140 | 0.6096 |
| 1.0789 | 15.71 | 9000 | 1.0094 | 0.6166 | 0.6130 |
| 1.0905 | 16.06 | 9200 | 1.0114 | 0.6164 | 0.6116 |
| 1.0829 | 16.4 | 9400 | 1.0132 | 0.6112 | 0.6081 |
| 1.0924 | 16.75 | 9600 | 1.0119 | 0.6165 | 0.6114 |
| 1.0855 | 17.1 | 9800 | 1.0113 | 0.6156 | 0.6110 |
| 1.0875 | 17.45 | 10000 | 1.0112 | 0.6153 | 0.6107 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_virus_covid-seqsight_32768_512_43M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_32768_512_43M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_43M",
"region:us"
] | null | 2024-05-03T15:01:25+00:00 |
feature-extraction | transformers | {} | MahmoudTaktak/LEGAL_E4 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:01:34+00:00 |
|
null | null | {} | chenbingAi/mistral-7b-bnb-4bit-1.0v-law | null | [
"region:us"
] | null | 2024-05-03T15:01:37+00:00 |
|
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jchenmath/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | jchenmath/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-05-03T15:02:26+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** chenbingAi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral 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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl", "sft"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | chenbingAi/mistral-7b-bnb-4bit-1.0v | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-05-03T15:02:31+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** rvian
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | rvian/llama3-midjourney-prompt-generator | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:03:51+00:00 |
text-classification | transformers | {} | koheisanno/roberta-large-finetuned-mnli | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:04:16+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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| {"library_name": "transformers", "tags": []} | dzungPaduahsgs/Vistral7B_mix_v4_adamany_model_batch_32_lr_2e-5_12h40_merged | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:04:27+00:00 |
null | peft |
<!-- 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. -->
# witness_count_mistral_train_run2
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "witness_count_mistral_train_run2", "results": []}]} | isaaclee/witness_count_mistral_train_run2 | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T15:04:30+00:00 |
text-generation | transformers | # jsfs11/WestTemptressTensor-10.7B-v0.2a-SLERP AWQ
- Model creator: [jsfs11](https://huggingface.co/jsfs11)
- Original model: [WestTemptressTensor-10.7B-v0.2a-SLERP](https://huggingface.co/jsfs11/WestTemptressTensor-10.7B-v0.2a-SLERP)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/WestTemptressTensor-10.7B-v0.2a-SLERP-AWQ"
system_message = "You are WestTemptressTensor-10.7B-v0.2a-SLERP, incarnated as a powerful AI. You were created by jsfs11."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/WestTemptressTensor-10.7B-v0.2a-SLERP-AWQ | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T15:05:10+00:00 |
null | peft |
<!-- 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. -->
# GUE_prom_prom_300_tata-seqsight_65536_512_47M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_tata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4868
- F1 Score: 0.7961
- Accuracy: 0.7961
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6088 | 5.13 | 200 | 0.5384 | 0.7487 | 0.7520 |
| 0.4928 | 10.26 | 400 | 0.5026 | 0.7783 | 0.7781 |
| 0.4693 | 15.38 | 600 | 0.4846 | 0.7717 | 0.7716 |
| 0.4521 | 20.51 | 800 | 0.4655 | 0.7841 | 0.7847 |
| 0.4428 | 25.64 | 1000 | 0.4595 | 0.7961 | 0.7961 |
| 0.4323 | 30.77 | 1200 | 0.4505 | 0.8009 | 0.8010 |
| 0.4233 | 35.9 | 1400 | 0.4517 | 0.8060 | 0.8059 |
| 0.4143 | 41.03 | 1600 | 0.4466 | 0.8039 | 0.8042 |
| 0.4078 | 46.15 | 1800 | 0.4488 | 0.8076 | 0.8075 |
| 0.401 | 51.28 | 2000 | 0.4429 | 0.8125 | 0.8124 |
| 0.3939 | 56.41 | 2200 | 0.4469 | 0.8040 | 0.8042 |
| 0.3921 | 61.54 | 2400 | 0.4504 | 0.8093 | 0.8091 |
| 0.3851 | 66.67 | 2600 | 0.4505 | 0.8093 | 0.8091 |
| 0.3812 | 71.79 | 2800 | 0.4460 | 0.8060 | 0.8059 |
| 0.3806 | 76.92 | 3000 | 0.4651 | 0.7993 | 0.7993 |
| 0.3745 | 82.05 | 3200 | 0.4532 | 0.8093 | 0.8091 |
| 0.3722 | 87.18 | 3400 | 0.4718 | 0.7976 | 0.7977 |
| 0.3652 | 92.31 | 3600 | 0.4520 | 0.8142 | 0.8140 |
| 0.367 | 97.44 | 3800 | 0.4515 | 0.8109 | 0.8108 |
| 0.3615 | 102.56 | 4000 | 0.4595 | 0.8109 | 0.8108 |
| 0.3633 | 107.69 | 4200 | 0.4684 | 0.7978 | 0.7977 |
| 0.3561 | 112.82 | 4400 | 0.4668 | 0.8093 | 0.8091 |
| 0.3533 | 117.95 | 4600 | 0.4705 | 0.8044 | 0.8042 |
| 0.351 | 123.08 | 4800 | 0.4721 | 0.8060 | 0.8059 |
| 0.3528 | 128.21 | 5000 | 0.4621 | 0.8141 | 0.8140 |
| 0.3479 | 133.33 | 5200 | 0.4673 | 0.8092 | 0.8091 |
| 0.3466 | 138.46 | 5400 | 0.4637 | 0.8140 | 0.8140 |
| 0.3432 | 143.59 | 5600 | 0.4672 | 0.8141 | 0.8140 |
| 0.3426 | 148.72 | 5800 | 0.4673 | 0.8158 | 0.8157 |
| 0.3417 | 153.85 | 6000 | 0.4708 | 0.8093 | 0.8091 |
| 0.3424 | 158.97 | 6200 | 0.4735 | 0.8125 | 0.8124 |
| 0.3372 | 164.1 | 6400 | 0.4821 | 0.8076 | 0.8075 |
| 0.3379 | 169.23 | 6600 | 0.4719 | 0.8125 | 0.8124 |
| 0.3357 | 174.36 | 6800 | 0.4774 | 0.8125 | 0.8124 |
| 0.332 | 179.49 | 7000 | 0.4816 | 0.8093 | 0.8091 |
| 0.3329 | 184.62 | 7200 | 0.4783 | 0.8124 | 0.8124 |
| 0.3318 | 189.74 | 7400 | 0.4833 | 0.8093 | 0.8091 |
| 0.3303 | 194.87 | 7600 | 0.4834 | 0.8125 | 0.8124 |
| 0.3287 | 200.0 | 7800 | 0.4815 | 0.8108 | 0.8108 |
| 0.3285 | 205.13 | 8000 | 0.4822 | 0.8174 | 0.8173 |
| 0.3328 | 210.26 | 8200 | 0.4839 | 0.8093 | 0.8091 |
