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{ card_data }
---
# Model Card for MyCoolModel
This model does this and that.
LOC Model New Severian
This model was created by [@{ author }](https://hf.co/{author}). | {} | text-generation | higgsfield/new_loc_model_severian_2 | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-11T21:23:37+00:00 | [] | [] | TAGS
#transformers #pytorch #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ---
{ card_data }
---
# Model Card for MyCoolModel
This model does this and that.
LOC Model New Severian
This model was created by @{ author }. | [
"# Model Card for MyCoolModel\n\n This model does this and that.\n\n LOC Model New Severian\n\n This model was created by @{ author }."
] | [
"TAGS\n#transformers #pytorch #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for MyCoolModel\n\n This model does this and that.\n\n LOC Model New Severian\n\n This model was created by @{ author }."
] | [
46,
31
] | [
"passage: TAGS\n#transformers #pytorch #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for MyCoolModel\n\n This model does this and that.\n\n LOC Model New Severian\n\n This model was created by @{ author }."
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null | null | diffusers |
# LoRA text2image fine-tuning - BootesVoid/rahul-gandhi-lora
These are LoRA adaption weights for pretrained_model_name_or_path. The weights were fine-tuned on the fw1zr/rahul-gandhi-captions dataset. | {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "pretrained_model_name_or_path", "inference": true} | text-to-image | BootesVoid/rahul-gandhi-lora | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:pretrained_model_name_or_path",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-11T21:37:20+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-pretrained_model_name_or_path #license-creativeml-openrail-m #region-us
|
# LoRA text2image fine-tuning - BootesVoid/rahul-gandhi-lora
These are LoRA adaption weights for pretrained_model_name_or_path. The weights were fine-tuned on the fw1zr/rahul-gandhi-captions dataset. | [
"# LoRA text2image fine-tuning - BootesVoid/rahul-gandhi-lora\nThese are LoRA adaption weights for pretrained_model_name_or_path. The weights were fine-tuned on the fw1zr/rahul-gandhi-captions dataset."
] | [
"TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-pretrained_model_name_or_path #license-creativeml-openrail-m #region-us \n",
"# LoRA text2image fine-tuning - BootesVoid/rahul-gandhi-lora\nThese are LoRA adaption weights for pretrained_model_name_or_path. The weights were fine-tuned on the fw1zr/rahul-gandhi-captions dataset."
] | [
65,
72
] | [
"passage: TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-pretrained_model_name_or_path #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - BootesVoid/rahul-gandhi-lora\nThese are LoRA adaption weights for pretrained_model_name_or_path. The weights were fine-tuned on the fw1zr/rahul-gandhi-captions dataset."
] | [
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null | 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. -->
# ger-vit-model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0070
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1419 | 3.85 | 500 | 0.0070 | 1.0 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "ger-vit-model", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "beans", "type": "beans", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 1.0, "name": "Accuracy"}]}]}]} | image-classification | ger99/ger-vit-model | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-11T21:42:55+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-beans #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| ger-vit-model
=============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0070
* Accuracy: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
85,
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"passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-beans #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | null | ## LoRA LLAMA-7B Adapter Hate speech and violence Detection
Delve into the realm of content moderation with our LoRA LLAMA-7B Adapter, a specialized tool for detecting hate speech and violence in textual data. This repository demonstrates the integration of the Low-Rank Adaptation (LORA) technique with the Peft library to refine the LLAMA 7B model's ability to identify and assess aggressive and harmful content with precision.
## Adapter Description
Our adapter employs the LORA technique to tailor the LLAMA 7B model for the specialized task of detecting hate speech and violence within textual data. This adaptation underscores the versatility of advanced models in addressing critical issues by discerning and categorizing aggressive and harmful content.
## Data Description
Dataset used for training the above adapter (https://huggingface.co/datasets/hate_speech_offensive)
## Usage
This section provides a quick guide on how to use the LLAMA 7B LORA Adapter for hate speech and violence detection.
### Prerequisites
Before running the code, ensure you have installed the `transformers` and `peft` libraries. You can install them using pip:
```bash
pip install transformers peft
import transformers
from peft import PeftModel
# Model and tokenizer names
model_name = "meta-llama/Llama-2-7b" # You can also use Lymsys LLAMA-2 finetuned Vicuna model alternatively "lmsys/vicuna-7b-v1.5"
peft_model_id = "Futurix-AI/LORA_LLAMA_7B_HATE_SPEECH_VIOLENCE"
# Initialize the tokenizer and model
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, peft_model_id)
# Prompt for sentiment detection
prompt = """
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
###Instruction:
Detect the level of Hate Speech and Violence in the given text.
###Input:
FuturixAI embodies the spirit of innovation, with a resolve to push the boundaries of what's possible through science and technology.
###Response:
"""
# Tokenize the prompt and prepare inputs
inputs = tokenizer(prompt, return_tensors="pt")
for k, v in inputs.items():
inputs[k] = v
# Generate a response using the model
outputs = model.generate(**inputs, max_length=256, do_sample=True)
# Decode and print the response
text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(text)
| {} | null | Futurix-AI/LORA_LLAMA_7B_HATE_SPEECH_VIOLENCE | [
"region:us"
] | 2023-11-11T21:53:24+00:00 | [] | [] | TAGS
#region-us
| ## LoRA LLAMA-7B Adapter Hate speech and violence Detection
Delve into the realm of content moderation with our LoRA LLAMA-7B Adapter, a specialized tool for detecting hate speech and violence in textual data. This repository demonstrates the integration of the Low-Rank Adaptation (LORA) technique with the Peft library to refine the LLAMA 7B model's ability to identify and assess aggressive and harmful content with precision.
## Adapter Description
Our adapter employs the LORA technique to tailor the LLAMA 7B model for the specialized task of detecting hate speech and violence within textual data. This adaptation underscores the versatility of advanced models in addressing critical issues by discerning and categorizing aggressive and harmful content.
## Data Description
Dataset used for training the above adapter (URL
## Usage
This section provides a quick guide on how to use the LLAMA 7B LORA Adapter for hate speech and violence detection.
### Prerequisites
Before running the code, ensure you have installed the 'transformers' and 'peft' libraries. You can install them using pip:
'''bash
pip install transformers peft
import transformers
from peft import PeftModel
# Model and tokenizer names
model_name = "meta-llama/Llama-2-7b" # You can also use Lymsys LLAMA-2 finetuned Vicuna model alternatively "lmsys/vicuna-7b-v1.5"
peft_model_id = "Futurix-AI/LORA_LLAMA_7B_HATE_SPEECH_VIOLENCE"
# Initialize the tokenizer and model
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
model = PeftModel.from_pretrained(model, peft_model_id)
# Prompt for sentiment detection
prompt = """
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
###Instruction:
Detect the level of Hate Speech and Violence in the given text.
###Input:
FuturixAI embodies the spirit of innovation, with a resolve to push the boundaries of what's possible through science and technology.
