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# Uploaded model
- **Developed by:** Parssky
- **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", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Parssky/Llama3-8B-TechnicalReport-bf16_GGUF | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:04:45+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. -->
# dinov2-large-prova3-drone-2024_05_02-with_data_aug_batch-size32_epochs100_freeze
DinoVd'eau is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the multilabel_complete_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2945
- F1 Micro: 0.7964
- F1 Macro: 0.5349
- Roc Auc: 0.8466
- Accuracy: 0.2347
- Learning Rate: 1e-05
## Model description
DinoVd'eau is a model built on top of dinov2 model for underwater multilabel image classification.The classification head is a combination of linear, ReLU, batch normalization, and dropout layers.
- **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)
## Intended uses & limitations
You can use the raw model for classify diverse marine species, encompassing coral morphotypes classes taken from the Global Coral Reef Monitoring Network (GCRMN), habitats classes and seagrass species.
## Training and evaluation data
Details on the number of images for each class are given in the following table:
| |train |val |test |Total |
|--- | --- | --- | --- | --- |
| Acropore_branched | 884 | 293 | 296 | 1473 |
| Acropore_digitised | 220 | 84 | 70 | 374 |
| Acropore_sub_massive | 219 | 101 | 108 | 428 |
| Acropore_tabular | 178 | 69 | 52 | 299 |
| Algae_assembly | 1712 | 558 | 546 | 2816 |
| Algae_drawn_up | 219 | 101 | 108 | 428 |
| Algae_limestone | 218 | 59 | 74 | 351 |
| Algae_sodding | 1228 | 403 | 412 | 2043 |
| Bleached_coral | 219 | 101 | 108 | 428 |
| Dead_coral | 1192 | 393 | 392 | 1977 |
| Fish | 974 | 336 | 328 | 1638 |
| No_acropore_massive | 340 | 127 | 108 | 575 |
| No_acropore_sub_massive | 739 | 244 | 233 | 1216 |
| Rock | 2814 | 935 | 937 | 4686 |
| Sand | 2807 | 936 | 935 | 4678 |
| Scrap | 2565 | 822 | 846 | 4233 |
## Training procedure
### Data Augmentation
Data were augmented using the following transformations :
- training transformations : Sequential(
(0): PreProcess()
(1): Resize(output_size=(159, 159), p=1.0, p_batch=1.0, same_on_batch=True, size=(159, 159), side=short, resample=bilinear, align_corners=True, antialias=False)
(2): RandomHorizontalFlip(p=0.25, p_batch=1.0, same_on_batch=False)
(3): RandomVerticalFlip(p=0.25, p_batch=1.0, same_on_batch=False)
(4): ColorJiggle(brightness=0.0, contrast=0.0, saturation=0.0, hue=0.0, p=0.25, p_batch=1.0, same_on_batch=False)
(5): RandomPerspective(distortion_scale=0.5, p=0.25, p_batch=1.0, same_on_batch=False, align_corners=False, resample=bilinear)
(6): Normalize(p=1.0, p_batch=1.0, same_on_batch=True, mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250]))
)
- validation transformations : Sequential(
(0): PreProcess()
(1): Resize(output_size=(159, 159), p=1.0, p_batch=1.0, same_on_batch=True, size=(159, 159), side=short, resample=bilinear, align_corners=True, antialias=False)
(2): Normalize(p=1.0, p_batch=1.0, same_on_batch=True, mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250]))
)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
- freeze_encoder: True
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:------:|
| No log | 1.0 | 107 | 0.4159 | 0.7692 | 0.5071 | 0.8330 | 0.1710 | 0.001 |
| No log | 2.0 | 214 | 0.3285 | 0.7729 | 0.4613 | 0.8288 | 0.1880 | 0.001 |
| No log | 3.0 | 321 | 0.3240 | 0.7751 | 0.4660 | 0.8311 | 0.1871 | 0.001 |
| No log | 4.0 | 428 | 0.3120 | 0.7876 | 0.5069 | 0.8415 | 0.1862 | 0.001 |
| 0.3847 | 5.0 | 535 | 0.3163 | 0.7831 | 0.4977 | 0.8372 | 0.1737 | 0.001 |
| 0.3847 | 6.0 | 642 | 0.3099 | 0.7868 | 0.4944 | 0.8393 | 0.1898 | 0.001 |
| 0.3847 | 7.0 | 749 | 0.3092 | 0.7892 | 0.5105 | 0.8419 | 0.1889 | 0.001 |
| 0.3847 | 8.0 | 856 | 0.3131 | 0.7896 | 0.5561 | 0.8444 | 0.1880 | 0.001 |
| 0.3847 | 9.0 | 963 | 0.3076 | 0.7870 | 0.5449 | 0.8404 | 0.1808 | 0.001 |
| 0.3097 | 10.0 | 1070 | 0.3084 | 0.7884 | 0.4930 | 0.8409 | 0.1996 | 0.001 |
| 0.3097 | 11.0 | 1177 | 0.3065 | 0.7861 | 0.5140 | 0.8383 | 0.1907 | 0.001 |
| 0.3097 | 12.0 | 1284 | 0.3066 | 0.7858 | 0.5194 | 0.8384 | 0.1853 | 0.001 |
| 0.3097 | 13.0 | 1391 | 0.3058 | 0.7928 | 0.5252 | 0.8449 | 0.1898 | 0.001 |
| 0.3097 | 14.0 | 1498 | 0.3031 | 0.7938 | 0.5230 | 0.8456 | 0.1952 | 0.001 |
| 0.2993 | 15.0 | 1605 | 0.3087 | 0.7894 | 0.5194 | 0.8412 | 0.2023 | 0.001 |
| 0.2993 | 16.0 | 1712 | 0.3117 | 0.7814 | 0.5141 | 0.8337 | 0.1943 | 0.001 |
| 0.2993 | 17.0 | 1819 | 0.3129 | 0.7922 | 0.5232 | 0.8435 | 0.1961 | 0.001 |
| 0.2993 | 18.0 | 1926 | 0.3055 | 0.7911 | 0.5242 | 0.8424 | 0.2059 | 0.001 |
| 0.2952 | 19.0 | 2033 | 0.3077 | 0.7888 | 0.5164 | 0.8411 | 0.1979 | 0.001 |
| 0.2952 | 20.0 | 2140 | 0.3041 | 0.7933 | 0.5229 | 0.8458 | 0.1952 | 0.001 |
| 0.2952 | 21.0 | 2247 | 0.2988 | 0.7945 | 0.5344 | 0.8458 | 0.1916 | 0.0001 |
| 0.2952 | 22.0 | 2354 | 0.2985 | 0.7941 | 0.5420 | 0.8462 | 0.1907 | 0.0001 |
| 0.2952 | 23.0 | 2461 | 0.2991 | 0.7916 | 0.5316 | 0.8440 | 0.1898 | 0.0001 |
| 0.2823 | 24.0 | 2568 | 0.3017 | 0.7930 | 0.5312 | 0.8445 | 0.1934 | 0.0001 |
| 0.2823 | 25.0 | 2675 | 0.3015 | 0.7936 | 0.5404 | 0.8455 | 0.1961 | 0.0001 |
| 0.2823 | 26.0 | 2782 | 0.3005 | 0.7927 | 0.5416 | 0.8449 | 0.1916 | 0.0001 |
| 0.2823 | 27.0 | 2889 | 0.2994 | 0.7931 | 0.5498 | 0.8452 | 0.1952 | 0.0001 |
| 0.2823 | 28.0 | 2996 | 0.2998 | 0.7920 | 0.5446 | 0.8437 | 0.1961 | 0.0001 |
| 0.2722 | 29.0 | 3103 | 0.2991 | 0.7922 | 0.5452 | 0.8437 | 0.1961 | 1e-05 |
| 0.2722 | 30.0 | 3210 | 0.2985 | 0.7918 | 0.5425 | 0.8435 | 0.1961 | 1e-05 |
| 0.2722 | 31.0 | 3317 | 0.2997 | 0.7926 | 0.5476 | 0.8447 | 0.1889 | 1e-05 |
| 0.2722 | 32.0 | 3424 | 0.2994 | 0.7933 | 0.5493 | 0.8453 | 0.1853 | 1e-05 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0
| {"language": ["eng"], "license": "apache-2.0", "tags": ["multilabel-image-classification", "multilabel", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/dinov2-large", "model-index": [{"name": "dinov2-large-prova3-drone-2024_05_02-with_data_aug_batch-size32_epochs100_freeze", "results": []}]} | lombardata/dinov2-large-prova3-drone-2024_05_02-with_data_aug_batch-size32_epochs100_freeze | null | [
"transformers",
"tensorboard",
"safetensors",
"dinov2",
"multilabel-image-classification",
"multilabel",
"generated_from_trainer",
"eng",
"base_model:facebook/dinov2-large",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:04:59+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
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#### 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. -->
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## Glossary [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | ekle-me/gemma-2b-it-EBA-finetune-106 | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:05:23+00:00 |
text2text-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. -->
# t5-base-finetuned-testcase
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 5 | 3.0276 | 20.8462 | 6.2353 | 14.3336 | 16.8951 | 19.0 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "t5-base", "model-index": [{"name": "t5-base-finetuned-testcase", "results": []}]} | ridhu-s/t5-base-finetuned-testcase | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:06:01+00:00 |
null | null | Model которая основана на
[LLaMA-2](https://ai.meta.com/llama/) и дообучена на корпусах, сгенерированных ChatGPT, таких как
[ru_turbo_alpaca](https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca),
[ru_turbo_saiga](https://huggingface.co/datasets/IlyaGusev/ru_turbo_saiga) и
[gpt_roleplay_realm](https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm) | {"license": "mit"} | julicq/model-q4_K | null | [
"gguf",
"license:mit",
"region:us"
] | null | 2024-05-02T10:06:02+00:00 |
null | null | {"license": "mit"} | vinay235/llama-3-8b-monhack | null | [
"safetensors",
"license:mit",
"region:us"
] | null | 2024-05-02T10:06:15+00:00 |
|
null | null | {} | automated-finetunning/bart_mohit_test | null | [
"region:us"
] | null | 2024-05-02T10:07:30+00:00 |
|
null | null | {} | Poojithpoosa/AIhumandetect | null | [
"region:us"
] | null | 2024-05-02T10:07:48+00:00 |
|
null | null |
# LlamaHermes-7B
LlamaHermes-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: DeepMount00/Llama-3-8b-Ita
- model: NousResearch/Hermes-2-Pro-Llama-3-8B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/LlamaHermes-7B"
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"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/LlamaHermes-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T10:08:14+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# avaimon/t5-summarizer
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.1356
- Validation Loss: 2.4205
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 60960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.1356 | 2.4205 | 0 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google/mt5-small", "model-index": [{"name": "avaimon/t5-summarizer", "results": []}]} | avaimon/t5-summarizer | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:08:39+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** LLMalberto
- **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", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | LLMalberto/llama3-8b-unsloth-llmmaster | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:08:46+00:00 |
null | null | {} | magicalwhisper/mainnet | null | [
"region:us"
] | null | 2024-05-02T10:10:13+00:00 |
|
null | transformers | {} | Rasi1610/Deathce502_series1_n7 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:10:32+00:00 |
|
null | null | {} | automated-finetunning/bart_mohit_test1 | null | [