| 0.3267 | 215.38 | 8400 | 0.4831 | 0.8125 | 0.8124 |
| 0.3287 | 220.51 | 8600 | 0.4837 | 0.8125 | 0.8124 |
| 0.3268 | 225.64 | 8800 | 0.4911 | 0.8027 | 0.8026 |
| 0.3265 | 230.77 | 9000 | 0.4880 | 0.8076 | 0.8075 |
| 0.3275 | 235.9 | 9200 | 0.4868 | 0.8093 | 0.8091 |
| 0.3238 | 241.03 | 9400 | 0.4868 | 0.8109 | 0.8108 |
| 0.3273 | 246.15 | 9600 | 0.4869 | 0.8093 | 0.8091 |
| 0.3274 | 251.28 | 9800 | 0.4875 | 0.8109 | 0.8108 |
| 0.3265 | 256.41 | 10000 | 0.4874 | 0.8109 | 0.8108 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_65536_512_47M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_65536_512_47M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:05:35+00:00 |
null | null | {} | Acopa/sdxl_turbo_lora_test | null | [
"region:us"
] | null | 2024-05-03T15:06:21+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | huntz47/qwenm10 | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T15:06:27+00:00 |
null | transformers |
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| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.05_8_5e-05 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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"region:us"
] | null | 2024-05-03T15:06:30+00:00 |
text-generation | transformers |
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<!-- 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]
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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]
| {"library_name": "transformers", "tags": []} | cilantro9246/faxebch | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T15:06:40+00:00 |
null | null | {} | ayoubkirouane/my_awesome_model | null | [
"region:us"
] | null | 2024-05-03T15:06:55+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# rloo_zephyr_vllm11
This model was trained from scratch 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: 3e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- gradient_accumulation_steps: 32
- total_train_batch_size: 224
- total_eval_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "rloo_zephyr_vllm11", "results": []}]} | vwxyzjn/rloo_zephyr_vllm11 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T15:07:17+00:00 |
null | null | {"license": "openrail"} | saeidebbei/Dub | null | [
"license:openrail",
"region:us"
] | null | 2024-05-03T15:07:32+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** animaRegem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | animaRegem/llama-3-lora-01-malayalam | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:07:34+00:00 |
null | transformers |
# 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
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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]
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[More Information Needed]
## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | animaRegem/llama-3-lora-01-malayalam-tokenizer | null | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:07:47+00:00 |
null | transformers |
<!-- 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. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.4563
- eval_mean_iou: 0.0432
- eval_mean_accuracy: 0.0696
- eval_overall_accuracy: 0.5913
- eval_per_category_iou: [0.4472851919015029, 0.6612097108758626, 0.817339666449671, 0.47928449607416507, 0.5911507360971395, 0.584974453286796, 0.6726074613245039, 0.2589327338580983, 0.022897061669389426, 0.3531389341071555, 0.0009033242331780954, 0.0, 0.38016586218727527, 0.0065494844799213895, 3.5410365901749114e-05, 0.0006227857923162527, 0.1369807957501803, 0.0, 0.0, 0.0, 0.3866305742675126, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15958629131507837, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
- eval_per_category_accuracy: [0.8334025427555467, 0.90546175118556, 0.9576760329344776, 0.9040202679951341, 0.9084813897020947, 0.7543100790506285, 0.924642649916285, 0.6768858942434451, 0.024248627368742136, 0.8855665819147363, 0.0009169818241372258, 0.0, 0.7872266396753254, 0.006739498091427447, 3.561201678944719e-05, 0.0006261997885292518, 0.24443709595222143, 0.0, 0.0, 0.0, 0.6322151772008276, 0.0, 0.0, 0.0, 0.0, 0.0, 0.16159973151359214, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
- eval_runtime: 22.5623
- eval_samples_per_second: 8.864
- eval_steps_per_second: 0.576
- epoch: 4.8
- step: 240
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 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: 50
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["scene_parse_150"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-b0-scene-parse-150", "results": []}]} | ChayawatP/segformer-b0-scene-parse-150 | null | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:07:57+00:00 |
text-classification | transformers | {} | eskayML/interview_classifier | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:08:00+00:00 |
|
null | null | {"license": "openrail"} | saeidebbei/Duble | null | [
"license:openrail",
"region:us"
] | null | 2024-05-03T15:08:04+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_prom_prom_300_tata-seqsight_65536_512_47M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_tata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4666
- F1 Score: 0.8026
- Accuracy: 0.8026
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5548 | 5.13 | 200 | 0.5050 | 0.7516 | 0.7553 |
| 0.4582 | 10.26 | 400 | 0.4638 | 0.7994 | 0.7993 |
| 0.4275 | 15.38 | 600 | 0.4521 | 0.8060 | 0.8059 |
| 0.4017 | 20.51 | 800 | 0.4511 | 0.7995 | 0.7993 |
| 0.3843 | 25.64 | 1000 | 0.4591 | 0.7995 | 0.7993 |
| 0.3641 | 30.77 | 1200 | 0.4585 | 0.8107 | 0.8108 |