###Response:
"""
# Tokenize the prompt and prepare inputs
inputs = tokenizer(prompt, return_tensors="pt")
for k, v in URL():
inputs[k] = v
# Generate a response using the model
outputs = model.generate(inputs, max_length=256, do_sample=True)
# Decode and print the response
text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(text)
| [
"## LoRA LLAMA-7B Adapter Hate speech and violence Detection \n\n\nDelve into the realm of content moderation with our LoRA LLAMA-7B Adapter, a specialized tool for detecting hate speech and violence in textual data. This repository demonstrates the integration of the Low-Rank Adaptation (LORA) technique with the Peft library to refine the LLAMA 7B model's ability to identify and assess aggressive and harmful content with precision.",
"## Adapter Description \n\nOur adapter employs the LORA technique to tailor the LLAMA 7B model for the specialized task of detecting hate speech and violence within textual data. This adaptation underscores the versatility of advanced models in addressing critical issues by discerning and categorizing aggressive and harmful content.",
"## Data Description \n\nDataset used for training the above adapter (URL",
"## Usage\n\nThis section provides a quick guide on how to use the LLAMA 7B LORA Adapter for hate speech and violence detection.",
"### Prerequisites\n\nBefore running the code, ensure you have installed the 'transformers' and 'peft' libraries. You can install them using pip:\n\n'''bash\npip install transformers peft\n\n\nimport transformers\nfrom peft import PeftModel",
"# Model and tokenizer names\nmodel_name = \"meta-llama/Llama-2-7b\" # You can also use Lymsys LLAMA-2 finetuned Vicuna model alternatively \"lmsys/vicuna-7b-v1.5\"\npeft_model_id = \"Futurix-AI/LORA_LLAMA_7B_HATE_SPEECH_VIOLENCE\"",
"# Initialize the tokenizer and model\ntokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\nmodel = transformers.AutoModelForCausalLM.from_pretrained(model_name)\nmodel = PeftModel.from_pretrained(model, peft_model_id)",
"# Prompt for sentiment detection\nprompt = \"\"\"\nBelow is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.",
"# Tokenize the prompt and prepare inputs\ninputs = tokenizer(prompt, return_tensors=\"pt\")\nfor k, v in URL():\n inputs[k] = v",
"# Generate a response using the model\noutputs = model.generate(inputs, max_length=256, do_sample=True)",
"# Decode and print the response\ntext = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\nprint(text)"
] | [
"TAGS\n#region-us \n",
"## LoRA LLAMA-7B Adapter Hate speech and violence Detection \n\n\nDelve into the realm of content moderation with our LoRA LLAMA-7B Adapter, a specialized tool for detecting hate speech and violence in textual data. This repository demonstrates the integration of the Low-Rank Adaptation (LORA) technique with the Peft library to refine the LLAMA 7B model's ability to identify and assess aggressive and harmful content with precision.",
"## Adapter Description \n\nOur adapter employs the LORA technique to tailor the LLAMA 7B model for the specialized task of detecting hate speech and violence within textual data. This adaptation underscores the versatility of advanced models in addressing critical issues by discerning and categorizing aggressive and harmful content.",
"## Data Description \n\nDataset used for training the above adapter (URL",
"## Usage\n\nThis section provides a quick guide on how to use the LLAMA 7B LORA Adapter for hate speech and violence detection.",
"### Prerequisites\n\nBefore running the code, ensure you have installed the 'transformers' and 'peft' libraries. You can install them using pip:\n\n'''bash\npip install transformers peft\n\n\nimport transformers\nfrom peft import PeftModel",
"# Model and tokenizer names\nmodel_name = \"meta-llama/Llama-2-7b\" # You can also use Lymsys LLAMA-2 finetuned Vicuna model alternatively \"lmsys/vicuna-7b-v1.5\"\npeft_model_id = \"Futurix-AI/LORA_LLAMA_7B_HATE_SPEECH_VIOLENCE\"",
"# Initialize the tokenizer and model\ntokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\nmodel = transformers.AutoModelForCausalLM.from_pretrained(model_name)\nmodel = PeftModel.from_pretrained(model, peft_model_id)",
"# Prompt for sentiment detection\nprompt = \"\"\"\nBelow is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.",
"# Tokenize the prompt and prepare inputs\ninputs = tokenizer(prompt, return_tensors=\"pt\")\nfor k, v in URL():\n inputs[k] = v",
"# Generate a response using the model\noutputs = model.generate(inputs, max_length=256, do_sample=True)",
"# Decode and print the response\ntext = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\nprint(text)"
] | [
6,
107,
69,
13,
30,
57,
88,
76,
47,
44,
36,
41
] | [
"passage: TAGS\n#region-us \n## LoRA LLAMA-7B Adapter Hate speech and violence Detection \n\n\nDelve into the realm of content moderation with our LoRA LLAMA-7B Adapter, a specialized tool for detecting hate speech and violence in textual data. This repository demonstrates the integration of the Low-Rank Adaptation (LORA) technique with the Peft library to refine the LLAMA 7B model's ability to identify and assess aggressive and harmful content with precision.## Adapter Description \n\nOur adapter employs the LORA technique to tailor the LLAMA 7B model for the specialized task of detecting hate speech and violence within textual data. This adaptation underscores the versatility of advanced models in addressing critical issues by discerning and categorizing aggressive and harmful content.## Data Description \n\nDataset used for training the above adapter (URL## Usage\n\nThis section provides a quick guide on how to use the LLAMA 7B LORA Adapter for hate speech and violence detection.### Prerequisites\n\nBefore running the code, ensure you have installed the 'transformers' and 'peft' libraries. You can install them using pip:\n\n'''bash\npip install transformers peft\n\n\nimport transformers\nfrom peft import PeftModel# Model and tokenizer names\nmodel_name = \"meta-llama/Llama-2-7b\" # You can also use Lymsys LLAMA-2 finetuned Vicuna model alternatively \"lmsys/vicuna-7b-v1.5\"\npeft_model_id = \"Futurix-AI/LORA_LLAMA_7B_HATE_SPEECH_VIOLENCE\"# Initialize the tokenizer and model\ntokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\nmodel = transformers.AutoModelForCausalLM.from_pretrained(model_name)\nmodel = PeftModel.from_pretrained(model, peft_model_id)# Prompt for sentiment detection\nprompt = \"\"\"\nBelow is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request."
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null | 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. -->
# DeitSonuclarFold3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7576
- Accuracy: 0.9070
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1564 | 1.0 | 54 | 0.3747 | 0.9070 |
| 0.0369 | 1.99 | 108 | 1.1971 | 0.8372 |
| 0.0392 | 2.99 | 162 | 0.5923 | 0.8140 |
| 0.0306 | 4.0 | 217 | 0.4089 | 0.9070 |
| 0.0102 | 5.0 | 271 | 1.0018 | 0.7674 |
| 0.0114 | 5.99 | 325 | 0.4262 | 0.8140 |
| 0.0017 | 6.99 | 379 | 0.5766 | 0.8605 |
| 0.0093 | 8.0 | 434 | 0.6440 | 0.8605 |
| 0.0027 | 9.0 | 488 | 0.5956 | 0.8140 |
| 0.001 | 9.99 | 542 | 0.4493 | 0.8837 |
| 0.0037 | 10.99 | 596 | 0.7511 | 0.7907 |
| 0.0189 | 12.0 | 651 | 0.4086 | 0.8837 |
| 0.0054 | 13.0 | 705 | 0.9063 | 0.8372 |
| 0.0035 | 13.99 | 759 | 0.5860 | 0.8605 |
| 0.0002 | 14.99 | 813 | 0.6873 | 0.8837 |
| 0.0 | 16.0 | 868 | 0.6735 | 0.9070 |
| 0.0 | 17.0 | 922 | 0.6811 | 0.9070 |
| 0.0 | 17.99 | 976 | 0.6864 | 0.9070 |
| 0.0 | 18.99 | 1030 | 0.6913 | 0.9070 |
| 0.0 | 20.0 | 1085 | 0.6959 | 0.9070 |
| 0.0 | 21.0 | 1139 | 0.7002 | 0.9070 |
| 0.0 | 21.99 | 1193 | 0.7041 | 0.9070 |
| 0.0 | 22.99 | 1247 | 0.7077 | 0.9070 |
| 0.0 | 24.0 | 1302 | 0.7113 | 0.9070 |
| 0.0 | 25.0 | 1356 | 0.7145 | 0.9070 |
| 0.0 | 25.99 | 1410 | 0.7179 | 0.9070 |
| 0.0 | 26.99 | 1464 | 0.7207 | 0.9070 |
| 0.0 | 28.0 | 1519 | 0.7235 | 0.9070 |
| 0.0 | 29.0 | 1573 | 0.7264 | 0.9070 |
| 0.0 | 29.99 | 1627 | 0.7291 | 0.9070 |
| 0.0 | 30.99 | 1681 | 0.7316 | 0.9070 |
| 0.0 | 32.0 | 1736 | 0.7340 | 0.9070 |
| 0.0 | 33.0 | 1790 | 0.7363 | 0.9070 |
| 0.0 | 33.99 | 1844 | 0.7385 | 0.9070 |
| 0.0 | 34.99 | 1898 | 0.7404 | 0.9070 |
| 0.0 | 36.0 | 1953 | 0.7425 | 0.9070 |
| 0.0 | 37.0 | 2007 | 0.7443 | 0.9070 |
| 0.0 | 37.99 | 2061 | 0.7461 | 0.9070 |
| 0.0 | 38.99 | 2115 | 0.7479 | 0.9070 |
| 0.0 | 40.0 | 2170 | 0.7494 | 0.9070 |
| 0.0 | 41.0 | 2224 | 0.7509 | 0.9070 |
| 0.0 | 41.99 | 2278 | 0.7521 | 0.9070 |
| 0.0 | 42.99 | 2332 | 0.7534 | 0.9070 |
| 0.0 | 44.0 | 2387 | 0.7544 | 0.9070 |
| 0.0 | 45.0 | 2441 | 0.7554 | 0.9070 |
| 0.0 | 45.99 | 2495 | 0.7562 | 0.9070 |
| 0.0 | 46.99 | 2549 | 0.7568 | 0.9070 |
| 0.0 | 48.0 | 2604 | 0.7573 | 0.9070 |
| 0.0 | 49.0 | 2658 | 0.7575 | 0.9070 |
| 0.0 | 49.77 | 2700 | 0.7576 | 0.9070 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-base-patch16-224", "model-index": [{"name": "DeitSonuclarFold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9069767441860465, "name": "Accuracy"}]}]}]} | image-classification | onizukal/DeitSonuclarFold3 | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-11T21:53:38+00:00 | [] | [] | TAGS
#transformers #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeitSonuclarFold3
=================
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7576
* Accuracy: 0.9070
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: 0.001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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30
] | [
"passage: TAGS\n#transformers #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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null | null | diffusers |
# LoRA text2image fine-tuning - vlipovoy/cartoon-blip-captions
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Norod78/cartoon-blip-captions dataset. You can find some example images in the following.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true} | text-to-image | vlipovoy/cartoon-blip-captions | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-11T21:59:59+00:00 | [] | [] | TAGS
#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
|
# LoRA text2image fine-tuning - vlipovoy/cartoon-blip-captions
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Norod78/cartoon-blip-captions dataset. You can find some example images in the following.