"region:us"
] | null | 2024-05-02T10:11:04+00:00 |
|
null | diffusers | {} | godlzj/BrushNet | null | [
"diffusers",
"safetensors",
"region:us"
] | null | 2024-05-02T10:11:30+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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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## Bias, Risks, and Limitations
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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### 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]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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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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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | Rhma/mistral_7b_fine-tuned-compt2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T10:12:25+00:00 |
null | null | {} | OpenBuddy/openbuddy-mixtral-22bx8-v21.1-65k | null | [
"region:us"
] | null | 2024-05-02T10:14:07+00:00 |
|
automatic-speech-recognition | 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. -->
# wav2vec2-vocals
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1234
- Cer: 0.2848
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 6.9359 | 3.6496 | 500 | 2.8329 | 0.9873 |
| 1.1168 | 7.2993 | 1000 | 1.0561 | 0.3050 |
| 0.3139 | 10.9489 | 1500 | 1.1234 | 0.2848 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.0.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-vocals", "results": []}]} | hongseongpil/wav2vec2-vocals | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:14:09+00:00 |
null | null | {} | optimum-internal-testing/optimum-neuron-cache-for-testing-wepck | null | [
"region:us"
] | null | 2024-05-02T10:14:15+00:00 |
|
text-generation | transformers | {} | sureshbabus/Llama-2-7b-chat-finetune | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:14:28+00:00 |
|
null | null | # Convection Parameterization in CAM
Note that this repository and code is still work in progress and undergoing significant development.
Once a useable release is produced it will be tagged.
## Description
This repository contains code as part of an effort to deploy machine learning (ML) models of geophysical parameterisations into the [Community Earth System Model (CESM)](https://www.cesm.ucar.edu/).
This work is part of the [M<sup>2</sup>LInES](https://m2lines.github.io/) project aiming to improve performance of climate models using ML models for subgrid parameterizations.
A Neural Net providing a subgrid parameterization of atmospheric convection in a [single column model](https://www.arm.gov/publications/proceedings/conf04/extended_abs/randall_da.pdf) has been developed and successfully deployed as part of an atmospheric simulation.
The work is described in a [GRL paper](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091363) with [accompanying code available](https://github.com/yaniyuval/Neural_nework_parameterization/tree/v.1.0.3). The repository contains the neural net and its implementation into a simple system for atmospheric modelling, [SAM](http://rossby.msrc.sunysb.edu/~marat/SAM.html).
The aims of this repository are to:
1. develop a standalone fortran module based on this neural net that can be used elsewhere,
2. deploy the module in another atmospheric model, and
3. evaluate its performance.
We may also perform an investigation into interfacing the pytorch implementation of the Neural Net using the [pytorch-fortran bridging code](https://github.com/Cambridge-ICCS/fortran-pytorch-lib) developed at the [Institute of Computing for Climate Science](https://cambridge-iccs.github.io/).
The model will first be deployed into the [Single Column Atmospheric Model (SCAM)](https://www.cesm.ucar.edu/models/simple/scam) - a single column version of the CESM.
We plan to evaluate performance using SCAM in the gateIII configuration for tropical convection in a similar manner described by the [SCAM6 pulication in JAMES](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018MS001578).
This will compare model performance to data from an intense observation period (IOP) described in an [AMS publication](https://journals.ametsoc.org/view/journals/atsc/36/1/1520-0469_1979_036_0053_saposs_2_0_co_2.xml).
Long term developments of this project will seek to re-deploy more complex ML parameterizations into mode complex atmospheric models such as the [Community Atmospheric Model (CAM)](https://www.cesm.ucar.edu/models/cam) part of the CESM.
## Repository structure
```
├── NN_module
│ └── ...
└── torch_nets
└── ...
```
### Contents
### `NN_module/`
This folder contains the fortran neural net extracted from the [code referenced above](https://github.com/yaniyuval/Neural_nework_parameterization/tree/v.1.0.3), along with any dependencies, that may be compiled as a standalone fortran module.
Currently there is code that can be built on CSD3 using the included shell script.
This now needs cleaning up, testing, and a proper makefile creating (see open issues #9 and #10).
### ``torch_nets/``
The directory contains the PyTorch versions of the neural networks we are interested in.
## Contributing
This repository is currently private as it is new and work in progress.
Open tickets can be viewed at ['Issues'](https://github.com/m2lines/convection-parameterization-in-CAM/issues).
To contribute find a relevant issue or open a new one and assign yourself to work on it.
Then create a branch in which to add your contribution and open a pull request.
Once ready assign a reviewer and request a code review.
Merging should _only_ be performed once a code review has taken place. | {"language": ["en"], "license": "mit", "tags": ["climate"]} | ICCS/cam-ml-yog-v0 | null | [
"climate",
"en",
"license:mit",
"region:us"
] | null | 2024-05-02T10:14:34+00:00 |
token-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. -->
# my_awesome_model
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 0.9890
- Recall: 1.0
- F1: 0.9945
- Accuracy: 1.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0002 | 1.0 | 806 | 0.0007 | 0.7980 | 0.9 | 0.8460 | 0.9996 |
| 0.0001 | 2.0 | 1612 | 0.0002 | 0.9569 | 0.9861 | 0.9713 | 0.9999 |
| 0.0 | 3.0 | 2418 | 0.0001 | 0.9728 | 0.9917 | 0.9821 | 0.9999 |
| 0.0 | 4.0 | 3224 | 0.0001 | 0.9863 | 1.0 | 0.9931 | 1.0000 |
| 0.0 | 5.0 | 4030 | 0.0001 | 0.9890 | 1.0 | 0.9945 | 1.0000 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "my_awesome_model", "results": []}]} | lilyyellow/my_awesome_model | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:15:50+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. -->
**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. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | yashdkadam/train-on-cleaned-dataset | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:15:54+00:00 |
null | null | {} | ACEGameAI/Vedant-ohwx_man | null | [
"region:us"
] | null | 2024-05-02T10:16:00+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. -->
# text_classification_gpt2_1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4571
- Accuracy: 0.792
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9088 | 0.1 | 625 | 0.6229 | 0.662 |
| 1.0188 | 0.2 | 1250 | 0.5429 | 0.7092 |
| 0.9781 | 0.3 | 1875 | 0.5086 | 0.7556 |
| 0.6919 | 0.4 | 2500 | 0.4571 | 0.792 |
| 0.002 | 0.5 | 3125 | 0.5278 | 0.7892 |
| 1.6834 | 0.6 | 3750 | 0.5348 | 0.8104 |
| 0.0436 | 0.7 | 4375 | 0.4732 | 0.826 |
| 0.0332 | 0.8 | 5000 | 0.4995 | 0.8252 |
| 0.9907 | 0.9 | 5625 | 0.4764 | 0.8344 |
| 0.9651 | 1.0 | 6250 | 0.4809 | 0.8364 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "gpt2", "model-index": [{"name": "text_classification_gpt2_1", "results": []}]} | badrabbitt/text_classification_gpt2_1 | null | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:16:27+00:00 |
null | null | {} | gabotechs/music_gen | null | [
"onnx",
"region:us"
] | null | 2024-05-02T10:16:30+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]
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<!-- Provide the basic links for the model. -->
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- **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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOpeepeepoopoo/herewegoagain2 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:17:41+00:00 |
null | null |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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] | {} | miansumairjaved/msj | null | [
"arxiv:1910.09700",
"region:us"
] | null | 2024-05-02T10:17:50+00:00 |
null | null |
# ChimerallamaHermes-7B
ChimerallamaHermes-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: mlabonne/ChimeraLlama-3-8B
- model: NousResearch/Hermes-2-Pro-Llama-3-8B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/ChimerallamaHermes-7B"
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"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/ChimerallamaHermes-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T10:17:53+00:00 |
null | null | {} | automated-finetunning/bart_test_100 | null | [
"region:us"
] | null | 2024-05-02T10:18:12+00:00 |
|
text-generation | transformers | {} | gurman-02/fairy1 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T10:18:32+00:00 |
|
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
YetAnother_Open-Llama-3B-LoRA-OpenOrca - GGUF
- Model creator: https://huggingface.co/Andron00e/