| 0.3522 | 35.9 | 1400 | 0.4668 | 0.8043 | 0.8042 |
| 0.3365 | 41.03 | 1600 | 0.4775 | 0.8086 | 0.8091 |
| 0.3228 | 46.15 | 1800 | 0.4857 | 0.7962 | 0.7961 |
| 0.3121 | 51.28 | 2000 | 0.4890 | 0.8056 | 0.8059 |
| 0.303 | 56.41 | 2200 | 0.5310 | 0.7911 | 0.7912 |
| 0.2937 | 61.54 | 2400 | 0.5404 | 0.7959 | 0.7961 |
| 0.283 | 66.67 | 2600 | 0.5231 | 0.8076 | 0.8075 |
| 0.2758 | 71.79 | 2800 | 0.5463 | 0.8026 | 0.8026 |
| 0.2732 | 76.92 | 3000 | 0.5306 | 0.7960 | 0.7961 |
| 0.2621 | 82.05 | 3200 | 0.5515 | 0.8059 | 0.8059 |
| 0.2568 | 87.18 | 3400 | 0.5725 | 0.7977 | 0.7977 |
| 0.248 | 92.31 | 3600 | 0.5643 | 0.8060 | 0.8059 |
| 0.246 | 97.44 | 3800 | 0.5643 | 0.7942 | 0.7945 |
| 0.2372 | 102.56 | 4000 | 0.6019 | 0.7928 | 0.7928 |
| 0.2343 | 107.69 | 4200 | 0.5971 | 0.8010 | 0.8010 |
| 0.2237 | 112.82 | 4400 | 0.6042 | 0.7962 | 0.7961 |
| 0.2207 | 117.95 | 4600 | 0.6285 | 0.7943 | 0.7945 |
| 0.2145 | 123.08 | 4800 | 0.6262 | 0.7848 | 0.7847 |
| 0.21 | 128.21 | 5000 | 0.6390 | 0.7962 | 0.7961 |
| 0.2078 | 133.33 | 5200 | 0.6459 | 0.7897 | 0.7896 |
| 0.1989 | 138.46 | 5400 | 0.6421 | 0.7896 | 0.7896 |
| 0.1996 | 143.59 | 5600 | 0.6495 | 0.7946 | 0.7945 |
| 0.1968 | 148.72 | 5800 | 0.6572 | 0.8011 | 0.8010 |
| 0.1925 | 153.85 | 6000 | 0.6692 | 0.8044 | 0.8042 |
| 0.1917 | 158.97 | 6200 | 0.6786 | 0.7994 | 0.7993 |
| 0.1868 | 164.1 | 6400 | 0.6769 | 0.7995 | 0.7993 |
| 0.1846 | 169.23 | 6600 | 0.6911 | 0.7978 | 0.7977 |
| 0.1786 | 174.36 | 6800 | 0.6737 | 0.7946 | 0.7945 |
| 0.1803 | 179.49 | 7000 | 0.6817 | 0.7995 | 0.7993 |
| 0.1777 | 184.62 | 7200 | 0.6831 | 0.7962 | 0.7961 |
| 0.1745 | 189.74 | 7400 | 0.7034 | 0.7995 | 0.7993 |
| 0.1752 | 194.87 | 7600 | 0.7135 | 0.7896 | 0.7896 |
| 0.1703 | 200.0 | 7800 | 0.7156 | 0.7978 | 0.7977 |
| 0.1649 | 205.13 | 8000 | 0.7408 | 0.7962 | 0.7961 |
| 0.1744 | 210.26 | 8200 | 0.7215 | 0.7946 | 0.7945 |
| 0.1698 | 215.38 | 8400 | 0.7257 | 0.7978 | 0.7977 |
| 0.1637 | 220.51 | 8600 | 0.7321 | 0.7979 | 0.7977 |
| 0.1608 | 225.64 | 8800 | 0.7433 | 0.7979 | 0.7977 |
| 0.1613 | 230.77 | 9000 | 0.7391 | 0.8011 | 0.8010 |
| 0.1636 | 235.9 | 9200 | 0.7425 | 0.7962 | 0.7961 |
| 0.1573 | 241.03 | 9400 | 0.7449 | 0.7979 | 0.7977 |
| 0.1632 | 246.15 | 9600 | 0.7407 | 0.7995 | 0.7993 |
| 0.1612 | 251.28 | 9800 | 0.7437 | 0.7978 | 0.7977 |
| 0.1601 | 256.41 | 10000 | 0.7429 | 0.7978 | 0.7977 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_65536_512_47M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_65536_512_47M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:08:13+00:00 |
null | peft |
<!-- 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. -->
# GUE_prom_prom_300_tata-seqsight_65536_512_47M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_tata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5055
- F1 Score: 0.8041
- Accuracy: 0.8042
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5307 | 5.13 | 200 | 0.4727 | 0.7787 | 0.7798 |
| 0.4297 | 10.26 | 400 | 0.4684 | 0.7909 | 0.7912 |
| 0.3842 | 15.38 | 600 | 0.4664 | 0.8026 | 0.8026 |
| 0.3458 | 20.51 | 800 | 0.4781 | 0.8040 | 0.8042 |
| 0.3183 | 25.64 | 1000 | 0.5174 | 0.8060 | 0.8059 |
| 0.2872 | 30.77 | 1200 | 0.5517 | 0.7946 | 0.7945 |
| 0.2632 | 35.9 | 1400 | 0.5920 | 0.7912 | 0.7912 |
| 0.2357 | 41.03 | 1600 | 0.6452 | 0.7859 | 0.7863 |
| 0.2121 | 46.15 | 1800 | 0.7001 | 0.7662 | 0.7667 |
| 0.1907 | 51.28 | 2000 | 0.7603 | 0.7797 | 0.7798 |
| 0.1756 | 56.41 | 2200 | 0.7975 | 0.7782 | 0.7781 |
| 0.1654 | 61.54 | 2400 | 0.8281 | 0.7765 | 0.7765 |
| 0.1525 | 66.67 | 2600 | 0.7975 | 0.7831 | 0.7830 |
| 0.1402 | 71.79 | 2800 | 0.8550 | 0.7848 | 0.7847 |
| 0.1315 | 76.92 | 3000 | 0.8706 | 0.7897 | 0.7896 |
| 0.1204 | 82.05 | 3200 | 0.9344 | 0.7881 | 0.7879 |
| 0.1086 | 87.18 | 3400 | 0.9829 | 0.7832 | 0.7830 |
| 0.1055 | 92.31 | 3600 | 1.0072 | 0.7927 | 0.7928 |
| 0.1014 | 97.44 | 3800 | 0.9490 | 0.7798 | 0.7798 |
| 0.0915 | 102.56 | 4000 | 1.0467 | 0.7864 | 0.7863 |
| 0.0915 | 107.69 | 4200 | 1.0706 | 0.7848 | 0.7847 |
| 0.0867 | 112.82 | 4400 | 1.0829 | 0.7832 | 0.7830 |
| 0.0787 | 117.95 | 4600 | 1.1589 | 0.7864 | 0.7863 |
| 0.0776 | 123.08 | 4800 | 1.1396 | 0.7783 | 0.7781 |
| 0.0732 | 128.21 | 5000 | 1.1038 | 0.7864 | 0.7863 |
| 0.0689 | 133.33 | 5200 | 1.1479 | 0.7832 | 0.7830 |
| 0.0692 | 138.46 | 5400 | 1.1645 | 0.7734 | 0.7732 |
| 0.0674 | 143.59 | 5600 | 1.1893 | 0.7815 | 0.7814 |
| 0.0658 | 148.72 | 5800 | 1.1625 | 0.7749 | 0.7749 |
| 0.0617 | 153.85 | 6000 | 1.2137 | 0.7815 | 0.7814 |
| 0.0606 | 158.97 | 6200 | 1.2414 | 0.7799 | 0.7798 |
| 0.056 | 164.1 | 6400 | 1.2492 | 0.7782 | 0.7781 |
| 0.0598 | 169.23 | 6600 | 1.2057 | 0.7815 | 0.7814 |
| 0.0553 | 174.36 | 6800 | 1.2501 | 0.7798 | 0.7798 |
| 0.0533 | 179.49 | 7000 | 1.2808 | 0.7767 | 0.7765 |
| 0.0532 | 184.62 | 7200 | 1.2510 | 0.7880 | 0.7879 |
| 0.0531 | 189.74 | 7400 | 1.2596 | 0.7799 | 0.7798 |
| 0.0514 | 194.87 | 7600 | 1.2814 | 0.7816 | 0.7814 |
| 0.0496 | 200.0 | 7800 | 1.2637 | 0.7864 | 0.7863 |
| 0.0463 | 205.13 | 8000 | 1.3075 | 0.7799 | 0.7798 |
| 0.0493 | 210.26 | 8200 | 1.3099 | 0.7816 | 0.7814 |
| 0.0471 | 215.38 | 8400 | 1.3130 | 0.7767 | 0.7765 |
| 0.0433 | 220.51 | 8600 | 1.3321 | 0.7799 | 0.7798 |
| 0.0442 | 225.64 | 8800 | 1.3315 | 0.7815 | 0.7814 |
| 0.0437 | 230.77 | 9000 | 1.3364 | 0.7766 | 0.7765 |
| 0.0431 | 235.9 | 9200 | 1.3456 | 0.7734 | 0.7732 |
| 0.0429 | 241.03 | 9400 | 1.3446 | 0.7799 | 0.7798 |
| 0.0439 | 246.15 | 9600 | 1.3338 | 0.7783 | 0.7781 |
| 0.0462 | 251.28 | 9800 | 1.3291 | 0.7815 | 0.7814 |