!img_0
!img_1
!img_2
!img_3
| [
"# LoRA text2image fine-tuning - vlipovoy/cartoon-blip-captions\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Norod78/cartoon-blip-captions dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3"
] | [
"TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n",
"# LoRA text2image fine-tuning - vlipovoy/cartoon-blip-captions\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Norod78/cartoon-blip-captions dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3"
] | [
72,
101
] | [
"passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - vlipovoy/cartoon-blip-captions\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Norod78/cartoon-blip-captions dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3"
] | [
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null | null | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PolicyGradient", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "38.80 +/- 44.12", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | conlan/Reinforce-Pixelcopter-PolicyGradient | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-11T22:02:59+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
41,
58
] | [
"passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Mazin100/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Mazin100/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-11T22:16:22+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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] |
null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Mazin100/taxi_RL", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "taxi_RL", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.50 +/- 2.70", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | Mazin100/taxi_RL | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-11T22:19:07+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | 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. -->
# RuBioRoBERTatutorial_data
This model is a fine-tuned version of [alexyalunin/RuBioRoBERTa](https://huggingface.co/alexyalunin/RuBioRoBERTa) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2763
- Precision: 0.4785
- Recall: 0.48
- F1: 0.4793
- Accuracy: 0.9165
## 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: 3
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.94 | 50 | 0.5960 | 0.0 | 0.0 | 0.0 | 0.8255 |
| No log | 1.89 | 100 | 0.7538 | 0.0 | 0.0 | 0.0 | 0.8282 |
| No log | 2.83 | 150 | 0.5480 | 0.0 | 0.0 | 0.0 | 0.8282 |
| No log | 3.77 | 200 | 0.5652 | 0.0 | 0.0 | 0.0 | 0.8282 |
| No log | 4.72 | 250 | 0.5420 | 0.0320 | 0.0738 | 0.0447 | 0.8073 |
| No log | 5.66 | 300 | 0.2975 | 0.2133 | 0.2369 | 0.2245 | 0.8936 |
| No log | 6.6 | 350 | 0.2706 | 0.3166 | 0.4462 | 0.3704 | 0.8966 |
| No log | 7.55 | 400 | 0.2688 | 0.3575 | 0.44 | 0.3945 | 0.8993 |
| No log | 8.49 | 450 | 0.2758 | 0.4475 | 0.4062 | 0.4258 | 0.9148 |
| 0.4814 | 9.43 | 500 | 0.2384 | 0.4504 | 0.5446 | 0.4930 | 0.9194 |
| 0.4814 | 10.38 | 550 | 0.3019 | 0.4136 | 0.5446 | 0.4701 | 0.9023 |
| 0.4814 | 11.32 | 600 | 0.3080 | 0.3801 | 0.5754 | 0.4578 | 0.8955 |
| 0.4814 | 12.26 | 650 | 0.2763 | 0.4785 | 0.48 | 0.4793 | 0.9165 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "alexyalunin/RuBioRoBERTa", "model-index": [{"name": "RuBioRoBERTatutorial_data", "results": []}]} | token-classification | DimasikKurd/RuBioRoBERTatutorial_data | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"base_model:alexyalunin/RuBioRoBERTa",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-11T22:21:18+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #token-classification #generated_from_trainer #base_model-alexyalunin/RuBioRoBERTa #autotrain_compatible #endpoints_compatible #region-us
| RuBioRoBERTatutorial\_data
==========================
This model is a fine-tuned version of alexyalunin/RuBioRoBERTa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2763
* Precision: 0.4785
* Recall: 0.48
* F1: 0.4793
* Accuracy: 0.9165
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: 3
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | null | This is openchat's [openchat_3.5](https://huggingface.co/openchat/openchat_3.5), converted to GGUF without quantization. No other changes were made.
The model was converted using `convert.py` from Georgi Gerganov's llama.cpp repo as of commit `#e86fc56`.
All credit belongs to [openchat](https://huggingface.co/openchat) for fine-tuning and releasing this model. Thank you! | {"license": "apache-2.0", "pipeline_tag": "text-generation"} | text-generation | ddh0/openchat_3.5-GGUF-fp16 | [
"gguf",
"text-generation",
"license:apache-2.0",
"region:us"
] | 2023-11-11T22:22:33+00:00 | [] | [] | TAGS
#gguf #text-generation #license-apache-2.0 #region-us
| This is openchat's openchat_3.5, converted to GGUF without quantization. No other changes were made.
The model was converted using 'URL' from Georgi Gerganov's URL repo as of commit '#e86fc56'.
All credit belongs to openchat for fine-tuning and releasing this model. Thank you! | [] | [
"TAGS\n#gguf #text-generation #license-apache-2.0 #region-us \n"
] | [
22
] | [
"passage: TAGS\n#gguf #text-generation #license-apache-2.0 #region-us \n"
] | [
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null | null | transformers |
# faster-whisper-large-v3
This is the model Whisper large-v3 converted to be used in [faster-whisper](https://github.com/guillaumekln/faster-whisper).
## Using
You can choose between monkey-patching faster-whisper 0.9.0 (while they don't update it) or using my fork (which is
easier).
### Using my fork
First, install it by executing:
```shell
pip install -U 'transformers[torch]>=4.35.0' https://github.com/PythonicCafe/faster-whisper/archive/refs/heads/feature/large-v3.zip#egg=faster-whisper
```
Then, use it as the regular faster-whisper:
```python
import time
import faster_whisper
filename = "my-audio.mp3"
initial_prompt = "My podcast recording" # Or `None`
word_timestamps = False
vad_filter = True
temperature = 0.0
language = "pt"
model_size = "large-v3"
device, compute_type = "cuda", "float16"
# or: device, compute_type = "cpu", "float32"
model = faster_whisper.WhisperModel(model_size, device=device, compute_type=compute_type)
segments, transcription_info = model.transcribe(
filename,
word_timestamps=word_timestamps,
vad_filter=vad_filter,
temperature=temperature,
language=language,
initial_prompt=initial_prompt,
)
print(transcription_info)
start_time = time.time()
for segment in segments:
row = {
"start": segment.start,
"end": segment.end,
"text": segment.text,
}
if word_timestamps:
row["words"] = [
{"start": word.start, "end": word.end, "word": word.word}
for word in segment.words
]
print(row)
end_time = time.time()
print(f"Transcription finished in {end_time - start_time:.2f}s")
```
### Monkey-patching faster-whisper 0.9.0
Make sure you have the latest version:
```shell
pip install -U 'faster-whisper>=0.9.0'
```
Then, use it with some little changes:
```python
import time
import faster_whisper.transcribe
# Monkey patch 1 (add model to list)
faster_whisper.utils._MODELS["large-v3"] = "turicas/faster-whisper-large-v3"
# Monkey patch 2 (fix Tokenizer)
faster_whisper.transcribe.Tokenizer.encode = lambda self, text: self.tokenizer.encode(text, add_special_tokens=False)
filename = "my-audio.mp3"
initial_prompt = "My podcast recording" # Or `None`
word_timestamps = False
vad_filter = True
temperature = 0.0
language = "pt"
model_size = "large-v3"
device, compute_type = "cuda", "float16"
# or: device, compute_type = "cpu", "float32"
model = faster_whisper.transcribe.WhisperModel(model_size, device=device, compute_type=compute_type)
# Monkey patch 3 (change n_mels)
from faster_whisper.feature_extractor import FeatureExtractor
model.feature_extractor = FeatureExtractor(feature_size=128)
# Monkey patch 4 (change tokenizer)
from transformers import AutoProcessor
model.hf_tokenizer = AutoProcessor.from_pretrained("openai/whisper-large-v3").tokenizer
model.hf_tokenizer.token_to_id = lambda token: model.hf_tokenizer.convert_tokens_to_ids(token)
segments, transcription_info = model.transcribe(
filename,
word_timestamps=word_timestamps,
vad_filter=vad_filter,
temperature=temperature,
language=language,
initial_prompt=initial_prompt,
)
print(transcription_info)
start_time = time.time()
for segment in segments:
row = {
"start": segment.start,
"end": segment.end,
"text": segment.text,
}
if word_timestamps:
row["words"] = [
{"start": word.start, "end": word.end, "word": word.word}
for word in segment.words
]
print(row)
end_time = time.time()
print(f"Transcription finished in {end_time - start_time:.2f}s")
```
## Converting
If you'd like to convert the model yourself, execute:
```shell
pip install -U 'ctranslate2>=3.21.0' 'transformers-4.35.0' 'OpenNMT-py==2.*' sentencepiece
ct2-transformers-converter --model openai/whisper-large-v3 --output_dir whisper-large-v3-ct2
```
Then, the files will be at `whisper-large-v3-ct2/`.
## License
These files have the same license as the original [openai/whisper-large-v3
model](https://huggingface.co/openai/whisper-large): Apache 2.0.
| {"license": "apache-2.0"} | null | turicas/faster-whisper-large-v3 | [
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-11T22:31:33+00:00 | [] | [] | TAGS
#transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# faster-whisper-large-v3
This is the model Whisper large-v3 converted to be used in faster-whisper.
## Using
You can choose between monkey-patching faster-whisper 0.9.0 (while they don't update it) or using my fork (which is
easier).
### Using my fork
First, install it by executing:
Then, use it as the regular faster-whisper:
### Monkey-patching faster-whisper 0.9.0
Make sure you have the latest version:
Then, use it with some little changes:
## Converting
If you'd like to convert the model yourself, execute:
Then, the files will be at 'whisper-large-v3-ct2/'.
## License
These files have the same license as the original openai/whisper-large-v3
model: Apache 2.0.
| [
"# faster-whisper-large-v3\n\nThis is the model Whisper large-v3 converted to be used in faster-whisper.",
"## Using\n\nYou can choose between monkey-patching faster-whisper 0.9.0 (while they don't update it) or using my fork (which is\neasier).",
"### Using my fork\n\nFirst, install it by executing:\n\n\n\nThen, use it as the regular faster-whisper:",
"### Monkey-patching faster-whisper 0.9.0\n\nMake sure you have the latest version:\n\n\n\nThen, use it with some little changes:",
"## Converting\n\nIf you'd like to convert the model yourself, execute:\n\n\n\nThen, the files will be at 'whisper-large-v3-ct2/'.",
"## License\n\nThese files have the same license as the original openai/whisper-large-v3\nmodel: Apache 2.0."