- Original model: https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q2_K.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q2_K.gguf) | Q2_K | 1.84GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ3_XS.gguf) | IQ3_XS | 1.84GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ3_S.gguf) | IQ3_S | 1.84GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q3_K_S.gguf) | Q3_K_S | 1.84GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ3_M.gguf) | IQ3_M | 1.92GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q3_K.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q3_K.gguf) | Q3_K | 1.99GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q3_K_M.gguf) | Q3_K_M | 1.99GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q3_K_L.gguf) | Q3_K_L | 2.06GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ4_XS.gguf) | IQ4_XS | 1.86GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_0.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_0.gguf) | Q4_0 | 1.84GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.IQ4_NL.gguf) | IQ4_NL | 1.86GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_K_S.gguf) | Q4_K_S | 2.24GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_K.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_K.gguf) | Q4_K | 2.4GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_K_M.gguf) | Q4_K_M | 2.4GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_1.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q4_1.gguf) | Q4_1 | 2.04GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_0.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_0.gguf) | Q5_0 | 2.23GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_K_S.gguf) | Q5_K_S | 2.42GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_K.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_K.gguf) | Q5_K | 2.57GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_K_M.gguf) | Q5_K_M | 2.57GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_1.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q5_1.gguf) | Q5_1 | 2.42GB |
| [YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q6_K.gguf](https://huggingface.co/RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf/blob/main/YetAnother_Open-Llama-3B-LoRA-OpenOrca.Q6_K.gguf) | Q6_K | 3.39GB |
Original model description:
---
license: apache-2.0
datasets:
- Open-Orca/OpenOrca
language:
- en
library_name: transformers
pipeline_tag: question-answering
metrics:
- accuracy
---
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Andron00e
- **Language(s) (NLP):** Python (PyTorch, transformers, peft)
- **License:** apache-2.0
- **Finetuned from model:** openlm-research/open_llama_3b
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/Andron00e/Fine-Tuning-project
### Training Data
<!-- This should link to a Data 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. -->
https://huggingface.co/datasets/Open-Orca/OpenOrca
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Evaluation of the model was carried out using EulerAI library, more [precisely](https://github.com/EleutherAI/lm-evaluation-harness/tree/e47e01beea79cfe87421e2dac49e64d499c240b4#task-versioning)
#### Testing Data
<!-- This should link to a Data Card if possible. -->
hellaswag testing dataset
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
Accuracy
### Results and Model Examination
| Task |Version| Metric |Value | |Stderr|
|---------|------:|--------|-----:|---|-----:|
|hellaswag| 0|acc |0.4899|± |0.0050|
| | |acc_norm|0.6506|± |0.0048|
## Citations
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{eval-harness,
author = {Gao, Leo and
Tow, Jonathan and
Biderman, Stella and
Black, Sid and
DiPofi, Anthony and
Foster, Charles and
Golding, Laurence and
Hsu, Jeffrey and
McDonell, Kyle and
Muennighoff, Niklas and
Phang, Jason and
Reynolds, Laria and
Tang, Eric and
Thite, Anish and
Wang, Ben and
Wang, Kevin and
Zou, Andy},
title = {A framework for few-shot language model evaluation},
month = sep,
year = 2021,
publisher = {Zenodo},
version = {v0.0.1},
doi = {10.5281/zenodo.5371628},
url = {https://doi.org/10.5281/zenodo.5371628}
}
```
## Model Card Authors and Contact
[Andron00e](https://github.com/Andron00e)
| {} | RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-02T10:19:16+00:00 |
null | null | {} | automated-finetunning/bart_test_101 | null | [
"region:us"
] | null | 2024-05-02T10:19:31+00:00 |
|
null | null | {} | KalaiselvanD/albert_test_model_new | null | [
"region:us"
] | null | 2024-05-02T10:19:49+00:00 |
|
null | null | {"license": "mit"} | Raj-d/Chatbot | null | [
"license:mit",
"region:us"
] | null | 2024-05-02T10:19:56+00:00 |
|
null | null | {} | automated-finetunning/bart_test_102 | null | [
"region:us"
] | null | 2024-05-02T10:20:15+00:00 |
|
null | null | {} | MR-Eder/phi3-chat-wiki-de-single-pairs-Q4-K-M | null | [
"gguf",
"region:us"
] | null | 2024-05-02T10:20:57+00:00 |
|
null | null | {} | AdityaNath/Osaka_Arch_LoRA | null | [
"region:us"
] | null | 2024-05-02T10:21:39+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** aiaustin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"} | aiaustin/llama-3-8b-Instruct-bnb-4bit-aiaustin-demo | null | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"8-bit",
"region:us"
] | null | 2024-05-02T10:21:46+00:00 |
text2text-generation | transformers | {} | abhishekparashar/t5-small-finetuned-xsum | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:22:15+00:00 |
|
null | null | {} | olena-tymchenko/whisper-small-hi | null | [
"region:us"
] | null | 2024-05-02T10:22:35+00:00 |
|
null | null | {} | automated-finetunning/bart_mohit_100 | null | [
"region:us"
] | null | 2024-05-02T10:23:51+00:00 |
|
automatic-speech-recognition | transformers | {} | cherifkhalifah/whisper-small-hi | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:24:13+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "243.91 +/- 24.07", "name": "mean_reward", "verified": false}]}]}]} | sairangoju/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-05-02T10:24:54+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
illuni-llama-2-ko-7b-test - bnb 4bits
- Model creator: https://huggingface.co/julleong/
- Original model: https://huggingface.co/julleong/illuni-llama-2-ko-7b-test/
Original model description:
---
language:
- ko
base_model: beomi/llama-2-ko-7b
license: mit
pipeline_tag: question-answering
tags:
- instruct
- instruction
- llama-2
---
# llama2-7b
### Model Details
- Developed by: Julleong
- Backbone Model: beomi/llama-2-ko-7b
- Library: [transformers](https://github.com/huggingface/transformers)
### Used Datasets
- 개체명 사전 2022(모두의 말뭉치)
### Prompt Template
```
<usr>
{Instruction}
<bot>
{Answer}
```
### License
- MIT
| {} | RichardErkhov/julleong_-_illuni-llama-2-ko-7b-test-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T10:25:29+00:00 |
text-generation | transformers | {} | azizkt/HPY_gpt2_v7 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:26:16+00:00 |
|
null | null | {} | BothBosu/distilbert-scam-classifier-v2 | null | [
"region:us"
] | null | 2024-05-02T10:27:19+00:00 |
|
text-generation | transformers |
Trained from athirdpath/Llama-3-15b-Instruct on 0.6 epochs of athirdpath/Merge_Glue
I cut it off pretty early because I didn't want the model taking on the tone/logic of the largely GPT 3.5/4 data, just to heal the grafts
It amazes me how, like the human brain, an LLM can recover from massive trauma by just being given an opportunity to reroute. Neuroplasticity is magic.
<p align="center"><font size="5"> <b>Date Writing Example @ q6_k</b> </font></p>
<b>Write a medium-length story about Alice, a 26 year old cocktail waitress, having steamy sex with a guy she picked up at the bar for a one night stand.</b>
<i>
Sure thing! I'll start by setting the scene for our protagonist, Alice. She's a stunning 26-year-old cocktail waitress who works at an upscale hotel bar near downtown Los Angeles. The atmosphere is always lively and bustling with business professionals and travelers looking to unwind after work or dinner.
Alice has a way of captivating people's attention, drawing them in with her bright blue eyes, long blonde hair, and radiant smile. She effortlessly juggles multiple conversations at once while expertly crafting her signature cocktails. Tonight, she's wearing a sleek black dress that accentuates her curves as she glides from table to table.
As the night wears on, Alice notices a handsome man standing alone at the end of the bar. He's tall and lean, with piercing brown eyes and chiseled features. There's something about him that catches her attention - maybe it's his confidence or sense of vulnerability. Whatever it is, she can't help but feel drawn to him.
As she approaches him with a refill on his drink, their gazes lock for a brief moment. The air crackles with electricity as they exchange a flirtatious smile and an easy conversation unfolds. Before long, the man introduces himself as Ryan, a successful businessman who's in town for work. Alice is charmed by his stories of adventure and travel.
As the night wears on, their banter turns more intimate. The tension between them builds until it becomes impossible to ignore. The chemistry between them grows stronger with each passing moment until they can no longer resist the allure of a one-night stand.
After sharing a few drinks, Alice leads Ryan away from the bar and into the hotel suite he's booked for the night. As they undress and slip under the sheets, their desire is palpable. The room darkens, lit only by soft candlelight that casts an eerie glow on their bodies.
Their passion builds until it reaches a crescendo as Alice wraps her arms around Ryan's neck and pulls him in close. Their lips meet in a kiss that defies all boundaries of modesty. The hotel suite is filled with the sounds of moans, gasps, and groans as they explore each other's desires.
Finally, after an unforgettable night of lust and abandon, their bodies come to rest against one another - exhausted but exhilarated by their forbidden pleasure. And though it will likely be their last meeting in this life, Alice knows that the memory of this night will forever linger in her heart.