| 0.0418 | 256.41 | 10000 | 1.3333 | 0.7832 | 0.7830 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_65536_512_47M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_65536_512_47M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:09:08+00:00 |
null | peft |
<!-- 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. -->
# GUE_prom_prom_300_notata-seqsight_65536_512_47M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1327
- F1 Score: 0.9504
- Accuracy: 0.9504
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3429 | 0.6 | 200 | 0.1803 | 0.9252 | 0.9252 |
| 0.1949 | 1.2 | 400 | 0.1537 | 0.9382 | 0.9382 |
| 0.1793 | 1.81 | 600 | 0.1431 | 0.9420 | 0.9420 |
| 0.168 | 2.41 | 800 | 0.1371 | 0.9440 | 0.9440 |
| 0.1618 | 3.01 | 1000 | 0.1396 | 0.9450 | 0.9450 |
| 0.1581 | 3.61 | 1200 | 0.1325 | 0.9471 | 0.9471 |
| 0.16 | 4.22 | 1400 | 0.1401 | 0.9446 | 0.9446 |
| 0.1499 | 4.82 | 1600 | 0.1287 | 0.9482 | 0.9482 |
| 0.152 | 5.42 | 1800 | 0.1407 | 0.9452 | 0.9452 |
| 0.1425 | 6.02 | 2000 | 0.1363 | 0.9474 | 0.9474 |
| 0.1462 | 6.63 | 2200 | 0.1221 | 0.9536 | 0.9536 |
| 0.1439 | 7.23 | 2400 | 0.1228 | 0.9508 | 0.9508 |
| 0.1375 | 7.83 | 2600 | 0.1223 | 0.9529 | 0.9529 |
| 0.1404 | 8.43 | 2800 | 0.1228 | 0.9521 | 0.9521 |
| 0.1427 | 9.04 | 3000 | 0.1190 | 0.9517 | 0.9518 |
| 0.1372 | 9.64 | 3200 | 0.1286 | 0.9510 | 0.9510 |
| 0.1378 | 10.24 | 3400 | 0.1184 | 0.9531 | 0.9531 |
| 0.1384 | 10.84 | 3600 | 0.1172 | 0.9536 | 0.9536 |
| 0.1333 | 11.45 | 3800 | 0.1242 | 0.9516 | 0.9516 |
| 0.1343 | 12.05 | 4000 | 0.1176 | 0.9563 | 0.9563 |
| 0.136 | 12.65 | 4200 | 0.1175 | 0.9544 | 0.9544 |
| 0.1362 | 13.25 | 4400 | 0.1166 | 0.9552 | 0.9552 |
| 0.1319 | 13.86 | 4600 | 0.1147 | 0.9548 | 0.9548 |
| 0.1312 | 14.46 | 4800 | 0.1158 | 0.9544 | 0.9544 |
| 0.1284 | 15.06 | 5000 | 0.1158 | 0.9538 | 0.9538 |
| 0.1296 | 15.66 | 5200 | 0.1147 | 0.9557 | 0.9557 |
| 0.1309 | 16.27 | 5400 | 0.1136 | 0.9533 | 0.9533 |
| 0.1228 | 16.87 | 5600 | 0.1137 | 0.9540 | 0.9540 |
| 0.1289 | 17.47 | 5800 | 0.1125 | 0.9546 | 0.9546 |
| 0.131 | 18.07 | 6000 | 0.1135 | 0.9552 | 0.9552 |
| 0.1287 | 18.67 | 6200 | 0.1125 | 0.9542 | 0.9542 |
| 0.1292 | 19.28 | 6400 | 0.1122 | 0.9535 | 0.9535 |
| 0.1254 | 19.88 | 6600 | 0.1129 | 0.9534 | 0.9535 |
| 0.1302 | 20.48 | 6800 | 0.1116 | 0.9553 | 0.9553 |
| 0.1223 | 21.08 | 7000 | 0.1126 | 0.9557 | 0.9557 |
| 0.1245 | 21.69 | 7200 | 0.1152 | 0.9553 | 0.9553 |
| 0.1258 | 22.29 | 7400 | 0.1138 | 0.9565 | 0.9565 |
| 0.1279 | 22.89 | 7600 | 0.1118 | 0.9565 | 0.9565 |
| 0.1227 | 23.49 | 7800 | 0.1128 | 0.9559 | 0.9559 |
| 0.1237 | 24.1 | 8000 | 0.1122 | 0.9548 | 0.9548 |
| 0.1256 | 24.7 | 8200 | 0.1112 | 0.9546 | 0.9546 |
| 0.1238 | 25.3 | 8400 | 0.1098 | 0.9544 | 0.9544 |
| 0.1252 | 25.9 | 8600 | 0.1113 | 0.9567 | 0.9567 |
| 0.1233 | 26.51 | 8800 | 0.1109 | 0.9557 | 0.9557 |
| 0.1253 | 27.11 | 9000 | 0.1102 | 0.9548 | 0.9548 |
| 0.1222 | 27.71 | 9200 | 0.1104 | 0.9555 | 0.9555 |
| 0.1192 | 28.31 | 9400 | 0.1105 | 0.9555 | 0.9555 |
| 0.1282 | 28.92 | 9600 | 0.1108 | 0.9563 | 0.9563 |
| 0.1206 | 29.52 | 9800 | 0.1107 | 0.9552 | 0.9552 |
| 0.1229 | 30.12 | 10000 | 0.1106 | 0.9555 | 0.9555 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_65536_512_47M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_65536_512_47M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:09:09+00:00 |
null | null | Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement:
https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
DPO fine tuning method using the following datasets:
- https://huggingface.co/datasets/Intel/orca_dpo_pairs
- https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo
- https://huggingface.co/datasets/unalignment/toxic-dpo-v0.2
- https://huggingface.co/datasets/M4-ai/prm_dpo_pairs_cleaned
- https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1
We are happy for anyone to try it out and give some feedback and we will have the model up on https://awanllm.com on our LLM API if it is popular.
Instruct format:
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
Quants:
FP16: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Instruct-DPO-v0.1
GGUF: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Instruct-DPO-v0.1-GGUF | {"license": "llama3"} | AwanLLM/Awanllm-Llama-3-8B-Instruct-DPO-v0.1-GGUF | null | [
"gguf",
"license:llama3",
"region:us"
] | null | 2024-05-03T15:09:21+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/SSNI-367 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:09:39+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/SDNM-448 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:09:42+00:00 |
null | peft |
<!-- 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. -->
# GUE_prom_prom_300_notata-seqsight_65536_512_47M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1188
- F1 Score: 0.9561
- Accuracy: 0.9561
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.2673 | 0.6 | 200 | 0.1413 | 0.9446 | 0.9446 |
| 0.1534 | 1.2 | 400 | 0.1216 | 0.9531 | 0.9531 |
| 0.1432 | 1.81 | 600 | 0.1169 | 0.9540 | 0.9540 |
| 0.1285 | 2.41 | 800 | 0.1113 | 0.9548 | 0.9548 |
| 0.1276 | 3.01 | 1000 | 0.1150 | 0.9550 | 0.9550 |
| 0.123 | 3.61 | 1200 | 0.1117 | 0.9549 | 0.9550 |
| 0.1236 | 4.22 | 1400 | 0.1043 | 0.9568 | 0.9568 |
| 0.1175 | 4.82 | 1600 | 0.1039 | 0.9587 | 0.9587 |
| 0.1194 | 5.42 | 1800 | 0.1027 | 0.9583 | 0.9584 |
| 0.1102 | 6.02 | 2000 | 0.1048 | 0.9580 | 0.9580 |
| 0.1133 | 6.63 | 2200 | 0.1019 | 0.9606 | 0.9606 |
| 0.108 | 7.23 | 2400 | 0.1015 | 0.9614 | 0.9614 |
| 0.1044 | 7.83 | 2600 | 0.1025 | 0.9608 | 0.9608 |
| 0.1069 | 8.43 | 2800 | 0.1215 | 0.9548 | 0.9548 |
| 0.1088 | 9.04 | 3000 | 0.1005 | 0.9612 | 0.9612 |
| 0.1034 | 9.64 | 3200 | 0.1039 | 0.9593 | 0.9593 |