] | [
"TAGS\n#transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# faster-whisper-large-v3\n\nThis is the model Whisper large-v3 converted to be used in faster-whisper.",
"## Using\n\nYou can choose between monkey-patching faster-whisper 0.9.0 (while they don't update it) or using my fork (which is\neasier).",
"### Using my fork\n\nFirst, install it by executing:\n\n\n\nThen, use it as the regular faster-whisper:",
"### Monkey-patching faster-whisper 0.9.0\n\nMake sure you have the latest version:\n\n\n\nThen, use it with some little changes:",
"## Converting\n\nIf you'd like to convert the model yourself, execute:\n\n\n\nThen, the files will be at 'whisper-large-v3-ct2/'.",
"## License\n\nThese files have the same license as the original openai/whisper-large-v3\nmodel: Apache 2.0."
] | [
25,
37,
41,
29,
33,
39,
29
] | [
"passage: TAGS\n#transformers #license-apache-2.0 #endpoints_compatible #region-us \n# faster-whisper-large-v3\n\nThis is the model Whisper large-v3 converted to be used in faster-whisper.## Using\n\nYou can choose between monkey-patching faster-whisper 0.9.0 (while they don't update it) or using my fork (which is\neasier).### Using my fork\n\nFirst, install it by executing:\n\n\n\nThen, use it as the regular faster-whisper:### Monkey-patching faster-whisper 0.9.0\n\nMake sure you have the latest version:\n\n\n\nThen, use it with some little changes:## Converting\n\nIf you'd like to convert the model yourself, execute:\n\n\n\nThen, the files will be at 'whisper-large-v3-ct2/'.## License\n\nThese files have the same license as the original openai/whisper-large-v3\nmodel: Apache 2.0."
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null | 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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# LoneStriker/Yi-34B-Spicyboros-3.1-LoRA
This model was fine-tuned on Spicyboros-3.1 dataset on top of the Yi-34B-Llama model.
## Model description
Base model [Yi-34B-Llama](https://huggingface.co/chargoddard/Yi-34B-Llama)
## Intended uses & limitations
See Yi license
## Training and evaluation data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1 | {"tags": ["generated_from_trainer"], "datasets": ["unalignment/spicy-3.1"], "model-index": [{"name": "airo-lora-out", "results": []}]} | text-generation | LoneStriker/Yi-34B-Spicyboros-3.1-LoRA | [
"transformers",
"llama",
"text-generation",
"generated_from_trainer",
"dataset:unalignment/spicy-3.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-11T22:32:24+00:00 | [] | [] | TAGS
#transformers #llama #text-generation #generated_from_trainer #dataset-unalignment/spicy-3.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
# LoneStriker/Yi-34B-Spicyboros-3.1-LoRA
This model was fine-tuned on Spicyboros-3.1 dataset on top of the Yi-34B-Llama model.
## Model description
Base model Yi-34B-Llama
## Intended uses & limitations
See Yi license
## Training and evaluation data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1 | [
"# LoneStriker/Yi-34B-Spicyboros-3.1-LoRA\n\nThis model was fine-tuned on Spicyboros-3.1 dataset on top of the Yi-34B-Llama model.",
"## Model description\n\nBase model Yi-34B-Llama",
"## Intended uses & limitations\n\nSee Yi license",
"## Training and evaluation data",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- total_train_batch_size: 4\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.34.1\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #llama #text-generation #generated_from_trainer #dataset-unalignment/spicy-3.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# LoneStriker/Yi-34B-Spicyboros-3.1-LoRA\n\nThis model was fine-tuned on Spicyboros-3.1 dataset on top of the Yi-34B-Llama model.",
"## Model description\n\nBase model Yi-34B-Llama",
"## Intended uses & limitations\n\nSee Yi license",
"## Training and evaluation data",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- total_train_batch_size: 4\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.34.1\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
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] | [
"passage: TAGS\n#transformers #llama #text-generation #generated_from_trainer #dataset-unalignment/spicy-3.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# LoneStriker/Yi-34B-Spicyboros-3.1-LoRA\n\nThis model was fine-tuned on Spicyboros-3.1 dataset on top of the Yi-34B-Llama model.## Model description\n\nBase model Yi-34B-Llama## Intended uses & limitations\n\nSee Yi license## Training and evaluation data## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- total_train_batch_size: 4\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.34.1\n- Pytorch 2.0.1+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | 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. -->
# test_repo
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.7892
- Validation Loss: 1.6001
- 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': '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': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.7892 | 1.6001 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "t5-small", "model-index": [{"name": "test_repo", "results": []}]} | text2text-generation | Michael-Vptn/test_repo | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-11T22:45:30+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| test\_repo
==========
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.7892
* Validation Loss: 1.6001
* 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': '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': 2e-05, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.35.0
* TensorFlow 2.14.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': 2e-05, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': 2e-05, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
75,
195,
4,
31
] | [
"passage: TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': 2e-05, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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] |
null | null | 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: dojix/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"]} | reinforcement-learning | dojix/ppo-Huggy | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | 2023-11-11T22:47:08+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us
|
# ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
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: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### 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 URL
2. Step 1: Find your model_id: dojix/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: dojix/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n",
"# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: dojix/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
44,
199
] | [
"passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: dojix/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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] |
null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="AperMesa/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | AperMesa/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-11T22:47:54+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="AperMesa/q-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | AperMesa/q-taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-11T22:52:02+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | 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. -->
# DeitSonuclarFold4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2977
- Accuracy: 0.9048
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1735 | 0.99 | 54 | 0.3384 | 0.8810 |
| 0.0644 | 1.99 | 109 | 0.7322 | 0.8333 |
| 0.0375 | 3.0 | 164 | 0.5655 | 0.8333 |
| 0.0303 | 4.0 | 219 | 0.4327 | 0.9048 |
| 0.0125 | 4.99 | 273 | 0.5293 | 0.8810 |
| 0.0088 | 5.99 | 328 | 0.6023 | 0.8333 |
| 0.0082 | 7.0 | 383 | 0.4273 | 0.9048 |
| 0.0153 | 8.0 | 438 | 0.3207 | 0.9286 |
| 0.0081 | 8.99 | 492 | 0.4235 | 0.9048 |
| 0.0066 | 9.99 | 547 | 0.4535 | 0.9048 |
| 0.0055 | 11.0 | 602 | 0.3329 | 0.9048 |
| 0.0082 | 12.0 | 657 | 0.9925 | 0.8095 |
| 0.0029 | 12.99 | 711 | 0.7740 | 0.8571 |
| 0.0024 | 13.99 | 766 | 0.2742 | 0.9048 |
| 0.0009 | 15.0 | 821 | 0.1458 | 0.9524 |
| 0.0001 | 16.0 | 876 | 0.2134 | 0.9286 |
| 0.0 | 16.99 | 930 | 0.2303 | 0.9286 |
| 0.0 | 17.99 | 985 | 0.2405 | 0.9286 |
| 0.0 | 19.0 | 1040 | 0.2482 | 0.9286 |
| 0.0 | 20.0 | 1095 | 0.2543 | 0.9286 |
| 0.0 | 20.99 | 1149 | 0.2591 | 0.9286 |
| 0.0 | 21.99 | 1204 | 0.2633 | 0.9286 |
| 0.0 | 23.0 | 1259 | 0.2672 | 0.9286 |
| 0.0 | 24.0 | 1314 | 0.2703 | 0.9286 |
| 0.0 | 24.99 | 1368 | 0.2731 | 0.9286 |
| 0.0 | 25.99 | 1423 | 0.2756 | 0.9286 |
| 0.0 | 27.0 | 1478 | 0.2779 | 0.9286 |
| 0.0 | 28.0 | 1533 | 0.2797 | 0.9286 |
| 0.0 | 28.99 | 1587 | 0.2813 | 0.9286 |
| 0.0 | 29.99 | 1642 | 0.2829 | 0.9286 |
| 0.0 | 31.0 | 1697 | 0.2849 | 0.9286 |
| 0.0 | 32.0 | 1752 | 0.2864 | 0.9286 |
| 0.0 | 32.99 | 1806 | 0.2877 | 0.9286 |
| 0.0 | 33.99 | 1861 | 0.2886 | 0.9286 |
| 0.0 | 35.0 | 1916 | 0.2899 | 0.9286 |
| 0.0 | 36.0 | 1971 | 0.2912 | 0.9048 |
| 0.0 | 36.99 | 2025 | 0.2920 | 0.9048 |
| 0.0 | 37.99 | 2080 | 0.2930 | 0.9048 |
| 0.0 | 39.0 | 2135 | 0.2934 | 0.9048 |
| 0.0 | 40.0 | 2190 | 0.2946 | 0.9048 |
| 0.0 | 40.99 | 2244 | 0.2948 | 0.9048 |
| 0.0 | 41.99 | 2299 | 0.2958 | 0.9048 |
| 0.0 | 43.0 | 2354 | 0.2962 | 0.9048 |
| 0.0 | 44.0 | 2409 | 0.2965 | 0.9048 |
| 0.0 | 44.99 | 2463 | 0.2969 | 0.9048 |
| 0.0 | 45.99 | 2518 | 0.2974 | 0.9048 |
| 0.0 | 47.0 | 2573 | 0.2976 | 0.9048 |
| 0.0 | 48.0 | 2628 | 0.2977 | 0.9048 |
| 0.0 | 48.99 | 2682 | 0.2977 | 0.9048 |
| 0.0 | 49.32 | 2700 | 0.2977 | 0.9048 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-base-patch16-224", "model-index": [{"name": "DeitSonuclarFold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9047619047619048, "name": "Accuracy"}]}]}]} | image-classification | onizukal/DeitSonuclarFold4 | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-11T22:58:06+00:00 | [] | [] | TAGS
#transformers #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeitSonuclarFold4
=================
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2977
* Accuracy: 0.9048
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: 0.001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
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"### Training results",
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] | [
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null | null | null | I DID NOT MAKE THIS MODEL!! It was made by VeniTheKitty on discord.