</i> | {"license": "llama3"} | athirdpath/Llama-3-15b-Instruct-GLUED | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:28:15+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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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | CHAFIK12/tsql_to_plsql_merged | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:28:35+00:00 |
null | null | {"license": "apache-2.0"} | ldh0507/test_gemma_2b_it_sum_ko | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T10:28:53+00:00 |
|
null | null | {} | irrrrrr/pegasus-samsum | null | [
"region:us"
] | null | 2024-05-02T10:29:47+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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## Uses
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### Direct Use
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### Downstream Use [optional]
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model41 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:30:17+00:00 |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: pietroorlandi/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | pietroorlandi/ppo-SnowballTarget | null | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | null | 2024-05-02T10:30:49+00:00 |
null | null | {} | sujithbandi2K6/wigp-mistral | null | [
"safetensors",
"region:us"
] | null | 2024-05-02T10:33:36+00:00 |
|
question-answering | 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. -->
# roberta_mrqa_v1
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9723
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 80 | 2.4611 |
| No log | 2.0 | 160 | 2.0962 |
| No log | 3.0 | 240 | 1.9723 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/roberta-base", "model-index": [{"name": "roberta_mrqa_v1", "results": []}]} | enriquesaou/roberta_mrqa_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:33:54+00:00 |
text-generation | mlx |
# Llama-3-IMPACTS-2x8B-64k-MLX
<img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/y7YKGpr7_Tg9YlhbIscI6.webp" width="500" height="500">
---
**Designed for Advanced Problem-Solving Across Interconnected Domains of Biomimicry, Climate Change, and Astrobiology**
The `Llama-3-IMPACTS-2x8B-64k-MLX` model is a cutting-edge large language model trained on the I.M.P.A.C.T.S dataset, which encompasses scenarios from biomimicry, climate change, and theoretical astrobiology. This model has been specifically tailored to generate innovative solutions and insights for both Earth and potential extraterrestrial environments, reflecting key themes of resilience, sustainability, and the interconnectedness of life across the universe.
## Model Details
### Description
- **Model name:** `Llama-3-IMPACTS-2x8B-64k-MLX`
- **Developer:** Severian
- **Version:** 1.0
- **License:** MIT
### Training Data
The model was trained on a subset of the I.M.P.A.C.T. dataset, utilizing 35,000 carefully curated examples that include detailed scenarios involving climate adaptation, biomimetic applications, and the potential for life in varying cosmic conditions.
### Model Architecture
- **Type:** Llama-3
- **Parameters:** 8 billion
- **MoE:** 2 Experts
- **Training:**
- Epochs: 1 (35K Examples)
- R = 64
- Alpha = 128
- Lr = 1e-7
- **Context Limit:** 64K
## Intended Uses
This model is intended for use in applications that require deep, interdisciplinary understanding and the generation of novel insights within the realms of environmental science, synthetic biology, space exploration, and sustainability studies. Its capabilities make it ideal for:
- Research and academic studies aiming to explore complex scenarios involving ecological and astrobiological phenomena.
- Organizations looking to innovate in the fields of climate resilience and biomimicry.
- Creative problem-solving in contexts where conventional approaches are insufficient.
## How to Use This Model
The model can be loaded and used in various natural language processing tasks that require nuanced understanding and creative output. Here is a basic example of how to load and use the model using the MLX library:
```python
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Severian/Llama-3-IMPACTS-2x8B-64k-MLX")
response = generate(model, tokenizer, prompt="how could Bioluminescent Algae come to evolve into a life form around a red dwarf star that has no planets or rocky material? Next, how could that Bioluminescent Algae somehow make it's way to earth as an alien entity? Then, what would happen over a 100 year span if that alien Bioluminescent Algae led to the over-acidification of the water on the entire planet? how could we use biomimicry to stop the ocean from over-acidification?", verbose=True)
```
## Limitations and Biases
While the `Llama-3-IMPACTS-2x8B-64k-MLX` model is designed to be a powerful tool for generating insightful content, it inherits limitations from its training data, which, though extensive, may not capture all possible scenarios or biases. Users should be aware of these limitations and consider them when interpreting the model's outputs, especially in decision-making contexts.
## Model Performance
Initial tests indicate that the model performs exceptionally well in tasks that involve complex reasoning and generating innovative solutions based on the scenarios presented in the I.M.P.A.C.T.S dataset. Further evaluation and fine-tuning may be required to optimize performance for specific applications.
The `Llama-3-IMPACTS-2x8B-64k-MLX` model represents an avenue that AI can use for exploring and solving complex problems across multiple domains. By leveraging the rich, interconnected dataset of I.M.P.A.C.T.S, it offers a valuable tool for researchers, innovators, and thinkers aiming to push the boundaries of what's possible in their fields.
# Use Cases
## Universe/World Simulation
Use Llama-3-IMPACTS-2x8B-64k to simulate incredibly unique and diverse ecosystems and cosmic worlds. Example image generation (text-to-image):
**User Prompt:** 'Craft a detailed image generation prompt focused on visualizing an innovative scenario that integrates biomimicry, climate change adaptation, and astrobiology.'
```md
Image Generation Prompt:
Title: “Biomimicry on Kepler-62f: A Red Dwarf Oasis”
Description:
In the year 2154, humanity discovers a new Earth-like planet, Kepler-62f, orbiting a red dwarf star. This distant world is beset by extreme climate conditions, with scorching daytime temperatures and frigid nighttime temperatures. To thrive in this unforgiving environment, scientists apply biomimetic technologies developed on Earth to create a sustainable ecosystem.
Scene:
In the foreground, a sprawling cityscape stretches across the planet’s surface, its architecture inspired by nature’s own designs. Towering “Tree-Habitats” mimic the branching structures of terrestrial trees, providing shelter and habitat for various extraterrestrial species. Their trunks are covered in iridescent, solar-powered panels that generate energy while also filtering out harmful radiation from the red dwarf star.
In the distance, a vast network of “Coral-Reefs” sprawls across the planet’s surface, mimicking the intricate structures found on Earth’s coral reefs. These artificial ecosystems support an incredible array of alien marine life, including bioluminescent fish, towering kelp-like seaweed, and gelatinous sea creatures that drift through the water like terrestrial jellyfish.
Above the city, a series of “Cloud-Cities” float majestically, their translucent membranes reflecting the red dwarf star’s light. These floating metropolises are home to species adapted for life in the upper atmosphere, such as winged beings with iridescent feathers and gas-filled bodies that allow them to soar through the skies.
Key Features:
Biomimetic Architecture: Buildings and structures are designed to mimic natural forms, providing insulation, shelter, and habitat for various species.
Artificial Ecosystems: “Coral-Reefs” support marine life, while “Tree-Habitats” provide shelter and food sources for terrestrial species.
Energy Harvesting: Solar-powered panels on the Tree-Habitats generate energy, while wind turbines and tidal power generators supplement the planet’s energy needs.
Advanced Life Support Systems: Closed-loop systems recycle water, air, and waste, minimizing the impact of human activities on the planet’s fragile ecosystem.
Extraterrestrial Biodiversity: Various alien species thrive in this red dwarf oasis, from towering kelp-like seaweed to bioluminescent fish and winged beings adapted for life in the upper atmosphere.
Color Palette:
Warm oranges and yellows evoke the scorching daytime temperatures
Deep blues and purples represent the frigid nighttime conditions
Iridescent hues on the Tree-Habitats and Cloud-Cities reflect the planet’s unique radiation patterns
Style:
Blend of realistic and stylized elements, with a focus on showcasing the innovative biomimetic technologies and extraterrestrial biodiversity.
Incorporate subtle texture and gradient effects to convey the harsh climate conditions and the adaptability of life on Kepler-62f.
By visualizing this futuristic scenario, we can explore the possibilities of biomimicry as a solution for climate change adaptation and sustainability in extreme environments. The image should inspire viewers to consider the potential applications of nature-inspired technologies in our own struggle against environmental challenges and encourage a deeper appreciation for the diversity of life that exists – or could exist – beyond Earth.
```
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/m3GfS0TcOejD1V7AI5OwN.webp" width="500" height="500">
---
### Disaster Response Simulation
**Prompt:** "Simulate the impact of a Category 5 hurricane hitting the eastern coast of Florida in 2030, considering factors such as rising sea levels and urban density. Assess potential emergency responses and resource needs."
#### Example Response
**Response:**
"**Scenario:**
A powerful Category 5 hurricane, named "Hurricane Ana," makes landfall on September 15, 2030, along the eastern coast of Florida, near Miami. The storm brings with it catastrophic winds, torrential rainfall, and a massive storm surge due to rising sea levels.
**Storm Characteristics:**
* Wind Speeds: Up to 160 mph (257 km/h)
* Storm Surge: 10-15 feet (3-4.5 meters) above mean sea level
* Rainfall: 12-18 inches (30-45 cm) in the first 24 hours, with isolated areas receiving up to 24 inches (60 cm)
**Impacts:**
1. **Coastal Flooding:** Rising sea levels exacerbate the storm surge, causing widespread flooding and damage along the eastern coast of Florida, particularly in Miami-Dade, Broward, and Palm Beach counties.
2. **Urban Infrastructure:** The densely populated cities along the coast suffer significant damage to buildings, roads, and infrastructure, leading to disruptions in essential services like power, water, and communication.
3. **Displacement and Evacuations:** Over 1 million people are displaced or evacuated from their homes, with many seeking shelter in nearby inland areas or temporary facilities.