| 0.1027 | 10.24 | 3400 | 0.0999 | 0.9610 | 0.9610 |
| 0.104 | 10.84 | 3600 | 0.0982 | 0.9604 | 0.9604 |
| 0.0961 | 11.45 | 3800 | 0.1007 | 0.9608 | 0.9608 |
| 0.0969 | 12.05 | 4000 | 0.1006 | 0.9619 | 0.9619 |
| 0.0976 | 12.65 | 4200 | 0.0973 | 0.9616 | 0.9616 |
| 0.0979 | 13.25 | 4400 | 0.1016 | 0.9606 | 0.9606 |
| 0.0929 | 13.86 | 4600 | 0.0961 | 0.9614 | 0.9614 |
| 0.0915 | 14.46 | 4800 | 0.1039 | 0.9612 | 0.9612 |
| 0.0935 | 15.06 | 5000 | 0.1010 | 0.9595 | 0.9595 |
| 0.0906 | 15.66 | 5200 | 0.0977 | 0.9621 | 0.9621 |
| 0.0909 | 16.27 | 5400 | 0.1007 | 0.9616 | 0.9616 |
| 0.0862 | 16.87 | 5600 | 0.1006 | 0.9604 | 0.9604 |
| 0.088 | 17.47 | 5800 | 0.0983 | 0.9614 | 0.9614 |
| 0.0917 | 18.07 | 6000 | 0.0972 | 0.9625 | 0.9625 |
| 0.0871 | 18.67 | 6200 | 0.0982 | 0.9634 | 0.9634 |
| 0.0876 | 19.28 | 6400 | 0.1016 | 0.9606 | 0.9606 |
| 0.0838 | 19.88 | 6600 | 0.1026 | 0.9617 | 0.9617 |
| 0.0886 | 20.48 | 6800 | 0.1015 | 0.9616 | 0.9616 |
| 0.0809 | 21.08 | 7000 | 0.1023 | 0.9606 | 0.9606 |
| 0.0844 | 21.69 | 7200 | 0.1022 | 0.9616 | 0.9616 |
| 0.0818 | 22.29 | 7400 | 0.1043 | 0.9601 | 0.9601 |
| 0.0837 | 22.89 | 7600 | 0.1006 | 0.9612 | 0.9612 |
| 0.0797 | 23.49 | 7800 | 0.1030 | 0.9621 | 0.9621 |
| 0.0802 | 24.1 | 8000 | 0.1003 | 0.9629 | 0.9629 |
| 0.0803 | 24.7 | 8200 | 0.1018 | 0.9621 | 0.9621 |
| 0.0791 | 25.3 | 8400 | 0.1009 | 0.9617 | 0.9617 |
| 0.0789 | 25.9 | 8600 | 0.1010 | 0.9631 | 0.9631 |
| 0.0802 | 26.51 | 8800 | 0.1020 | 0.9614 | 0.9614 |
| 0.0791 | 27.11 | 9000 | 0.0996 | 0.9623 | 0.9623 |
| 0.0772 | 27.71 | 9200 | 0.1006 | 0.9623 | 0.9623 |
| 0.075 | 28.31 | 9400 | 0.1024 | 0.9621 | 0.9621 |
| 0.0812 | 28.92 | 9600 | 0.1016 | 0.9621 | 0.9621 |
| 0.074 | 29.52 | 9800 | 0.1017 | 0.9617 | 0.9617 |
| 0.078 | 30.12 | 10000 | 0.1013 | 0.9623 | 0.9623 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_65536_512_47M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_65536_512_47M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:10:00+00:00 |
null | peft |
<!-- 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. -->
# GUE_prom_prom_300_notata-seqsight_65536_512_47M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1215
- F1 Score: 0.9536
- Accuracy: 0.9536
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.2907 | 0.6 | 200 | 0.1476 | 0.9401 | 0.9401 |
| 0.1704 | 1.2 | 400 | 0.1310 | 0.9500 | 0.9501 |
| 0.1551 | 1.81 | 600 | 0.1234 | 0.9518 | 0.9518 |
| 0.1386 | 2.41 | 800 | 0.1177 | 0.9527 | 0.9527 |
| 0.1356 | 3.01 | 1000 | 0.1208 | 0.9533 | 0.9533 |
| 0.1322 | 3.61 | 1200 | 0.1136 | 0.9538 | 0.9538 |
| 0.1342 | 4.22 | 1400 | 0.1152 | 0.9548 | 0.9548 |
| 0.1255 | 4.82 | 1600 | 0.1090 | 0.9570 | 0.9570 |
| 0.1286 | 5.42 | 1800 | 0.1090 | 0.9563 | 0.9563 |
| 0.1202 | 6.02 | 2000 | 0.1089 | 0.9569 | 0.9568 |
| 0.1233 | 6.63 | 2200 | 0.1050 | 0.9578 | 0.9578 |
| 0.1185 | 7.23 | 2400 | 0.1058 | 0.9585 | 0.9585 |
| 0.115 | 7.83 | 2600 | 0.1066 | 0.9585 | 0.9585 |
| 0.1179 | 8.43 | 2800 | 0.1076 | 0.9567 | 0.9567 |
| 0.1208 | 9.04 | 3000 | 0.1067 | 0.9566 | 0.9567 |
| 0.1145 | 9.64 | 3200 | 0.1089 | 0.9584 | 0.9584 |
| 0.116 | 10.24 | 3400 | 0.1037 | 0.9602 | 0.9602 |
| 0.1157 | 10.84 | 3600 | 0.1020 | 0.9587 | 0.9587 |
| 0.1103 | 11.45 | 3800 | 0.1030 | 0.9570 | 0.9570 |
| 0.1111 | 12.05 | 4000 | 0.1031 | 0.9585 | 0.9585 |
| 0.113 | 12.65 | 4200 | 0.1007 | 0.9593 | 0.9593 |
| 0.1121 | 13.25 | 4400 | 0.1026 | 0.9582 | 0.9582 |
| 0.1088 | 13.86 | 4600 | 0.1006 | 0.9585 | 0.9585 |
| 0.1087 | 14.46 | 4800 | 0.1022 | 0.9584 | 0.9584 |
| 0.1068 | 15.06 | 5000 | 0.1024 | 0.9572 | 0.9572 |
| 0.1061 | 15.66 | 5200 | 0.1008 | 0.9585 | 0.9585 |
| 0.1079 | 16.27 | 5400 | 0.1027 | 0.9593 | 0.9593 |
| 0.1017 | 16.87 | 5600 | 0.1010 | 0.9597 | 0.9597 |
| 0.1065 | 17.47 | 5800 | 0.0994 | 0.9600 | 0.9601 |
| 0.1092 | 18.07 | 6000 | 0.0988 | 0.9600 | 0.9601 |
| 0.1059 | 18.67 | 6200 | 0.0993 | 0.9606 | 0.9606 |
| 0.1059 | 19.28 | 6400 | 0.1002 | 0.9608 | 0.9608 |
| 0.102 | 19.88 | 6600 | 0.1012 | 0.9610 | 0.9610 |
| 0.1073 | 20.48 | 6800 | 0.1008 | 0.9595 | 0.9595 |
| 0.0999 | 21.08 | 7000 | 0.0996 | 0.9600 | 0.9601 |
| 0.1031 | 21.69 | 7200 | 0.1016 | 0.9595 | 0.9595 |
| 0.1025 | 22.29 | 7400 | 0.1003 | 0.9593 | 0.9593 |
| 0.1042 | 22.89 | 7600 | 0.0990 | 0.9599 | 0.9599 |
| 0.1001 | 23.49 | 7800 | 0.0998 | 0.9599 | 0.9599 |
| 0.1021 | 24.1 | 8000 | 0.0995 | 0.9608 | 0.9608 |
| 0.1017 | 24.7 | 8200 | 0.0989 | 0.9606 | 0.9606 |
| 0.1015 | 25.3 | 8400 | 0.0985 | 0.9608 | 0.9608 |
| 0.1007 | 25.9 | 8600 | 0.0991 | 0.9608 | 0.9608 |
| 0.1007 | 26.51 | 8800 | 0.0989 | 0.9610 | 0.9610 |
| 0.1012 | 27.11 | 9000 | 0.0982 | 0.9606 | 0.9606 |
| 0.0987 | 27.71 | 9200 | 0.0984 | 0.9608 | 0.9608 |
| 0.0965 | 28.31 | 9400 | 0.0987 | 0.9606 | 0.9606 |
| 0.1031 | 28.92 | 9600 | 0.0987 | 0.9606 | 0.9606 |
| 0.0962 | 29.52 | 9800 | 0.0985 | 0.9602 | 0.9602 |
| 0.0993 | 30.12 | 10000 | 0.0984 | 0.9606 | 0.9606 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_65536_512_47M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_65536_512_47M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:10:00+00:00 |
null | null | {} | sarahahtee/signwriting-illustration | null | [
"region:us"
] | null | 2024-05-03T15:10:20+00:00 |
|
null | null |
<!-- 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. -->
# falcon7binstruct_mentalhealthmodel_oct23
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- 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: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.0
- Tokenizers 0.13.2