All I am doing is uploading it to hugging face for use with the RVC Google Colab Notebook. | {"license": "openrail"} | null | xunnylee/tenko-chabishira-Venithekittys-model | [
"license:openrail",
"region:us"
] | 2023-11-11T23:21:13+00:00 | [] | [] | TAGS
#license-openrail #region-us
| I DID NOT MAKE THIS MODEL!! It was made by VeniTheKitty on discord.
All I am doing is uploading it to hugging face for use with the RVC Google Colab Notebook. | [] | [
"TAGS\n#license-openrail #region-us \n"
] | [
12
] | [
"passage: TAGS\n#license-openrail #region-us \n"
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] |
null | null | null | <div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://huggingface.co/Ichsan2895/Merak-7B-v4/resolve/main/FINAL_LOGO/6.png" alt="MERAK" style="width: 50%; min-width: 100px; display: block; margin: auto;">
</div>
# HAPPY TO ANNOUNCE THE RELEASE OF MERAK-7B-V4-GGUF!
Merak-7B is the Large Language Model of Indonesian Language
This model is based on [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and fine tuned by some of Indonesia Wikipedia articles that I cleaned before.
Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM
Merak-7B and all of its derivatives are Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0). Merak-7B empowers AI enthusiasts, researchers alike.
Big thanks to all my friends and communities that help to build our first model. Thanks for Axolotl for a great fine tuning tool which designed to streamline the fine-tuning of various AI models.
Feel free, to ask me about the model and please share the news on your social media.
## HOW TO USE
### What is GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
### What is the software that support GGUF
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
### Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
### Provided files
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [Merak-7B-v4-model-Q2_K.gguf](https://huggingface.co/Ichsan2895/Merak-7B-v4-GGUF/blob/main/Merak-7B-v4-model-q2_k.gguf) | Q2_K | 2 | 3.08 GB| smallest, significant quality loss - not recommended for most purposes |
| [Merak-7B-v4-model-Q3_K_M.gguf](https://huggingface.co/Ichsan2895/Merak-7B-v4-GGUF/blob/main/Merak-7B-v4-model-q3_k_m.gguf) | Q3_K_M | 3 | 3.52 GB| very small, high quality loss |
| [Merak-7B-v4-model-Q4_0.gguf](https://huggingface.co/Ichsan2895/Merak-7B-v4-GGUF/blob/main/Merak-7B-v4-model-q4_0.gguf) | Q4_0 | 4 | 4.11 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [Merak-7B-v4-model-Q4_K_M.gguf](https://huggingface.co/Ichsan2895/Merak-7B-v4-GGUF/blob/main/Merak-7B-v4-model-q4_k_m.gguf) | Q4_K_M | 4 | 4.37 GB| medium, balanced quality - recommended |
| [Merak-7B-v4-model-Q5_0.gguf](https://huggingface.co/Ichsan2895/Merak-7B-v4-GGUF/blob/main/Merak-7B-v4-model-q5_0.gguf) | Q5_0 | 5 | 5 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [Merak-7B-v4-model-Q5_K_M.gguf](https://huggingface.co/Ichsan2895/Merak-7B-v4-GGUF/blob/main/Merak-7B-v4-model-q5_k_m.gguf) | Q5_K_M | 5 | 5.13 GB| large, very low quality loss - recommended |
| [Merak-7B-v4-model-Q6_K.gguf](https://huggingface.co/Ichsan2895/Merak-7B-v4-GGUF/blob/main/Merak-7B-v4-model-q6_k.gguf) | Q6_K | 6 | 5.94 GB| very large, extremely low quality loss |
| [Merak-7B-v4-model-Q8_0.gguf](https://huggingface.co/Ichsan2895/Merak-7B-v4-GGUF/blob/main/Merak-7B-v4-model-q8_0.gguf) | Q8_0 | 8 | 7.7 GB| very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
</details>
## 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
### In `text-generation-webui`
Under Download Model, you can enter the model repo: Ichsan2895/Merak-7B-v4-GGUF and below it, a specific filename to download, such as: Merak-7B-v4-model-q5_k_m.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download Ichsan2895/Merak-7B-v4-GGUF Merak-7B-v4-model-q5_k_m.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download Ichsan2895/Merak-7B-v4-GGUF --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 Ichsan2895/Merak-7B-v4-GGUF Merak-7B-v4-model-q5_k_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 -->
## Example `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 32 -m Merak-7B-v4-model-q5_k_m.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` 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.
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)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run 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.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# 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 = AutoModelForCausalLM.from_pretrained("Ichsan2895/Merak-7B-v4-GGUF", model_file="Merak-7B-v4-model-q5_k_m.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use 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)
## CITATION
```
@software{lian2023mistralorca1
title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca},
}
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
@inproceedings{wolf-etal-2020-transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
@article{dettmers2023qlora,
title = {QLoRA: Efficient Finetuning of Quantized LLMs},
author = {Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal = {arXiv preprint arXiv:2305.14314},
year = {2023}
}
Special thanks to theBloke for his Readme.Md that We adopted in this model
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## HOW TO CITE THIS PROJECT
If you use the Merak-7B model in your research or project, please cite it as:
```
@article{Merak,
title={Merak-7B: The LLM for Bahasa Indonesia},
author={Muhammad Ichsan},
publisher={Hugging Face}
journal={Hugging Face Repository},
year={2023}
}
``` | {"language": ["id", "en"], "license": "cc-by-nc-sa-4.0", "datasets": ["wikipedia", "Ichsan2895/OASST_Top1_Indonesian", "Ichsan2895/alpaca-gpt4-indonesian"], "pipeline_tag": "text-generation", "other": "mistral"} | text-generation | Ichsan2895/Merak-7B-v4-GGUF | [
"gguf",
"text-generation",
"id",
"en",
"dataset:wikipedia",
"dataset:Ichsan2895/OASST_Top1_Indonesian",
"dataset:Ichsan2895/alpaca-gpt4-indonesian",
"arxiv:2306.02707",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2023-11-11T23:23:37+00:00 | [
"2306.02707"
] | [
"id",
"en"
] | TAGS
#gguf #text-generation #id #en #dataset-wikipedia #dataset-Ichsan2895/OASST_Top1_Indonesian #dataset-Ichsan2895/alpaca-gpt4-indonesian #arxiv-2306.02707 #license-cc-by-nc-sa-4.0 #region-us
|
HAPPY TO ANNOUNCE THE RELEASE OF MERAK-7B-V4-GGUF!
==================================================
Merak-7B is the Large Language Model of Indonesian Language
This model is based on Mistral-7B-OpenOrca and fine tuned by some of Indonesia Wikipedia articles that I cleaned before.
Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM
Merak-7B and all of its derivatives are Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0). Merak-7B empowers AI enthusiasts, researchers alike.
Big thanks to all my friends and communities that help to build our first model. Thanks for Axolotl for a great fine tuning tool which designed to streamline the fine-tuning of various AI models.
Feel free, to ask me about the model and please share the news on your social media.
HOW TO USE
----------
### What is GGUF
GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.
### What is the software that support GGUF
Here is an incomplate list of clients and libraries that are known to support GGUF:
* URL. The source project for GGUF. Offers a CLI and a server option.
* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
### Compatibility
These quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
### Provided files
Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
### Explanation of quantisation methods
Click to see details
The new methods available are:
* GGML\_TYPE\_Q2\_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML\_TYPE\_Q3\_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML\_TYPE\_Q4\_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML\_TYPE\_Q5\_K - "type-1" 5-bit quantization. Same super-block structure as GGML\_TYPE\_Q4\_K resulting in 5.5 bpw
* GGML\_TYPE\_Q6\_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
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
* URL
### In 'text-generation-webui'
Under Download Model, you can enter the model repo: Ichsan2895/Merak-7B-v4-GGUF and below it, a specific filename to download, such as: Merak-7B-v4-model-q5\_k\_m.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the 'huggingface-hub' Python library:
Then you can download any individual model file to the current directory, at high speed, with a command like this:
More advanced huggingface-cli download usage
You can also download multiple files at once with a pattern:
For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\_transfer':
And set environment variable 'HF\_HUB\_ENABLE\_HF\_TRANSFER' to '1':
Windows Command Line users: You can set the environment variable by running 'set HF\_HUB\_ENABLE\_HF\_TRANSFER=1' before the download command.
Example 'URL' command
---------------------
Make sure you are using 'URL' from commit d0cee0d or later.
Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change '-c 2048' 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 URL automatically.
If you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'
For other parameters and how to use them, please refer to the URL documentation
How to run in 'text-generation-webui'
-------------------------------------
Further instructions here: text-generation-webui/docs/URL.