4. **Environmental Concerns:** The storm surge contaminates waterways, causing concerns about water quality and potential health risks for those affected.
5. **Economic Impact:** The estimated economic loss is around $100 billion, considering damage to infrastructure, businesses, and agricultural industries.
**Emergency Response:**
1. **Evacuation Efforts:** Emergency responders prioritize evacuations, focusing on vulnerable populations like the elderly, children, and those with disabilities. They also ensure that essential services like hospitals, fire stations, and emergency centers are evacuated.
2. **Search and Rescue Operations:** Teams conduct door-to-door searches for trapped individuals, using specialized equipment and resources to navigate flooded areas.
3. **Food, Water, and Medical Supplies:** Aid distributions prioritize the most affected communities, with a focus on providing essential supplies, shelter, and medical care.
4. **Power Restoration:** Utility companies work to restore power as quickly as possible, deploying crews from neighboring states and using advanced technologies like drones and satellite imaging to assess damage and optimize response efforts.
**Resource Needs:**
1. **Personnel:** Over 10,000 emergency responders, including firefighters, police officers, medical personnel, and National Guard troops, are deployed to support the response effort.
2. **Equipment and Supplies:** Critical resources include:
* Generators and fuel
* Food, water, and medical supplies
* Communication equipment (satellite phones, etc.)
* Heavy machinery for debris removal and infrastructure repair
* Shelter materials (tents, blankets, etc.)
3. **Funding:** Estimated costs exceed $500 million, requiring a significant allocation of federal, state, and local funds to support the response and recovery efforts.
4. **Logistical Support:** Coordination with neighboring states and national agencies is crucial for maintaining supply chains, transporting personnel and equipment, and sharing expertise.
**Challenges:**
1. **Infrastructure Overload:** The sheer scale of damage overwhelms local infrastructure, leading to delays in aid distribution and response efforts.
2. **Water Contamination:** Concerns about water quality hinder access to clean drinking water, exacerbating public health risks.
3. **Displacement and Shelter:** The large number of displaced individuals creates a significant shortage of available shelter options, straining resources and emergency services.
**Lessons Learned:**
1. **Preparedness is Key:** Prioritizing evacuation planning, infrastructure hardening, and disaster preparedness can significantly reduce the impact of such events.
2. **Collaboration is Crucial:** Effective coordination between local, state, and federal agencies, as well as with neighboring states and national organizations, is essential for a successful response.
3. **Adaptation to Rising Sea Levels:** The increasing threat of sea-level rise requires a shift in disaster preparedness strategies, focusing on resilient infrastructure design and adaptive management practices.
**Conclusion:**
The simulated impact of Hurricane Ana highlights the devastating consequences of a Category 5 hurricane hitting Florida's eastern coast, exacerbated by rising sea levels. The emergency response efforts require significant resources, personnel, and coordination to address the complex challenges posed by this disaster. As we move forward, it is essential to prioritize preparedness, collaboration, and adaptation to mitigate the effects of these events and protect vulnerable communities."
# Try it out yourself!
### Extraterrestrial Colony Planning
**Prompt:** "Design a sustainable human colony for Mars, focusing on water resource management, energy production, and habitat construction using local materials. Include potential environmental challenges and solutions."
### Environmental Policy Formulation
**Prompt:** "Evaluate the long-term ecological impacts of banning single-use plastics globally. Simulate the effects on marine life, pollution levels, and waste management systems over the next 50 years."
### Advanced Educational Tools
**Prompt:** "Create an interactive simulation that demonstrates the water cycle on Earth, including the effects of deforestation and urbanization on water availability and quality in major river basins."
### Interactive Storytelling
**Prompt:** "Generate a narrative where the user is a leader in a community facing severe drought conditions. Allow the user to make decisions about water usage, agricultural practices, and public policy, showing the consequences of each choice."
### Biodiversity Conservation Strategies
**Prompt:** "Develop a conservation strategy for the Amazon rainforest, focusing on mitigating the effects of deforestation and climate change. Simulate various scenarios involving local communities and global stakeholders."
### Interstellar Communication Simulation
**Prompt:** "Imagine a scenario where Earth receives a signal from a distant planet. Simulate a series of potential communications that could be exchanged, considering language barriers and the transmission delay over light-years."
### Bioengineering Solutions
**Prompt:** "Propose a bioengineering project to create microbial life forms that can detoxify plastic waste in the ocean. Describe the genetic traits these organisms would need and simulate the potential ecological impact."
### Cross-Planetary Impact Studies
**Prompt:** "Analyze how a supernova explosion in a neighboring star system could affect planetary systems in its vicinity, including potential impacts on Earth's magnetic field and atmosphere."
### Custom Scenario Development
**Prompt:** "Allow the user to create a custom scenario involving an unexpected volcanic eruption in Iceland. Let the user set parameters like the eruption's size, duration, and ash distribution, then simulate the global climate and air travel impacts."
These prompts are designed to maximize the utilization of the model's capabilities in various complex and interdisciplinary scenarios, making them useful for researchers, educators, policymakers, and enthusiasts interested in exploring these domains. | {"language": ["en"], "license": "mit", "library_name": "mlx", "tags": ["climate change", "biomimicry", "theoretical astrobiology", "environmental simulations", "predictive modeling", "life origins", "ecological impacts", "sustainable technologies", "cross-disciplinary learning", "artificial intelligence", "machine learning", "data integration", "complex systems", "scenario analysis", "speculative science", "universe exploration", "biodiversity", "planetary studies", "innovation in science", "role playing scenarios"], "datasets": ["Severian/IMPACTS"], "pipeline_tag": "text-generation"} | Severian/Llama-3-IMPACTS-2x8B-64k-MLX | null | [
"mlx",
"mixtral",
"climate change",
"biomimicry",
"theoretical astrobiology",
"environmental simulations",
"predictive modeling",
"life origins",
"ecological impacts",
"sustainable technologies",
"cross-disciplinary learning",
"artificial intelligence",
"machine learning",
"data integration",
"complex systems",
"scenario analysis",
"speculative science",
"universe exploration",
"biodiversity",
"planetary studies",
"innovation in science",
"role playing scenarios",
"text-generation",
"conversational",
"en",
"dataset:Severian/IMPACTS",
"license:mit",
"region:us"
] | null | 2024-05-02T10:33:54+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|------------|------:|------|-----:|--------|-----:|---|-----:|
|hellaswag_it| 1|none | 0|acc |0.4486|± |0.0052|
| | |none | 0|acc_norm|0.5970|± |0.0051|
|arc_it | 1|none | 0|acc |0.0915|± |0.0084|
| | |none | 0|acc_norm|0.4166|± |0.0144|
|m_mmlu_it | 0|none | 5|acc |0.5651|± |0.0043|
## 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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### Direct Use
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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### 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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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"language": ["it"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["cosimoiaia/Loquace-102k"]} | nonsonpratico/phi3-3.8-128k-italian-v2 | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"it",
"dataset:cosimoiaia/Loquace-102k",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:34:39+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.
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
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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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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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### Model Architecture and Objective
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | 46an/WaziAi-finetuned-weights | null | [
"transformers",
"safetensors",
"trl",
"sft",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:36:02+00:00 |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: raydium/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | raydium/ppo-Huggy | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | null | 2024-05-02T10:36:26+00:00 |
null | null | {} | raidavid/whisper-tiny-20240502-no-open-data | null | [
"region:us"
] | null | 2024-05-02T10:37:20+00:00 |
|
text2text-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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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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]
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<!-- 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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[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).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | steve1989/flant5xl-finetuned-finance-headlines-sentiment-analysis-bnb4bits | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:37:34+00:00 |
null | null | {} | Poojithpoosa/patentclassification | null | [
"region:us"
] | null | 2024-05-02T10:37:54+00:00 |
|
video-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. -->
# vivit-b-16x2-kinetics400-finetuned-temp-original
This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1267
- Accuracy: 0.64
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 420
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3132 | 0.25 | 105 | 2.3486 | 0.14 |
| 1.8975 | 1.25 | 210 | 1.6591 | 0.42 |
| 1.423 | 2.25 | 315 | 1.2563 | 0.58 |
| 0.456 | 3.25 | 420 | 1.1267 | 0.64 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vivit-b-16x2-kinetics400", "model-index": [{"name": "vivit-b-16x2-kinetics400-finetuned-temp-original", "results": []}]} | kkumtori/vivit-b-16x2-kinetics400-finetuned-temp-original | null | [
"transformers",
"tensorboard",
"safetensors",
"vivit",
"video-classification",
"generated_from_trainer",
"base_model:google/vivit-b-16x2-kinetics400",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:38:34+00:00 |
null | null | Apakah Varicap?
Varicap Tablet ialah kapsul farmaseutikal yang direka untuk menangani hipertensi, biasanya dikenali sebagai tekanan darah tinggi. Ia mengandungi gabungan unik bahan semulajadi, dirumus dengan teliti untuk mengawal tahap tekanan darah dan menggalakkan kesihatan kardiovaskular.
Laman web rasmi:<a href="https://www.nutritionsee.com/varicoama">www.varicap.com</a>
<p><a href="https://www.nutritionsee.com/varicoama"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/Varicap-1.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/varicoama">Beli sekarang!! Klik pautan di bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang... Cepat</a>
Laman web rasmi:<a href="https://www.nutritionsee.com/varicoama">www.varicap.com</a>
| {"license": "apache-2.0"} | VaricapMalaysia/Varicap | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T10:38:49+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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- **Shared by [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
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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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. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["llama-factory"]} | Moriacrafter/Gemma-2B_DepressionDetection | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:38:50+00:00 |
text-generation | transformers |
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
>>> import transformers
>>> import torch
>>> model_id = "meta-llama/Meta-Llama-3-8B"
>>> pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
)
>>> pipeline("Hey how are you doing today?")