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "vilsonrodrigues/falcon-7b-instruct-sharded", "model-index": [{"name": "falcon7binstruct_mentalhealthmodel_oct23", "results": []}]} | KingMidas89/falcon7binstruct_mentalhealthmodel_oct23 | null | [
"trl",
"sft",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T15:10:42+00:00 |
null | null | {"license": "wtfpl"} | Poomz/pooomz | null | [
"license:wtfpl",
"region:us"
] | null | 2024-05-03T15:10:57+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** rvian
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | rvian/lora-llama3-midjourney-prompt-generator | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:11:31+00:00 |
null | null | {} | just1nseo/tulu2-13b-cost-tulumix-5e-7 | null | [
"safetensors",
"region:us"
] | null | 2024-05-03T15:11:43+00:00 |
|
null | null | {} | just1nseo/tulu2-13b-cost-tulumix-5e-6 | null | [
"safetensors",
"region:us"
] | null | 2024-05-03T15:11:57+00:00 |
|
null | null | {} | just1nseo/tulu2-13b-cost-tulumix-1e-6 | null | [
"safetensors",
"region:us"
] | null | 2024-05-03T15:12:35+00:00 |
|
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | Mustfain/News-headline-Generation | null | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:13:32+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/SAN-235 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:13:48+00:00 |
null | transformers |
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| {"library_name": "transformers", "tags": []} | ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.05_8_0.0002 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:13:49+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/SAN-236 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:13:51+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | mayukhbis/llama3-gguf | null | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:14:38+00:00 |
null | diffusers | {} | xkronosx/train_prior_celeb_unet | null | [
"diffusers",
"safetensors",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-05-03T15:14:53+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# gptneo-TxtToJson-v0.3.1
This model is a fine-tuned version of [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1921
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9333 | 1.0 | 219 | 0.9018 |
| 0.6128 | 2.0 | 438 | 0.5939 |
| 0.3284 | 3.0 | 657 | 0.3776 |
| 0.1879 | 4.0 | 876 | 0.2272 |
| 0.1326 | 5.0 | 1095 | 0.1921 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
| {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gptneo-TxtToJson-v0.3.1", "results": []}]} | AhmedTaha012/gptneo-TxtToJson-v0.3.1 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:15:10+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** waylandzhang
- **License:** apache-2.0
- **Finetuned from model :** Llama-3-8b-Chinese-Novel-4bit-lesson-v2
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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "Llama-3-8b-Chinese-Novel-4bit-lesson-v2"} | waylandzhang/Llama-3-8b-Chinese-Novel-4bit-lesson-v2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:Llama-3-8b-Chinese-Novel-4bit-lesson-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-05-03T15:15:13+00:00 |
text-classification | transformers | {} | macadeliccc/distilbert-base-uncasedon-off-v2 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:15:34+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[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 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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| {"library_name": "transformers", "tags": []} | golf2248/9x70bu1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T15:15:34+00:00 |
null | peft |
<!-- 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. -->
# GUE_prom_prom_core_all-seqsight_65536_512_47M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4257
- F1 Score: 0.8007
- Accuracy: 0.8008
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.549 | 0.54 | 200 | 0.4976 | 0.7582 | 0.7595 |
| 0.487 | 1.08 | 400 | 0.4718 | 0.7750 | 0.7752 |
| 0.4771 | 1.62 | 600 | 0.4630 | 0.7801 | 0.7801 |
| 0.4668 | 2.16 | 800 | 0.4605 | 0.7807 | 0.7807 |
| 0.4679 | 2.7 | 1000 | 0.4629 | 0.7809 | 0.7812 |
| 0.4644 | 3.24 | 1200 | 0.4596 | 0.7829 | 0.7833 |
| 0.4585 | 3.78 | 1400 | 0.4593 | 0.7808 | 0.7812 |
| 0.4543 | 4.32 | 1600 | 0.4668 | 0.7818 | 0.7826 |
| 0.4524 | 4.86 | 1800 | 0.4616 | 0.7837 | 0.7843 |
| 0.4524 | 5.41 | 2000 | 0.4568 | 0.7876 | 0.7880 |
| 0.4494 | 5.95 | 2200 | 0.4508 | 0.7900 | 0.7900 |
| 0.4502 | 6.49 | 2400 | 0.4500 | 0.7899 | 0.7900 |
| 0.4417 | 7.03 | 2600 | 0.4465 | 0.7914 | 0.7914 |
| 0.4475 | 7.57 | 2800 | 0.4516 | 0.7919 | 0.7921 |
| 0.4415 | 8.11 | 3000 | 0.4528 | 0.7868 | 0.7873 |
| 0.4412 | 8.65 | 3200 | 0.4449 | 0.7936 | 0.7936 |
| 0.4447 | 9.19 | 3400 | 0.4458 | 0.7918 | 0.7919 |
| 0.4368 | 9.73 | 3600 | 0.4534 | 0.7882 | 0.7889 |
| 0.4416 | 10.27 | 3800 | 0.4480 | 0.7890 | 0.7894 |
| 0.4402 | 10.81 | 4000 | 0.4432 | 0.7936 | 0.7936 |
| 0.4403 | 11.35 | 4200 | 0.4446 | 0.7922 | 0.7924 |
| 0.4375 | 11.89 | 4400 | 0.4480 | 0.7916 | 0.7921 |
| 0.4358 | 12.43 | 4600 | 0.4401 | 0.7973 | 0.7973 |
| 0.4337 | 12.97 | 4800 | 0.4428 | 0.7934 | 0.7936 |
| 0.4349 | 13.51 | 5000 | 0.4518 | 0.7875 | 0.7885 |
| 0.433 | 14.05 | 5200 | 0.4425 | 0.7932 | 0.7934 |
| 0.4319 | 14.59 | 5400 | 0.4393 | 0.7973 | 0.7973 |
| 0.4317 | 15.14 | 5600 | 0.4396 | 0.7976 | 0.7976 |
| 0.4326 | 15.68 | 5800 | 0.4442 | 0.7919 | 0.7924 |
| 0.4274 | 16.22 | 6000 | 0.4443 | 0.7920 | 0.7924 |
| 0.4354 | 16.76 | 6200 | 0.4405 | 0.7941 | 0.7944 |
| 0.4318 | 17.3 | 6400 | 0.4427 | 0.7921 | 0.7926 |
| 0.4311 | 17.84 | 6600 | 0.4425 | 0.7918 | 0.7922 |