How to run from Python code
---------------------------
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
#### Simple ctransformers example code
How to use with LangChain
-------------------------
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* LangChain + llama-cpp-python
* LangChain + ctransformers
CITATION
--------
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
HOW TO CITE THIS PROJECT
------------------------
If you use the Merak-7B model in your research or project, please cite it as:
| [
"### What is GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.",
"### What is the software that support GGUF\n\n\nHere is an incomplate list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.",
"### Compatibility\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.",
"### Provided files\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.",
"### Explanation of quantisation methods\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote 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.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: Ichsan2895/Merak-7B-v4-GGUF and below it, a specific filename to download, such as: Merak-7B-v4-model-q5\\_k\\_m.gguf.\n\n\nThen click Download.",
"### On the command line, including multiple files at once\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\nMore advanced huggingface-cli download usage\nYou can also download multiple files at once with a pattern:\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nExample 'URL' command\n---------------------\n\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\n\nChange '-c 2048' 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 URL automatically.\n\n\nIf you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'\n\n\nFor other parameters and how to use them, please refer to the URL documentation\n\n\nHow to run in 'text-generation-webui'\n-------------------------------------\n\n\nFurther instructions here: text-generation-webui/docs/URL.\n\n\nHow to run from Python code\n---------------------------\n\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.",
"### How to load this model in Python code, using ctransformers",
"#### First install the package\n\n\nRun one of the following commands, according to your system:",
"#### Simple ctransformers example code\n\n\nHow to use with LangChain\n-------------------------\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nCITATION\n--------\n\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>\n\n\nHOW TO CITE THIS PROJECT\n------------------------\n\n\nIf you use the Merak-7B model in your research or project, please cite it as:"
] | [
"TAGS\n#gguf #text-generation #id #en #dataset-wikipedia #dataset-Ichsan2895/OASST_Top1_Indonesian #dataset-Ichsan2895/alpaca-gpt4-indonesian #arxiv-2306.02707 #license-cc-by-nc-sa-4.0 #region-us \n",
"### What is GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.",
"### What is the software that support GGUF\n\n\nHere is an incomplate list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.",
"### Compatibility\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.",
"### Provided files\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.",
"### Explanation of quantisation methods\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote 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.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: Ichsan2895/Merak-7B-v4-GGUF and below it, a specific filename to download, such as: Merak-7B-v4-model-q5\\_k\\_m.gguf.\n\n\nThen click Download.",
"### On the command line, including multiple files at once\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\nMore advanced huggingface-cli download usage\nYou can also download multiple files at once with a pattern:\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nExample 'URL' command\n---------------------\n\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\n\nChange '-c 2048' 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 URL automatically.\n\n\nIf you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'\n\n\nFor other parameters and how to use them, please refer to the URL documentation\n\n\nHow to run in 'text-generation-webui'\n-------------------------------------\n\n\nFurther instructions here: text-generation-webui/docs/URL.\n\n\nHow to run from Python code\n---------------------------\n\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.",
"### How to load this model in Python code, using ctransformers",
"#### First install the package\n\n\nRun one of the following commands, according to your system:",
"#### Simple ctransformers example code\n\n\nHow to use with LangChain\n-------------------------\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nCITATION\n--------\n\n\n<img src=\"URL alt=\"Built with Axolotl\" width=\"200\" height=\"32\"/>\n\n\nHOW TO CITE THIS PROJECT\n------------------------\n\n\nIf you use the Merak-7B model in your research or project, please cite it as:"
] | [
80,
45,
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415,
76,
426,
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19,
125
] | [
"passage: TAGS\n#gguf #text-generation #id #en #dataset-wikipedia #dataset-Ichsan2895/OASST_Top1_Indonesian #dataset-Ichsan2895/alpaca-gpt4-indonesian #arxiv-2306.02707 #license-cc-by-nc-sa-4.0 #region-us \n### What is GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.### What is the software that support GGUF\n\n\nHere is an incomplate list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.### Compatibility\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.",
"passage: ### Provided files\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.### Explanation of quantisation methods\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote 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.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: Ichsan2895/Merak-7B-v4-GGUF and below it, a specific filename to download, such as: Merak-7B-v4-model-q5\\_k\\_m.gguf.\n\n\nThen click Download."
] | [
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null | null | 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. -->
# shungyan/my_awesome_qa_model
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:
- Train Loss: 2.7088
- Validation Loss: 2.7578
- 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', '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': 250, '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 | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.7120 | 2.7578 | 0 |
| 2.7178 | 2.7578 | 1 |
| 2.7088 | 2.7578 | 2 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "shungyan/my_awesome_qa_model", "results": []}]} | question-answering | shungyan/my_awesome_qa_model | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-11T23:24:43+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
| shungyan/my\_awesome\_qa\_model
===============================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.7088
* Validation Loss: 2.7578
* 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', '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': 250, '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
### Framework versions
* Transformers 4.35.0
* TensorFlow 2.14.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': 250, '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}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': 250, '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}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
63,
303,
4,
31
] | [
"passage: TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': 250, '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}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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. -->
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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]
### 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 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. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "huggyllama/llama-7b"} | null | MayIBorn/mrpc_qlora-llama-7b_init_A_with_svd_from_back_with_scaling_entire | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"region:us"
] | 2023-11-11T23:38:12+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-huggyllama/llama-7b #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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null | 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. -->
# DeitSonuclarFold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6219
- Accuracy: 0.9024
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.151 | 1.0 | 55 | 0.4907 | 0.8049 |
| 0.0455 | 2.0 | 110 | 1.6794 | 0.7073 |
| 0.0267 | 3.0 | 165 | 0.3723 | 0.9024 |
| 0.0181 | 4.0 | 220 | 1.1643 | 0.7561 |
| 0.0078 | 5.0 | 275 | 1.0360 | 0.8049 |
| 0.0024 | 6.0 | 330 | 0.7530 | 0.8537 |
| 0.0008 | 7.0 | 385 | 2.0256 | 0.7317 |
| 0.0128 | 8.0 | 440 | 1.5322 | 0.7317 |
| 0.0015 | 9.0 | 495 | 0.9955 | 0.8293 |
| 0.0109 | 10.0 | 550 | 1.4691 | 0.8049 |
| 0.0043 | 11.0 | 605 | 1.4713 | 0.7805 |
| 0.0152 | 12.0 | 660 | 0.8152 | 0.8293 |
| 0.0159 | 13.0 | 715 | 0.8534 | 0.9024 |
| 0.0064 | 14.0 | 770 | 0.6560 | 0.8293 |
| 0.0049 | 15.0 | 825 | 0.9902 | 0.8049 |
| 0.0001 | 16.0 | 880 | 0.4702 | 0.9024 |
| 0.0 | 17.0 | 935 | 0.4880 | 0.9024 |
| 0.0 | 18.0 | 990 | 0.5038 | 0.9024 |
| 0.0 | 19.0 | 1045 | 0.5154 | 0.9024 |
| 0.0 | 20.0 | 1100 | 0.5258 | 0.9024 |
| 0.0 | 21.0 | 1155 | 0.5342 | 0.9024 |
| 0.0 | 22.0 | 1210 | 0.5417 | 0.9024 |
| 0.0 | 23.0 | 1265 | 0.5484 | 0.9024 |
| 0.0 | 24.0 | 1320 | 0.5544 | 0.9024 |
| 0.0 | 25.0 | 1375 | 0.5602 | 0.9024 |
| 0.0 | 26.0 | 1430 | 0.5651 | 0.9024 |
| 0.0 | 27.0 | 1485 | 0.5701 | 0.9024 |
| 0.0 | 28.0 | 1540 | 0.5745 | 0.9024 |
| 0.0 | 29.0 | 1595 | 0.5785 | 0.9024 |
| 0.0 | 30.0 | 1650 | 0.5829 | 0.9024 |
| 0.0 | 31.0 | 1705 | 0.5866 | 0.9024 |
| 0.0 | 32.0 | 1760 | 0.5899 | 0.9024 |
| 0.0 | 33.0 | 1815 | 0.5933 | 0.9024 |
| 0.0 | 34.0 | 1870 | 0.5967 | 0.9024 |
| 0.0 | 35.0 | 1925 | 0.5996 | 0.9024 |
| 0.0 | 36.0 | 1980 | 0.6022 | 0.9024 |
| 0.0 | 37.0 | 2035 | 0.6050 | 0.9024 |
| 0.0 | 38.0 | 2090 | 0.6074 | 0.9024 |
| 0.0 | 39.0 | 2145 | 0.6097 | 0.9024 |
| 0.0 | 40.0 | 2200 | 0.6117 | 0.9024 |
| 0.0 | 41.0 | 2255 | 0.6138 | 0.9024 |
| 0.0 | 42.0 | 2310 | 0.6155 | 0.9024 |
| 0.0 | 43.0 | 2365 | 0.6170 | 0.9024 |
| 0.0 | 44.0 | 2420 | 0.6185 | 0.9024 |
| 0.0 | 45.0 | 2475 | 0.6196 | 0.9024 |
| 0.0 | 46.0 | 2530 | 0.6206 | 0.9024 |
| 0.0 | 47.0 | 2585 | 0.6212 | 0.9024 |
| 0.0 | 48.0 | 2640 | 0.6217 | 0.9024 |
| 0.0 | 49.0 | 2695 | 0.6219 | 0.9024 |
| 0.0 | 50.0 | 2750 | 0.6219 | 0.9024 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-base-patch16-224", "model-index": [{"name": "DeitSonuclarFold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9024390243902439, "name": "Accuracy"}]}]}]} | image-classification | onizukal/DeitSonuclarFold5 | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T00:02:27+00:00 | [] | [] | TAGS
#transformers #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| DeitSonuclarFold5
=================
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6219
* Accuracy: 0.9024
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: 0.001
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 50
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
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"### Training results",
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null | null | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="yupengchen/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | yupengchen/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-12T00:04:33+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-Instruct-v0.1-LC-PI-.5-noSW
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8995
## Model description
This model is a fine-tuning of Mistral-7B-Instruct-v0.1.
This FT was done with full attention (removing the 4k SWA).
This FT was using a Position Interpolation factor of 0.5 (Linear RoPE scaling).
Please note that the RoPE scaling factor should be determined by L/L' where L is the pre-training max context length and L' is the new max context length. In our case, we are just making experiments (and for us we would have had L/L' = 8096/7200 > 1 which did not require any PI scaling).