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B --include "original/*" --local-dir Meta-Llama-3-8B
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to Meta\u2019s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"} | mlabonne/Meta-Llama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null | 2024-05-02T10:40:58+00:00 |
null | null | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.com/invite/vb6SmA3hxu)
## This repo contains GGUF versions of the MathLLM/MathCoder-CL-7B model.
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.com/invite/vb6SmA3hxu) to share feedback/suggestions or get help.
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with GGUF.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***What is the model format?*** We use GGUF format.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
# Downloading and running the models
You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/):
| Quant type | Description |
|------------|--------------------------------------------------------------------------------------------|
| Q5_K_M | High quality, recommended. |
| Q5_K_S | High quality, recommended. |
| Q4_K_M | Good quality, uses about 4.83 bits per weight, recommended. |
| Q4_K_S | Slightly lower quality with more space savings, recommended. |
| IQ4_NL | Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. |
| IQ4_XS | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| Q3_K_L | Lower quality but usable, good for low RAM availability. |
| Q3_K_M | Even lower quality. |
| IQ3_M | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| IQ3_S | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| Q3_K_S | Low quality, not recommended. |
| IQ3_XS | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| Q2_K | Very low quality but surprisingly usable. |
## How to download GGUF files ?
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
- **Option A** - Downloading in `text-generation-webui`:
- **Step 1**: Under Download Model, you can enter the model repo: PrunaAI/MathCoder-CL-7B-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf.
- **Step 2**: Then click Download.
- **Option B** - Downloading on the command line (including multiple files at once):
- **Step 1**: We recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
- **Step 2**: Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download PrunaAI/MathCoder-CL-7B-GGUF-smashed MathCoder-CL-7B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
Alternatively, you can also download multiple files at once with a pattern:
```shell
huggingface-cli download PrunaAI/MathCoder-CL-7B-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/MathCoder-CL-7B-GGUF-smashed MathCoder-CL-7B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## How to run model in GGUF format?
- **Option A** - Introductory example with `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m MathCoder-CL-7B.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
- **Option B** - Running in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp).
- **Option C** - Running from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./MathCoder-CL-7B.IQ3_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<s>[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./MathCoder-CL-7B.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
- **Option D** - Running with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
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| {"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"} | PrunaAI/MathCoder-CL-7B-GGUF-smashed | null | [
"gguf",
"pruna-ai",
"region:us"
] | null | 2024-05-02T10:41:49+00:00 |
null | peft |
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"} | sravaniayyagari/lora_model_1 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | 2024-05-02T10:43:35+00:00 |
text-generation | transformers | # merged
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Shaleen123/llama3-code-8bit](https://huggingface.co/Shaleen123/llama3-code-8bit) as a base.
### Models Merged
The following models were included in the merge:
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: Shaleen123/llama3-code-8bit
dtype: float16
merge_method: ties
parameters:
int8_mask: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 32]
model: Shaleen123/llama3-code-8bit
- layer_range: [0, 32]
model: Shaleen123/llama3-code-8bit
parameters:
density: 0.5
weight: 0.5
- layer_range: [0, 32]
model: Shaleen123/llama3-code-8bit
parameters:
density: 0.5
weight: 0.3
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Shaleen123/llama3-code-8bit"]} | Shaleen123/llama3-code-8bit-merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:Shaleen123/llama3-code-8bit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T10:43:36+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | hoang1123/Llama-2-7b-chat-4bit-gptq | null | [
"transformers",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T10:44:19+00:00 |
null | null | {} | BossNahed/SWT | null | [
"region:us"
] | null | 2024-05-02T10:44:51+00:00 |
|
null | null | {"license": "creativeml-openrail-m"} | Warcylewis/mklanxxxNSFWrealistic | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-05-02T10:45:34+00:00 |
|
automatic-speech-recognition | transformers | {} | raidavid/whisper-tiny-20240502-no-opendata | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:45:35+00:00 |
|
null | null | {} | icerodrigo/Vozes | null | [
"region:us"
] | null | 2024-05-02T10:45:49+00:00 |
|
null | null | {"license": "mit"} | Fikaaw/en-setfit-absa-trial | null | [
"license:mit",
"region:us"
] | null | 2024-05-02T10:47:04+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
IMPORTANT NOTE: This is for ONGOING EXPERIMENTATION and is not meant for reuse.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | tanzuml/phi3-4k | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:47:20+00:00 |
null | null | {"license": "llama2"} | manohar02/text_video_summarizer | null | [
"license:llama2",
"region:us"
] | null | 2024-05-02T10:48:01+00:00 |
|
text-generation | transformers |
**Attention! The model is in a training state and this repository contains only a checkpoint for 20 billion tokens.**
# Aeoinum v1 Base 1B
A state-of-the-art language model for Russian language processing.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("aeonium/Aeonium-v1-Base-1.6B-checkpoint-20B")
model = AutoModelForCausalLM.from_pretrained("aeonium/Aeonium-v1-Base-1.6B-checkpoint-20B").cuda()
input_ids = tokenizer("Искусственный интеллект - это", return_tensors='pt').to(model.device)["input_ids"]
output = model.generate(input_ids, max_new_tokens=48, do_sample=True, temperature=0.7)
print(tokenizer.decode(output[0]))
```
Output:
```
Искусственный интеллект - это основа современной науки и техники. Его потенциал позволяет решать задачи, которые выходят за пределы человеческих возможностей. В работе над ними участвуют все: от ученых до инженеров и даже военных. В своей книге "Искусственный интеллект" автор книги, профессор Л
```
## Dataset Detail
The dataset for pre-training is collected from public data, most of which are web pages in Russian. The total size of the data is 20B tokens.
## Training Detail
The training is performed thanks to a grant from [TPU Research Cloud](https://sites.research.google/trc/about/) on a TPU v4-32 node.
## Copyright
The model is released under the Apache 2.0 license. | {"language": ["ru"], "license": "apache-2.0", "datasets": ["uonlp/CulturaX"], "pipeline_tag": "text-generation"} | aeonium/Aeonium-v1-Base-1.6B-checkpoint-20B | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"ru",
"dataset:uonlp/CulturaX",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:48:05+00:00 |
null | null | {} | zryb/ShahidAnwar | null | [
"region:us"
] | null | 2024-05-02T10:48:26+00:00 |
|
null | null | {} | ACEGameAI/Lang-Mei_ohwx-man | null | [
"region:us"
] | null | 2024-05-02T10:48:28+00:00 |
|
null | null | {} | LinxuanPastel/Jancaca | null | [
"region:us"
] | null | 2024-05-02T10:48:37+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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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": []} | steve1989/fingpt_sentiment_fin_headlines_bnb4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:48:41+00:00 |
null | null | {} | HeavyDriver/Doodle_Dreamlike | null | [
"region:us"
] | null | 2024-05-02T10:50:13+00:00 |
|
null | null | {} | AbdullahBarayan/bart-large-CEFR-24GPT | null | [
"region:us"
] | null | 2024-05-02T10:50:49+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. -->
# albert_model_02
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6010
- Accuracy: 0.7406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 310 | 0.6197 | 0.7203 |
| 0.6587 | 2.0 | 620 | 0.6010 | 0.7406 |
| 0.6587 | 3.0 | 930 | 0.6613 | 0.7345 |
| 0.3804 | 4.0 | 1240 | 0.7342 | 0.7467 |
| 0.2432 | 5.0 | 1550 | 0.7571 | 0.7476 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "albert_model_02", "results": []}]} | KalaiselvanD/albert_model_02 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:51:06+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.