| 0.4299 | 18.38 | 6800 | 0.4439 | 0.7905 | 0.7912 |
| 0.4295 | 18.92 | 7000 | 0.4402 | 0.7951 | 0.7954 |
| 0.423 | 19.46 | 7200 | 0.4400 | 0.7978 | 0.7980 |
| 0.434 | 20.0 | 7400 | 0.4384 | 0.7968 | 0.7970 |
| 0.4285 | 20.54 | 7600 | 0.4409 | 0.7936 | 0.7939 |
| 0.4247 | 21.08 | 7800 | 0.4419 | 0.7958 | 0.7961 |
| 0.4277 | 21.62 | 8000 | 0.4382 | 0.7955 | 0.7956 |
| 0.4288 | 22.16 | 8200 | 0.4382 | 0.7982 | 0.7983 |
| 0.4335 | 22.7 | 8400 | 0.4407 | 0.7933 | 0.7937 |
| 0.4244 | 23.24 | 8600 | 0.4383 | 0.7951 | 0.7953 |
| 0.4243 | 23.78 | 8800 | 0.4388 | 0.7963 | 0.7965 |
| 0.4248 | 24.32 | 9000 | 0.4377 | 0.7982 | 0.7983 |
| 0.4276 | 24.86 | 9200 | 0.4377 | 0.7975 | 0.7976 |
| 0.4387 | 25.41 | 9400 | 0.4372 | 0.7958 | 0.7959 |
| 0.4208 | 25.95 | 9600 | 0.4376 | 0.7968 | 0.7970 |
| 0.4303 | 26.49 | 9800 | 0.4389 | 0.7959 | 0.7961 |
| 0.4243 | 27.03 | 10000 | 0.4384 | 0.7962 | 0.7965 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_65536_512_47M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_65536_512_47M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:16:03+00:00 |
null | null | {} | just1nseo/tulu2-13b-cost-tulumix-5e-7-nojudge | null | [
"safetensors",
"region:us"
] | null | 2024-05-03T15:16:24+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_prom_prom_core_all-seqsight_65536_512_47M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4193
- F1 Score: 0.8054
- Accuracy: 0.8056
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.526 | 0.54 | 200 | 0.4758 | 0.7691 | 0.7698 |
| 0.4709 | 1.08 | 400 | 0.4679 | 0.7773 | 0.7779 |
| 0.4604 | 1.62 | 600 | 0.4528 | 0.7853 | 0.7853 |
| 0.4477 | 2.16 | 800 | 0.4555 | 0.7883 | 0.7885 |
| 0.4485 | 2.7 | 1000 | 0.4553 | 0.7842 | 0.7850 |
| 0.4449 | 3.24 | 1200 | 0.4446 | 0.7877 | 0.7880 |
| 0.4387 | 3.78 | 1400 | 0.4491 | 0.7882 | 0.7890 |
| 0.4322 | 4.32 | 1600 | 0.4488 | 0.7902 | 0.7909 |
| 0.4316 | 4.86 | 1800 | 0.4496 | 0.7901 | 0.7909 |
| 0.4319 | 5.41 | 2000 | 0.4445 | 0.7915 | 0.7921 |
| 0.4288 | 5.95 | 2200 | 0.4381 | 0.7986 | 0.7986 |
| 0.4292 | 6.49 | 2400 | 0.4365 | 0.7974 | 0.7975 |
| 0.4201 | 7.03 | 2600 | 0.4383 | 0.7981 | 0.7981 |
| 0.4242 | 7.57 | 2800 | 0.4390 | 0.7993 | 0.7993 |
| 0.4205 | 8.11 | 3000 | 0.4402 | 0.7938 | 0.7944 |
| 0.4195 | 8.65 | 3200 | 0.4349 | 0.7990 | 0.7992 |
| 0.4236 | 9.19 | 3400 | 0.4336 | 0.8013 | 0.8014 |
| 0.4177 | 9.73 | 3600 | 0.4404 | 0.7960 | 0.7965 |
| 0.4211 | 10.27 | 3800 | 0.4361 | 0.7984 | 0.7986 |
| 0.4178 | 10.81 | 4000 | 0.4395 | 0.7978 | 0.7978 |
| 0.4184 | 11.35 | 4200 | 0.4357 | 0.8010 | 0.8012 |
| 0.416 | 11.89 | 4400 | 0.4357 | 0.7957 | 0.7963 |
| 0.4147 | 12.43 | 4600 | 0.4314 | 0.8011 | 0.8012 |
| 0.4137 | 12.97 | 4800 | 0.4320 | 0.8023 | 0.8024 |
| 0.4148 | 13.51 | 5000 | 0.4417 | 0.7896 | 0.7909 |
| 0.4119 | 14.05 | 5200 | 0.4309 | 0.8016 | 0.8017 |
| 0.4099 | 14.59 | 5400 | 0.4304 | 0.8017 | 0.8017 |
| 0.4126 | 15.14 | 5600 | 0.4311 | 0.8008 | 0.8008 |
| 0.411 | 15.68 | 5800 | 0.4394 | 0.7957 | 0.7966 |
| 0.4071 | 16.22 | 6000 | 0.4338 | 0.8024 | 0.8029 |
| 0.4131 | 16.76 | 6200 | 0.4273 | 0.8024 | 0.8025 |
| 0.4118 | 17.3 | 6400 | 0.4299 | 0.8017 | 0.8020 |
| 0.4103 | 17.84 | 6600 | 0.4301 | 0.8021 | 0.8024 |
| 0.4071 | 18.38 | 6800 | 0.4381 | 0.7940 | 0.7951 |
| 0.4084 | 18.92 | 7000 | 0.4289 | 0.8024 | 0.8027 |
| 0.4003 | 19.46 | 7200 | 0.4300 | 0.8045 | 0.8047 |
| 0.412 | 20.0 | 7400 | 0.4284 | 0.8041 | 0.8042 |
| 0.4064 | 20.54 | 7600 | 0.4316 | 0.8042 | 0.8046 |
| 0.4029 | 21.08 | 7800 | 0.4307 | 0.8054 | 0.8056 |
| 0.4037 | 21.62 | 8000 | 0.4268 | 0.8032 | 0.8032 |
| 0.4058 | 22.16 | 8200 | 0.4276 | 0.8044 | 0.8046 |
| 0.4105 | 22.7 | 8400 | 0.4313 | 0.8029 | 0.8034 |
| 0.401 | 23.24 | 8600 | 0.4283 | 0.8025 | 0.8027 |
| 0.4021 | 23.78 | 8800 | 0.4289 | 0.8032 | 0.8034 |
| 0.4006 | 24.32 | 9000 | 0.4281 | 0.8038 | 0.8039 |
| 0.4035 | 24.86 | 9200 | 0.4288 | 0.8032 | 0.8034 |
| 0.4139 | 25.41 | 9400 | 0.4271 | 0.8031 | 0.8032 |
| 0.3975 | 25.95 | 9600 | 0.4281 | 0.8038 | 0.8039 |
| 0.4074 | 26.49 | 9800 | 0.4291 | 0.8030 | 0.8032 |
| 0.4027 | 27.03 | 10000 | 0.4288 | 0.8034 | 0.8035 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_65536_512_47M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_65536_512_47M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:18:45+00:00 |
null | peft |
<!-- 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. -->
# GUE_prom_prom_core_all-seqsight_65536_512_47M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4156
- F1 Score: 0.8109
- Accuracy: 0.8110
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5132 | 0.54 | 200 | 0.4606 | 0.7790 | 0.7791 |
| 0.4625 | 1.08 | 400 | 0.4707 | 0.7759 | 0.7772 |
| 0.4478 | 1.62 | 600 | 0.4477 | 0.7905 | 0.7907 |
| 0.4355 | 2.16 | 800 | 0.4496 | 0.7915 | 0.7919 |
| 0.4368 | 2.7 | 1000 | 0.4496 | 0.7880 | 0.7892 |
| 0.433 | 3.24 | 1200 | 0.4341 | 0.7970 | 0.7971 |
| 0.4274 | 3.78 | 1400 | 0.4410 | 0.7922 | 0.7931 |
| 0.4195 | 4.32 | 1600 | 0.4366 | 0.7994 | 0.7997 |
| 0.422 | 4.86 | 1800 | 0.4424 | 0.7957 | 0.7965 |
| 0.42 | 5.41 | 2000 | 0.4373 | 0.7984 | 0.7990 |
| 0.4181 | 5.95 | 2200 | 0.4332 | 0.8020 | 0.8020 |
| 0.4171 | 6.49 | 2400 | 0.4306 | 0.8014 | 0.8015 |
| 0.4083 | 7.03 | 2600 | 0.4315 | 0.8081 | 0.8081 |
| 0.4111 | 7.57 | 2800 | 0.4338 | 0.8024 | 0.8024 |
| 0.4086 | 8.11 | 3000 | 0.4315 | 0.8030 | 0.8034 |
| 0.4062 | 8.65 | 3200 | 0.4297 | 0.8022 | 0.8025 |
| 0.4087 | 9.19 | 3400 | 0.4271 | 0.8037 | 0.8037 |
| 0.404 | 9.73 | 3600 | 0.4336 | 0.8026 | 0.8030 |
| 0.406 | 10.27 | 3800 | 0.4313 | 0.8051 | 0.8054 |
| 0.4013 | 10.81 | 4000 | 0.4364 | 0.8054 | 0.8054 |
| 0.4023 | 11.35 | 4200 | 0.4277 | 0.8066 | 0.8068 |