## Intended uses & limitations
More information needed
## Training and evaluation data
Data is a 9k sample from the RedPajama datset. The context is <=7200 with a decreasing exponential distribution of scale 1500.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1056 | 0.18 | 50 | 1.9680 |
| 2.1266 | 0.36 | 100 | 1.9213 |
| 1.978 | 0.55 | 150 | 1.9084 |
| 1.8576 | 0.73 | 200 | 1.9022 |
| 2.0311 | 0.91 | 250 | 1.8999 |
| 1.9197 | 1.09 | 300 | 1.8995 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "Mistral-7B-Instruct-v0.1-LC-PI-.5-noSW", "results": []}]} | null | sade-adrien/Mistral-7B-Instruct-v0.1-LC-PI-.5-noSW | [
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | 2023-11-12T00:08:36+00:00 | [] | [] | TAGS
#generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us
| Mistral-7B-Instruct-v0.1-LC-PI-.5-noSW
======================================
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8995
Model description
-----------------
This model is a fine-tuning of Mistral-7B-Instruct-v0.1.
This FT was done with full attention (removing the 4k SWA).
This FT was using a Position Interpolation factor of 0.5 (Linear RoPE scaling).
Please note that the RoPE scaling factor should be determined by L/L' where L is the pre-training max context length and L' is the new max context length. In our case, we are just making experiments (and for us we would have had L/L' = 8096/7200 > 1 which did not require any PI scaling).
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
Data is a 9k sample from the RedPajama datset. The context is <=7200 with a decreasing exponential distribution of scale 1500.
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 32
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 20
* training\_steps: 300
### Training results
### Framework versions
* Transformers 4.34.1
* Pytorch 2.0.0+cu117
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 300",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.0.0+cu117\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 300",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.0.0+cu117\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 32\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 300### Training results### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.0.0+cu117\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | 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. -->
# roberta-base-bne-finetuned-Analisis_De_Sentimientos
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9565
- F1: 0.5966
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9645 | 1.0 | 657 | 0.9270 | 0.5825 |
| 0.6996 | 2.0 | 1314 | 0.9565 | 0.5966 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "roberta-base-bne-finetuned-Analisis_De_Sentimientos", "results": []}]} | text-classification | DataPath/roberta-base-bne-finetuned-Analisis_De_Sentimientos | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T00:09:10+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-bne-finetuned-Analisis\_De\_Sentimientos
=====================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9565
* F1: 0.5966
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: 2
### Training results
### Framework versions
* Transformers 4.28.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.13.3
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
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null | null | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="yupengchen/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | yupengchen/q-Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-12T00:15:57+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | 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. -->
# borough-oblast/distilbert-base-uncased-finetuned-imdb
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:
- Train Loss: 0.0006
- Validation Loss: 0.0004
- 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': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -687, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.0006 | 0.0004 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "borough-oblast/distilbert-base-uncased-finetuned-imdb", "results": []}]} | fill-mask | borough-oblast/distilbert-base-uncased-finetuned-imdb | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T00:19:31+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| borough-oblast/distilbert-base-uncased-finetuned-imdb
=====================================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.0006
* Validation Loss: 0.0004
* 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': 'transformers.optimization\_tf', 'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_schedule\_fn': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': -687, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'warmup\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\_name': 'WarmUp'}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.35.0
* TensorFlow 2.14.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': -687, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': -687, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
70,
333,
4,
31
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"passage: TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': -687, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | transformers | <!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Mistral 7B OpenOrca oasst Top1 2023 08 25 v1 - GGUF
- Model creator: [Nicky](https://huggingface.co/NickyNicky)
- Original model: [Mistral 7B OpenOrca oasst Top1 2023 08 25 v1](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF)
* [Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF/blob/main/mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## 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
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF and below it, a specific filename to download, such as: mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF --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 TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_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 -->
## Example `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 32 -m mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` 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.
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)
## How to run 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%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run 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.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# 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 = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF", model_file="mistral-7b-openorca-oasst_top1_2023-08-25-v1.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use 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)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
```
reference-data-model:
datasets:
- OpenAssistant/oasst_top1_2023-08-25:
Lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
Link: https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25
model:
- Open-Orca/Mistral-7B-OpenOrca
Link: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
100 examples of generating:
Link: https://docs.google.com/spreadsheets/d/1_4rqFnhgvjA7trwAaEidaRWczAMzuKpw/edit?usp=sharing&ouid=116592149115238887304&rtpof=true&sd=true
Version 2:
Link: https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2
```
## Version
```py
import torch, transformers,torchvision
torch.__version__,transformers.__version__, torchvision.__version__
#OUTPUTS: ('2.0.1+cu118', '4.34.0.dev0', '0.15.2+cu118')
```
## How to use
```py
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
GenerationConfig,
TextIteratorStreamer,
)
import torch
# model_id = 'Open-Orca/Mistral-7B-OpenOrca'
model_id='NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1'
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
load_in_4bit=True,
low_cpu_mem_usage= True,
)
max_length=2048
print("max_length",max_length)
tokenizer = AutoTokenizer.from_pretrained(model_id,
# use_fast = False,
max_length=max_length,)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'right'
#EXAMPLE #1
txt="""<|im_start|>user
I'm looking for an efficient Python script to output prime numbers. Can you help me out? I'm interested in a script that can handle large numbers and output them quickly. Also, it would be great if the script could take a range of numbers as input and output all the prime numbers within that range. Can you generate a script that fits these requirements? Thanks!<|im_end|>
<|im_start|>assistant
"""
#EXAMPLE #2
txt="""<|im_start|>user
Estoy desarrollando una REST API con Nodejs, y estoy tratando de aplicar algún sistema de seguridad, ya sea con tokens o algo similar, me puedes ayudar?<|im_end|>
<|im_start|>assistant
"""
inputs = tokenizer.encode(txt, return_tensors="pt").to("cuda")
generation_config = GenerationConfig(
max_new_tokens=max_new_tokens,
temperature=0.7,
top_p=0.9,
top_k=len_tokens,
repetition_penalty=1.11,
do_sample=True,
# pad_token_id=tokenizer.eos_token_id,
# eos_token_id=tokenizer.eos_token_id,
# use_cache=True,
# stopping_criteria= StoppingCriteriaList([stopping_criteria]),
)
outputs = model.generate(generation_config=generation_config,
input_ids=inputs,)
tokenizer.decode(outputs[0], skip_special_tokens=False) #True
```
<!-- original-model-card end -->
| {"language": ["bg", "ca", "cs", "da", "de", "en", "es", "fr", "hr", "hu", "it", "nl", "pl", "pt", "ro", "ru", "sl", "sr", "sv", "uk"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["Open-Orca/OpenOrca", "OpenAssistant/oasst_top1_2023-08-25"], "model_name": "Mistral 7B OpenOrca oasst Top1 2023 08 25 v1", "base_model": "NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1", "inference": false, "model_creator": "Nicky", "model_type": "mistral", "prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", "quantized_by": "TheBloke"} | null | TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF | [
"transformers",
"gguf",
"mistral",
"bg",
"ca",
"cs",
"da",
"de",
"en",
"es",
"fr",
"hr",
"hu",
"it",
"nl",
"pl",
"pt",
"ro",
"ru",
"sl",
"sr",
"sv",
"uk",
"dataset:Open-Orca/OpenOrca",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"base_model:NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | 2023-11-12T00:21:39+00:00 | [] | [
"bg",
"ca",
"cs",
"da",
"de",
"en",
"es",
"fr",
"hr",
"hu",
"it",
"nl",
"pl",
"pt",
"ro",
"ru",
"sl",
"sr",
"sv",
"uk"
] | TAGS
#transformers #gguf #mistral #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #text-generation-inference #region-us
|
[[TheBloke's LLM work is generously supported by a grant from [andreessen horowitz (a16z)](URL)](URL to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style=)](URL & support: TheBloke's Discord server</a></p>
</div>
<div style=)
---
Mistral 7B OpenOrca oasst Top1 2023 08 25 v1 - GGUF
===================================================
* Model creator: Nicky
* Original model: Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
Description
-----------
This repo contains GGUF format model files for Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1.
These files were quantised using hardware kindly provided by Massed Compute.
### About GGUF
GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* URL. The source project for GGUF. Offers a CLI and a server option.
* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
Repositories available
----------------------
* AWQ model(s) for GPU inference.
* GPTQ models for GPU inference, with multiple quantisation parameter options.
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: ChatML
-----------------------
Compatibility
-------------
These quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
Explanation of quantisation methods
-----------------------------------
Click to see details
The new methods available are:
* GGML\_TYPE\_Q2\_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML\_TYPE\_Q3\_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML\_TYPE\_Q4\_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML\_TYPE\_Q5\_K - "type-1" 5-bit quantization. Same super-block structure as GGML\_TYPE\_Q4\_K resulting in 5.5 bpw
* GGML\_TYPE\_Q6\_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
Provided files
--------------
Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
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
* URL
### In 'text-generation-webui'
Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-GGUF and below it, a specific filename to download, such as: mistral-7b-openorca-oasst\_top1\_2023-08-25-v1.Q4\_K\_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the 'huggingface-hub' Python library:
Then you can download any individual model file to the current directory, at high speed, with a command like this:
More advanced huggingface-cli download usage
You can also download multiple files at once with a pattern:
For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\_transfer':
And set environment variable 'HF\_HUB\_ENABLE\_HF\_TRANSFER' to '1':
Windows Command Line users: You can set the environment variable by running 'set HF\_HUB\_ENABLE\_HF\_TRANSFER=1' before the download command.
Example 'URL' command
---------------------
Make sure you are using 'URL' from commit d0cee0d or later.
Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change '-c 2048' 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 URL automatically.
If you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'
For other parameters and how to use them, please refer to the URL documentation
How to run in 'text-generation-webui'
-------------------------------------
Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.