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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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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- **Hardware Type:** [More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | vaatsav06/Llama3_medmcqa_finetune | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T10:51:48+00:00 |
automatic-speech-recognition | 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. -->
# whisper-tiny-finetune
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5792
- Wer: 20.6820
## 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: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 4.1356 | 0.2778 | 10 | 4.1201 | 47.9002 |
| 4.0312 | 0.5556 | 20 | 4.0231 | 47.3319 |
| 3.917 | 0.8333 | 30 | 3.8659 | 46.5425 |
| 3.7606 | 1.1111 | 40 | 3.6569 | 45.8478 |
| 3.4823 | 1.3889 | 50 | 3.3969 | 44.5216 |
| 3.0938 | 1.6667 | 60 | 3.0765 | 41.9324 |
| 2.7895 | 1.9444 | 70 | 2.6692 | 34.9542 |
| 2.3101 | 2.2222 | 80 | 2.1389 | 34.5122 |
| 1.6935 | 2.5 | 90 | 1.5546 | 34.8911 |
| 1.1419 | 2.7778 | 100 | 1.0650 | 36.5330 |
| 0.904 | 3.0556 | 110 | 0.8400 | 29.4601 |
| 0.7536 | 3.3333 | 120 | 0.7657 | 28.9233 |
| 0.6857 | 3.6111 | 130 | 0.7202 | 27.7550 |
| 0.6609 | 3.8889 | 140 | 0.6886 | 26.6814 |
| 0.5804 | 4.1667 | 150 | 0.6656 | 25.6710 |
| 0.5611 | 4.4444 | 160 | 0.6465 | 25.0710 |
| 0.5574 | 4.7222 | 170 | 0.6293 | 24.3448 |
| 0.552 | 5.0 | 180 | 0.6135 | 24.0606 |
| 0.4717 | 5.2778 | 190 | 0.6024 | 24.5974 |
| 0.4681 | 5.5556 | 200 | 0.5898 | 24.0290 |
| 0.4679 | 5.8333 | 210 | 0.5778 | 23.5238 |
| 0.4351 | 6.1111 | 220 | 0.5670 | 23.6501 |
| 0.3982 | 6.3889 | 230 | 0.5599 | 23.2081 |
| 0.3892 | 6.6667 | 240 | 0.5520 | 22.0714 |
| 0.3771 | 6.9444 | 250 | 0.5439 | 21.1872 |
| 0.3532 | 7.2222 | 260 | 0.5372 | 21.6925 |
| 0.3435 | 7.5 | 270 | 0.5309 | 27.5024 |
| 0.336 | 7.7778 | 280 | 0.5253 | 20.9346 |
| 0.3088 | 8.0556 | 290 | 0.5201 | 20.4610 |
| 0.3014 | 8.3333 | 300 | 0.5184 | 20.5242 |
| 0.316 | 8.6111 | 310 | 0.5146 | 20.2400 |
| 0.2931 | 8.8889 | 320 | 0.5118 | 19.9874 |
| 0.2228 | 9.1667 | 330 | 0.5079 | 20.3663 |
| 0.2445 | 9.4444 | 340 | 0.5052 | 20.2716 |
| 0.2343 | 9.7222 | 350 | 0.5039 | 20.2084 |
| 0.2893 | 10.0 | 360 | 0.5023 | 20.0189 |
| 0.2014 | 10.2778 | 370 | 0.5030 | 20.0505 |
| 0.2048 | 10.5556 | 380 | 0.5036 | 19.6400 |
| 0.1941 | 10.8333 | 390 | 0.5003 | 20.1137 |
| 0.1601 | 11.1111 | 400 | 0.4992 | 19.8295 |
| 0.1647 | 11.3889 | 410 | 0.5010 | 19.8926 |
| 0.1519 | 11.6667 | 420 | 0.5044 | 19.6716 |
| 0.1747 | 11.9444 | 430 | 0.5005 | 20.1137 |
| 0.1194 | 12.2222 | 440 | 0.5076 | 20.7452 |
| 0.1021 | 12.5 | 450 | 0.5104 | 19.9242 |
| 0.1115 | 12.7778 | 460 | 0.5102 | 20.7136 |
| 0.1355 | 13.0556 | 470 | 0.5068 | 20.3979 |
| 0.0824 | 13.3333 | 480 | 0.5152 | 20.5557 |
| 0.0858 | 13.6111 | 490 | 0.5189 | 20.3663 |
| 0.0786 | 13.8889 | 500 | 0.5225 | 21.1557 |
| 0.0564 | 14.1667 | 510 | 0.5250 | 20.9031 |
| 0.056 | 14.4444 | 520 | 0.5232 | 20.8715 |
| 0.0558 | 14.7222 | 530 | 0.5282 | 20.5557 |
| 0.0657 | 15.0 | 540 | 0.5299 | 20.7452 |
| 0.0369 | 15.2778 | 550 | 0.5342 | 20.6505 |
| 0.0355 | 15.5556 | 560 | 0.5341 | 20.1137 |
| 0.0383 | 15.8333 | 570 | 0.5370 | 20.4926 |
| 0.0333 | 16.1111 | 580 | 0.5401 | 20.5557 |
| 0.027 | 16.3889 | 590 | 0.5455 | 20.9346 |
| 0.0261 | 16.6667 | 600 | 0.5480 | 20.6189 |
| 0.024 | 16.9444 | 610 | 0.5494 | 20.4294 |
| 0.0164 | 17.2222 | 620 | 0.5505 | 20.3663 |
| 0.0159 | 17.5 | 630 | 0.5577 | 20.7136 |
| 0.0168 | 17.7778 | 640 | 0.5549 | 20.9031 |
| 0.015 | 18.0556 | 650 | 0.5555 | 20.8083 |
| 0.0116 | 18.3333 | 660 | 0.5596 | 20.9978 |
| 0.0131 | 18.6111 | 670 | 0.5614 | 20.9346 |
| 0.0121 | 18.8889 | 680 | 0.5634 | 20.3663 |
| 0.009 | 19.1667 | 690 | 0.5643 | 20.7452 |
| 0.0108 | 19.4444 | 700 | 0.5633 | 20.3031 |
| 0.0096 | 19.7222 | 710 | 0.5666 | 20.3979 |
| 0.0123 | 20.0 | 720 | 0.5660 | 20.4610 |
| 0.009 | 20.2778 | 730 | 0.5695 | 20.5242 |
| 0.0099 | 20.5556 | 740 | 0.5684 | 20.3663 |
| 0.0079 | 20.8333 | 750 | 0.5701 | 20.7768 |
| 0.008 | 21.1111 | 760 | 0.5701 | 20.7136 |
| 0.0084 | 21.3889 | 770 | 0.5719 | 20.7136 |
| 0.0076 | 21.6667 | 780 | 0.5724 | 20.4610 |
| 0.0081 | 21.9444 | 790 | 0.5724 | 20.7136 |
| 0.0067 | 22.2222 | 800 | 0.5731 | 20.6820 |
| 0.0076 | 22.5 | 810 | 0.5737 | 20.4926 |
| 0.0079 | 22.7778 | 820 | 0.5748 | 20.3979 |
| 0.0069 | 23.0556 | 830 | 0.5747 | 20.6820 |
| 0.0066 | 23.3333 | 840 | 0.5751 | 20.7136 |
| 0.0062 | 23.6111 | 850 | 0.5755 | 20.7136 |
| 0.0071 | 23.8889 | 860 | 0.5764 | 20.5873 |
| 0.0062 | 24.1667 | 870 | 0.5774 | 20.7136 |
| 0.0059 | 24.4444 | 880 | 0.5769 | 20.5873 |
| 0.0066 | 24.7222 | 890 | 0.5772 | 20.6189 |
| 0.0066 | 25.0 | 900 | 0.5778 | 20.5873 |
| 0.0066 | 25.2778 | 910 | 0.5779 | 20.5557 |
| 0.0062 | 25.5556 | 920 | 0.5781 | 20.5873 |
| 0.006 | 25.8333 | 930 | 0.5787 | 20.6189 |
| 0.0061 | 26.1111 | 940 | 0.5789 | 20.5873 |
| 0.0056 | 26.3889 | 950 | 0.5788 | 20.5557 |
| 0.006 | 26.6667 | 960 | 0.5789 | 20.5873 |
| 0.0055 | 26.9444 | 970 | 0.5790 | 20.5873 |
| 0.0057 | 27.2222 | 980 | 0.5791 | 20.6189 |
| 0.0063 | 27.5 | 990 | 0.5792 | 20.6820 |
| 0.0059 | 27.7778 | 1000 | 0.5792 | 20.6820 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-tiny.en", "model-index": [{"name": "whisper-tiny-finetune", "results": []}]} | edoyin/whisper-tiny-finetune | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny.en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:51:56+00:00 |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# cancerfarore/albert-base-v2-CancerFarore-Model
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7456
- Train End Logits Accuracy: 0.7778
- Train Start Logits Accuracy: 0.7525
- Validation Loss: 0.9444
- Validation End Logits Accuracy: 0.7069
- Validation Start Logits Accuracy: 0.6994
- Epoch: 1
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3798, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.2646 | 0.6255 | 0.6055 | 0.9592 | 0.6964 | 0.6829 | 0 |
| 0.7456 | 0.7778 | 0.7525 | 0.9444 | 0.7069 | 0.6994 | 1 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "albert/albert-base-v2", "model-index": [{"name": "cancerfarore/albert-base-v2-CancerFarore-Model", "results": []}]} | cancerfarore/albert-base-v2-CancerFarore-Model | null | [
"transformers",
"tf",
"albert",
"question-answering",
"generated_from_keras_callback",
"base_model:albert/albert-base-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:51:59+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]
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- **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]
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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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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | yashdkadam/new-train-on-cleaned-dataset | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"trl",
"sft",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-05-02T10:51:59+00:00 |
token-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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6573
- Answer: {'precision': 0.7060773480662983, 'recall': 0.7898640296662547, 'f1': 0.7456242707117853, 'number': 809}
- Header: {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119}
- Question: {'precision': 0.7687661777394306, 'recall': 0.8366197183098592, 'f1': 0.8012589928057554, 'number': 1065}
- Overall Precision: 0.7168
- Overall Recall: 0.7898
- Overall F1: 0.7515
- Overall Accuracy: 0.8172
## 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-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7999 | 1.0 | 10 | 1.5802 | {'precision': 0.008905852417302799, 'recall': 0.00865265760197775, 'f1': 0.00877742946708464, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1717325227963526, 'recall': 0.10610328638497653, 'f1': 0.13116656993615786, 'number': 1065} | 0.0831 | 0.0602 | 0.0698 | 0.3604 |
| 1.4567 | 2.0 | 20 | 1.2493 | {'precision': 0.18839103869653767, 'recall': 0.22867737948084055, 'f1': 0.20658849804578447, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.45693950177935944, 'recall': 0.6028169014084507, 'f1': 0.5198380566801619, 'number': 1065} | 0.3465 | 0.4150 | 0.3776 | 0.5986 |
| 1.114 | 3.0 | 30 | 0.9406 | {'precision': 0.43853820598006643, 'recall': 0.4894932014833127, 'f1': 0.46261682242990654, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5861538461538461, 'recall': 0.7154929577464789, 'f1': 0.6443974630021141, 'number': 1065} | 0.5237 | 0.5810 | 0.5509 | 0.7001 |
| 0.8434 | 4.0 | 40 | 0.7906 | {'precision': 0.5922836287799792, 'recall': 0.7021013597033374, 'f1': 0.6425339366515838, 'number': 809} | {'precision': 0.1111111111111111, 'recall': 0.04201680672268908, 'f1': 0.06097560975609755, 'number': 119} | {'precision': 0.6526994359387591, 'recall': 0.7605633802816901, 'f1': 0.7025151777970512, 'number': 1065} | 0.6160 | 0.6939 | 0.6527 | 0.7541 |