| 0.3986 | 11.89 | 4400 | 0.4297 | 0.8014 | 0.8019 |
| 0.3979 | 12.43 | 4600 | 0.4287 | 0.8071 | 0.8071 |
| 0.3977 | 12.97 | 4800 | 0.4271 | 0.8047 | 0.8047 |
| 0.3981 | 13.51 | 5000 | 0.4321 | 0.8036 | 0.8044 |
| 0.3933 | 14.05 | 5200 | 0.4248 | 0.8083 | 0.8083 |
| 0.3888 | 14.59 | 5400 | 0.4270 | 0.8068 | 0.8069 |
| 0.3943 | 15.14 | 5600 | 0.4270 | 0.8072 | 0.8073 |
| 0.3905 | 15.68 | 5800 | 0.4352 | 0.8020 | 0.8029 |
| 0.3869 | 16.22 | 6000 | 0.4285 | 0.8054 | 0.8057 |
| 0.393 | 16.76 | 6200 | 0.4206 | 0.8074 | 0.8076 |
| 0.3903 | 17.3 | 6400 | 0.4256 | 0.8080 | 0.8083 |
| 0.3896 | 17.84 | 6600 | 0.4255 | 0.8083 | 0.8086 |
| 0.3859 | 18.38 | 6800 | 0.4339 | 0.8035 | 0.8044 |
| 0.3861 | 18.92 | 7000 | 0.4214 | 0.8095 | 0.8098 |
| 0.3767 | 19.46 | 7200 | 0.4267 | 0.8053 | 0.8056 |
| 0.3911 | 20.0 | 7400 | 0.4236 | 0.8093 | 0.8095 |
| 0.3823 | 20.54 | 7600 | 0.4286 | 0.8060 | 0.8064 |
| 0.3793 | 21.08 | 7800 | 0.4268 | 0.8106 | 0.8108 |
| 0.3811 | 21.62 | 8000 | 0.4190 | 0.8094 | 0.8095 |
| 0.3812 | 22.16 | 8200 | 0.4225 | 0.8069 | 0.8071 |
| 0.3844 | 22.7 | 8400 | 0.4288 | 0.8070 | 0.8074 |
| 0.3786 | 23.24 | 8600 | 0.4225 | 0.8086 | 0.8088 |
| 0.3761 | 23.78 | 8800 | 0.4261 | 0.8088 | 0.8090 |
| 0.3754 | 24.32 | 9000 | 0.4253 | 0.8090 | 0.8091 |
| 0.3777 | 24.86 | 9200 | 0.4252 | 0.8076 | 0.8078 |
| 0.3854 | 25.41 | 9400 | 0.4232 | 0.8078 | 0.8079 |
| 0.3738 | 25.95 | 9600 | 0.4243 | 0.8088 | 0.8090 |
| 0.3793 | 26.49 | 9800 | 0.4254 | 0.8087 | 0.8090 |
| 0.3786 | 27.03 | 10000 | 0.4251 | 0.8084 | 0.8086 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_65536_512_47M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_65536_512_47M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:19:37+00:00 |
text-classification | transformers |
# Model Card for Model ID
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## 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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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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[More Information Needed] | {"library_name": "transformers", "tags": []} | quangtqv/cross_encoder_tool_learning_v1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T15:19:56+00:00 |
null | peft |
<!-- 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. -->
# GUE_prom_prom_core_notata-seqsight_65536_512_47M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_65536_512_47M](https://huggingface.co/mahdibaghbanzadeh/seqsight_65536_512_47M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3864
- F1 Score: 0.8244
- Accuracy: 0.8244
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5401 | 0.6 | 200 | 0.4229 | 0.8010 | 0.8012 |
| 0.4555 | 1.2 | 400 | 0.4026 | 0.8174 | 0.8174 |
| 0.4417 | 1.81 | 600 | 0.3950 | 0.8208 | 0.8208 |
| 0.4392 | 2.41 | 800 | 0.3931 | 0.8234 | 0.8234 |
| 0.4271 | 3.01 | 1000 | 0.3877 | 0.8263 | 0.8263 |
| 0.4232 | 3.61 | 1200 | 0.3867 | 0.8320 | 0.8321 |
| 0.4263 | 4.22 | 1400 | 0.3861 | 0.8296 | 0.8297 |
| 0.4219 | 4.82 | 1600 | 0.3821 | 0.8308 | 0.8308 |
| 0.4145 | 5.42 | 1800 | 0.3790 | 0.8338 | 0.8338 |
| 0.4172 | 6.02 | 2000 | 0.3822 | 0.8308 | 0.8312 |
| 0.4138 | 6.63 | 2200 | 0.3785 | 0.8304 | 0.8306 |
| 0.4095 | 7.23 | 2400 | 0.3788 | 0.8330 | 0.8334 |
| 0.408 | 7.83 | 2600 | 0.3765 | 0.8356 | 0.8359 |
| 0.4016 | 8.43 | 2800 | 0.3857 | 0.8306 | 0.8314 |
| 0.4067 | 9.04 | 3000 | 0.3784 | 0.8337 | 0.8342 |
| 0.4005 | 9.64 | 3200 | 0.3701 | 0.8386 | 0.8387 |
| 0.4027 | 10.24 | 3400 | 0.3698 | 0.8371 | 0.8372 |
| 0.3984 | 10.84 | 3600 | 0.3687 | 0.8366 | 0.8366 |
| 0.3998 | 11.45 | 3800 | 0.3714 | 0.8399 | 0.8400 |
| 0.3972 | 12.05 | 4000 | 0.3693 | 0.8397 | 0.8398 |
| 0.3943 | 12.65 | 4200 | 0.3703 | 0.8397 | 0.8398 |
| 0.4001 | 13.25 | 4400 | 0.3704 | 0.8373 | 0.8374 |
| 0.3998 | 13.86 | 4600 | 0.3683 | 0.8394 | 0.8395 |
| 0.3954 | 14.46 | 4800 | 0.3684 | 0.8396 | 0.8396 |
| 0.3926 | 15.06 | 5000 | 0.3705 | 0.8364 | 0.8364 |
| 0.3924 | 15.66 | 5200 | 0.3683 | 0.8398 | 0.8398 |
| 0.3911 | 16.27 | 5400 | 0.3681 | 0.8392 | 0.8393 |
| 0.3924 | 16.87 | 5600 | 0.3793 | 0.8339 | 0.8346 |
| 0.3892 | 17.47 | 5800 | 0.3756 | 0.8325 | 0.8331 |
| 0.397 | 18.07 | 6000 | 0.3725 | 0.8377 | 0.8381 |
| 0.3895 | 18.67 | 6200 | 0.3694 | 0.8389 | 0.8391 |
| 0.3988 | 19.28 | 6400 | 0.3660 | 0.8396 | 0.8396 |
| 0.3886 | 19.88 | 6600 | 0.3688 | 0.8385 | 0.8387 |
| 0.3878 | 20.48 | 6800 | 0.3704 | 0.8387 | 0.8389 |
| 0.3956 | 21.08 | 7000 | 0.3712 | 0.8391 | 0.8395 |
| 0.392 | 21.69 | 7200 | 0.3669 | 0.8409 | 0.8410 |
| 0.3875 | 22.29 | 7400 | 0.3689 | 0.8395 | 0.8396 |
| 0.3884 | 22.89 | 7600 | 0.3674 | 0.8399 | 0.8400 |
| 0.3894 | 23.49 | 7800 | 0.3684 | 0.8393 | 0.8395 |
| 0.3905 | 24.1 | 8000 | 0.3692 | 0.8371 | 0.8374 |
| 0.3915 | 24.7 | 8200 | 0.3718 | 0.8374 | 0.8378 |
| 0.3847 | 25.3 | 8400 | 0.3688 | 0.8385 | 0.8387 |
| 0.392 | 25.9 | 8600 | 0.3667 | 0.8388 | 0.8389 |
| 0.3841 | 26.51 | 8800 | 0.3659 | 0.8409 | 0.8410 |
| 0.3908 | 27.11 | 9000 | 0.3665 | 0.8401 | 0.8402 |
| 0.395 | 27.71 | 9200 | 0.3668 | 0.8399 | 0.8400 |
| 0.381 | 28.31 | 9400 | 0.3687 | 0.8379 | 0.8381 |
| 0.3861 | 28.92 | 9600 | 0.3671 | 0.8401 | 0.8402 |
| 0.389 | 29.52 | 9800 | 0.3668 | 0.8397 | 0.8398 |
| 0.3852 | 30.12 | 10000 | 0.3671 | 0.8395 | 0.8396 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_65536_512_47M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_65536_512_47M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_65536_512_47M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_65536_512_47M",
"region:us"
] | null | 2024-05-03T15:20:43+00:00 |
Subsets and Splits