| 0.6817 | 5.0 | 50 | 0.7106 | {'precision': 0.6502192982456141, 'recall': 0.7330037082818294, 'f1': 0.6891342242882045, 'number': 809} | {'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119} | {'precision': 0.683921568627451, 'recall': 0.8187793427230047, 'f1': 0.7452991452991454, 'number': 1065} | 0.6546 | 0.7456 | 0.6972 | 0.7854 |
| 0.5737 | 6.0 | 60 | 0.6807 | {'precision': 0.6482617586912065, 'recall': 0.7836835599505563, 'f1': 0.7095691102406267, 'number': 809} | {'precision': 0.273972602739726, 'recall': 0.16806722689075632, 'f1': 0.20833333333333331, 'number': 119} | {'precision': 0.717206132879046, 'recall': 0.7906103286384977, 'f1': 0.7521214828048235, 'number': 1065} | 0.6724 | 0.7506 | 0.7093 | 0.7898 |
| 0.5058 | 7.0 | 70 | 0.6538 | {'precision': 0.6564102564102564, 'recall': 0.7911001236093943, 'f1': 0.7174887892376681, 'number': 809} | {'precision': 0.3048780487804878, 'recall': 0.21008403361344538, 'f1': 0.24875621890547264, 'number': 119} | {'precision': 0.7324894514767932, 'recall': 0.8150234741784037, 'f1': 0.7715555555555556, 'number': 1065} | 0.6838 | 0.7692 | 0.7240 | 0.7996 |
| 0.4425 | 8.0 | 80 | 0.6574 | {'precision': 0.6625766871165644, 'recall': 0.8009888751545118, 'f1': 0.7252378287632905, 'number': 809} | {'precision': 0.3055555555555556, 'recall': 0.2773109243697479, 'f1': 0.2907488986784141, 'number': 119} | {'precision': 0.7365771812080537, 'recall': 0.8244131455399061, 'f1': 0.7780239255649092, 'number': 1065} | 0.6844 | 0.7822 | 0.7300 | 0.7999 |
| 0.3932 | 9.0 | 90 | 0.6375 | {'precision': 0.6876971608832808, 'recall': 0.8084054388133498, 'f1': 0.7431818181818182, 'number': 809} | {'precision': 0.3645833333333333, 'recall': 0.29411764705882354, 'f1': 0.3255813953488372, 'number': 119} | {'precision': 0.752129471890971, 'recall': 0.8291079812206573, 'f1': 0.7887449754354622, 'number': 1065} | 0.7078 | 0.7888 | 0.7461 | 0.8087 |
| 0.3798 | 10.0 | 100 | 0.6437 | {'precision': 0.6981541802388708, 'recall': 0.7948084054388134, 'f1': 0.7433526011560695, 'number': 809} | {'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119} | {'precision': 0.7665505226480837, 'recall': 0.8262910798122066, 'f1': 0.7953004970628107, 'number': 1065} | 0.7136 | 0.7837 | 0.7470 | 0.8098 |
| 0.3225 | 11.0 | 110 | 0.6566 | {'precision': 0.6817226890756303, 'recall': 0.8022249690976514, 'f1': 0.7370812038614423, 'number': 809} | {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119} | {'precision': 0.7593856655290102, 'recall': 0.8356807511737089, 'f1': 0.7957085382208315, 'number': 1065} | 0.7030 | 0.7933 | 0.7454 | 0.8038 |
| 0.3097 | 12.0 | 120 | 0.6421 | {'precision': 0.6957928802588996, 'recall': 0.7972805933250927, 'f1': 0.7430875576036866, 'number': 809} | {'precision': 0.35, 'recall': 0.35294117647058826, 'f1': 0.35146443514644354, 'number': 119} | {'precision': 0.7692307692307693, 'recall': 0.8356807511737089, 'f1': 0.8010801080108011, 'number': 1065} | 0.7155 | 0.7913 | 0.7515 | 0.8177 |
| 0.2916 | 13.0 | 130 | 0.6515 | {'precision': 0.7035010940919038, 'recall': 0.7948084054388134, 'f1': 0.7463726059199072, 'number': 809} | {'precision': 0.33076923076923076, 'recall': 0.36134453781512604, 'f1': 0.34538152610441764, 'number': 119} | {'precision': 0.7649092480553155, 'recall': 0.8309859154929577, 'f1': 0.7965796579657966, 'number': 1065} | 0.7138 | 0.7883 | 0.7492 | 0.8154 |
| 0.2707 | 14.0 | 140 | 0.6557 | {'precision': 0.7016393442622951, 'recall': 0.7935723114956736, 'f1': 0.7447795823665894, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.36134453781512604, 'f1': 0.34677419354838707, 'number': 119} | {'precision': 0.7688966116420504, 'recall': 0.8309859154929577, 'f1': 0.7987364620938627, 'number': 1065} | 0.7153 | 0.7878 | 0.7498 | 0.8146 |
| 0.2729 | 15.0 | 150 | 0.6573 | {'precision': 0.7060773480662983, 'recall': 0.7898640296662547, 'f1': 0.7456242707117853, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119} | {'precision': 0.7687661777394306, 'recall': 0.8366197183098592, 'f1': 0.8012589928057554, 'number': 1065} | 0.7168 | 0.7898 | 0.7515 | 0.8172 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cpu
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["funsd"], "base_model": "microsoft/layoutlm-base-uncased", "model-index": [{"name": "layoutlm-funsd", "results": []}]} | RakhissBouchra/layoutlm-funsd | null | [
"transformers",
"tensorboard",
"safetensors",
"layoutlm",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"base_model:microsoft/layoutlm-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:52:12+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]
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## Uses
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[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. -->
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[More Information Needed]
## 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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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | ArunIcfoss/instruct_mal_eng_translation | null | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:52:28+00:00 |
null | null | {} | QQQGGG/test1 | null | [
"region:us"
] | null | 2024-05-02T10:54:54+00:00 |
|
text-generation | transformers |
# LuminariX-8B
LuminariX-8B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
## 🧩 Configuration
```yaml
models:
- model: Orenguteng/Llama-3-8B-Lexi-Uncensored
- model: Weyaxi/Einstein-v6.1-Llama3-8B
- model: cognitivecomputations/dolphin-2.9-llama3-8b-256k
merge_method: model_stock
base_model: cognitivecomputations/dolphin-2.9-llama3-8b-256k
dtype: bfloat16
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit"]} | bunnycore/LuminariX-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:55:43+00:00 |
null | null | {"license": "apache-2.0"} | derek-thomas/tgi-benchmark-notebooks | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T10:56:08+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. -->
# NDD-petclinic_test-content
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1901
- Accuracy: 0.9551
- F1: 0.9518
- Precision: 0.9567
- Recall: 0.9551
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1138 | 0.9993 | 674 | 0.1272 | 0.9620 | 0.9604 | 0.9618 | 0.9620 |
| 0.0691 | 1.9985 | 1348 | 0.1901 | 0.9551 | 0.9518 | 0.9567 | 0.9551 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "NDD-petclinic_test-content", "results": []}]} | lgk03/NDD-petclinic_test-content | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:56:13+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** abdulrehmanibk
- **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"], "metrics": ["accuracy"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "pipeline_tag": "text-generation"} | abdulrehmanibk/mpg_project-b | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"text-generation",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:56:32+00:00 |
null | null | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/chargoddard/llama-2-16b-nastychat
| {} | mradermacher/llama-2-16b-nastychat-i1-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-05-02T10:56:45+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Poojithpoosa/financial_phrasebankmodel
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1745
- Validation Loss: 0.0643
- Train Accuracy: 0.9736
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 24230, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6113 | 0.3262 | 0.8803 | 0 |
| 0.3522 | 0.1855 | 0.9416 | 1 |
| 0.1745 | 0.0643 | 0.9736 | 2 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.10.1
- Datasets 2.19.0
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "Poojithpoosa/financial_phrasebankmodel", "results": []}]} | Poojithpoosa/financial_phrasebankmodel | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:57:28+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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
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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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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | azizksar/mistral-sartex | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T10:59:01+00:00 |
null | null |
# ConfigurableLlama-7B
ConfigurableLlama-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
* [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B)
## 🧩 Configuration
```yaml
models:
- model: NousResearch/Meta-Llama-3-8B
# No parameters necessary for base model
- model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
density: 0.6
weight: 0.5
- model: mlabonne/OrpoLlama-3-8B
parameters:
density: 0.55
weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/ConfigurableLlama-7B"
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"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["NousResearch/Meta-Llama-3-8B-Instruct", "mlabonne/OrpoLlama-3-8B"]} | automerger/ConfigurableLlama-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"base_model:mlabonne/OrpoLlama-3-8B",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T10:59:04+00:00 |
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