How to run from Python code
---------------------------
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
#### Simple ctransformers example code
How to use with LangChain
-------------------------
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* LangChain + llama-cpp-python
* LangChain + ctransformers
Discord
-------
For further support, and discussions on these models and AI in general, join us at:
TheBloke AI's Discord server
Thanks, and how to contribute
-----------------------------
Thanks to the URL team!
Thanks to Clay from URL!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: URL
* Ko-Fi: URL
Special thanks to: Aemon Algiz.
Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
=========================================================================
Version
-------
How to use
----------
| [
"### About GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: ChatML\n-----------------------\n\n\nCompatibility\n-------------\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.\n\n\nExplanation of quantisation methods\n-----------------------------------\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n\n\n\nProvided files\n--------------\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote 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.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GGUF and below it, a specific filename to download, such as: mistral-7b-openorca-oasst\\_top1\\_2023-08-25-v1.Q4\\_K\\_M.gguf.\n\n\nThen click Download.",
"### On the command line, including multiple files at once\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\nMore advanced huggingface-cli download usage\nYou can also download multiple files at once with a pattern:\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nExample 'URL' command\n---------------------\n\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\n\nChange '-c 2048' 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 URL automatically.\n\n\nIf you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'\n\n\nFor other parameters and how to use them, please refer to the URL documentation\n\n\nHow to run in 'text-generation-webui'\n-------------------------------------\n\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.\n\n\nHow to run from Python code\n---------------------------\n\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.",
"### How to load this model in Python code, using ctransformers",
"#### First install the package\n\n\nRun one of the following commands, according to your system:",
"#### Simple ctransformers example code\n\n\nHow to use with LangChain\n-------------------------\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1\n=========================================================================\n\n\nVersion\n-------\n\n\nHow to use\n----------"
] | [
"TAGS\n#transformers #gguf #mistral #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #text-generation-inference #region-us \n",
"### About GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: ChatML\n-----------------------\n\n\nCompatibility\n-------------\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.\n\n\nExplanation of quantisation methods\n-----------------------------------\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n\n\n\nProvided files\n--------------\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote 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.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL",
"### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GGUF and below it, a specific filename to download, such as: mistral-7b-openorca-oasst\\_top1\\_2023-08-25-v1.Q4\\_K\\_M.gguf.\n\n\nThen click Download.",
"### On the command line, including multiple files at once\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\nMore advanced huggingface-cli download usage\nYou can also download multiple files at once with a pattern:\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nExample 'URL' command\n---------------------\n\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\n\nChange '-c 2048' 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 URL automatically.\n\n\nIf you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'\n\n\nFor other parameters and how to use them, please refer to the URL documentation\n\n\nHow to run in 'text-generation-webui'\n-------------------------------------\n\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.\n\n\nHow to run from Python code\n---------------------------\n\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.",
"### How to load this model in Python code, using ctransformers",
"#### First install the package\n\n\nRun one of the following commands, according to your system:",
"#### Simple ctransformers example code\n\n\nHow to use with LangChain\n-------------------------\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1\n=========================================================================\n\n\nVersion\n-------\n\n\nHow to use\n----------"
] | [
138,
963,
111,
443,
15,
19,
820
] | [
"passage: TAGS\n#transformers #gguf #mistral #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #text-generation-inference #region-us \n",
"passage: ### About GGUF\n\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: ChatML\n-----------------------\n\n\nCompatibility\n-------------\n\n\nThese quantised GGUFv2 files are compatible with URL from August 27th onwards, as of commit d0cee0d\n\n\nThey are also compatible with many third party UIs and libraries - please see the list at the top of this README.\n\n\nExplanation of quantisation methods\n-----------------------------------\n\n\n\nClick to see details\nThe new methods available are:\n\n\n* GGML\\_TYPE\\_Q2\\_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML\\_TYPE\\_Q3\\_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML\\_TYPE\\_Q4\\_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML\\_TYPE\\_Q5\\_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML\\_TYPE\\_Q4\\_K resulting in 5.5 bpw\n* GGML\\_TYPE\\_Q6\\_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw\n\n\nRefer to the Provided Files table below to see what files use which methods, and how.\n\n\n\nProvided files\n--------------\n\n\n\nNote: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.\n\n\nHow to download GGUF files\n--------------------------\n\n\nNote 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.\n\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n\n* LM Studio\n* LoLLMS Web UI\n* URL### In 'text-generation-webui'\n\n\nUnder Download Model, you can enter the model repo: TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GGUF and below it, a specific filename to download, such as: mistral-7b-openorca-oasst\\_top1\\_2023-08-25-v1.Q4\\_K\\_M.gguf.\n\n\nThen click Download.",
"passage: ### On the command line, including multiple files at once\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n\nMore advanced huggingface-cli download usage\nYou can also download multiple files at once with a pattern:\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.\n\n\n\nExample 'URL' command\n---------------------\n\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\n\nChange '-c 2048' 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 URL automatically.\n\n\nIf you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins'\n\n\nFor other parameters and how to use them, please refer to the URL documentation\n\n\nHow to run in 'text-generation-webui'\n-------------------------------------\n\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.\n\n\nHow to run from Python code\n---------------------------\n\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.### How to load this model in Python code, using ctransformers#### First install the package\n\n\nRun one of the following commands, according to your system:"
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null | null | transformers | <!-- markdownlint-disable MD041 -->
<!-- header start -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Mistral 7B OpenOrca oasst Top1 2023 08 25 v1 - AWQ
- Model creator: [Nicky](https://huggingface.co/NickyNicky)
- Original model: [Mistral 7B OpenOrca oasst Top1 2023 08 25 v1](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1)
<!-- description start -->
## Description
This repo contains AWQ model files for [Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF)
* [Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
```
reference-data-model:
datasets:
- OpenAssistant/oasst_top1_2023-08-25:
Lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
Link: https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25
model:
- Open-Orca/Mistral-7B-OpenOrca
Link: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
100 examples of generating:
Link: https://docs.google.com/spreadsheets/d/1_4rqFnhgvjA7trwAaEidaRWczAMzuKpw/edit?usp=sharing&ouid=116592149115238887304&rtpof=true&sd=true
Version 2:
Link: https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2
```
## Version
```py
import torch, transformers,torchvision
torch.__version__,transformers.__version__, torchvision.__version__
#OUTPUTS: ('2.0.1+cu118', '4.34.0.dev0', '0.15.2+cu118')
```
## How to use
```py
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
GenerationConfig,
TextIteratorStreamer,
)
import torch
# model_id = 'Open-Orca/Mistral-7B-OpenOrca'
model_id='NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1'
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
load_in_4bit=True,
low_cpu_mem_usage= True,
)
max_length=2048
print("max_length",max_length)
tokenizer = AutoTokenizer.from_pretrained(model_id,
# use_fast = False,
max_length=max_length,)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'right'
#EXAMPLE #1
txt="""<|im_start|>user
I'm looking for an efficient Python script to output prime numbers. Can you help me out? I'm interested in a script that can handle large numbers and output them quickly. Also, it would be great if the script could take a range of numbers as input and output all the prime numbers within that range. Can you generate a script that fits these requirements? Thanks!<|im_end|>
<|im_start|>assistant
"""
#EXAMPLE #2
txt="""<|im_start|>user
Estoy desarrollando una REST API con Nodejs, y estoy tratando de aplicar algún sistema de seguridad, ya sea con tokens o algo similar, me puedes ayudar?<|im_end|>
<|im_start|>assistant
"""
inputs = tokenizer.encode(txt, return_tensors="pt").to("cuda")
generation_config = GenerationConfig(
max_new_tokens=max_new_tokens,
temperature=0.7,
top_p=0.9,
top_k=len_tokens,
repetition_penalty=1.11,
do_sample=True,
# pad_token_id=tokenizer.eos_token_id,
# eos_token_id=tokenizer.eos_token_id,
# use_cache=True,
# stopping_criteria= StoppingCriteriaList([stopping_criteria]),
)
outputs = model.generate(generation_config=generation_config,
input_ids=inputs,)
tokenizer.decode(outputs[0], skip_special_tokens=False) #True
```
| {"language": ["bg", "ca", "cs", "da", "de", "en", "es", "fr", "hr", "hu", "it", "nl", "pl", "pt", "ro", "ru", "sl", "sr", "sv", "uk"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["Open-Orca/OpenOrca", "OpenAssistant/oasst_top1_2023-08-25"], "model_name": "Mistral 7B OpenOrca oasst Top1 2023 08 25 v1", "base_model": "NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1", "inference": false, "model_creator": "Nicky", "model_type": "mistral", "prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", "quantized_by": "TheBloke"} | text-generation | TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"bg",
"ca",
"cs",
"da",
"de",
"en",
"es",
"fr",
"hr",
"hu",
"it",
"nl",
"pl",
"pt",
"ro",
"ru",
"sl",
"sr",
"sv",
"uk",
"dataset:Open-Orca/OpenOrca",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"base_model:NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-12T00:21:39+00:00 | [] | [
"bg",
"ca",
"cs",
"da",
"de",
"en",
"es",
"fr",
"hr",
"hu",
"it",
"nl",
"pl",
"pt",
"ro",
"ru",
"sl",
"sr",
"sv",
"uk"
] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us
|
[[TheBloke's LLM work is generously supported by a grant from [andreessen horowitz (a16z)](URL)](URL to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style=)](URL & support: TheBloke's Discord server</a></p>
</div>
<div style=)
---
Mistral 7B OpenOrca oasst Top1 2023 08 25 v1 - AWQ
==================================================
* Model creator: Nicky
* Original model: Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
Description
-----------
This repo contains AWQ model files for Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1.
These files were quantised using hardware kindly provided by Massed Compute.
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
* Text Generation Webui - using Loader: AutoAWQ
* vLLM - Llama and Mistral models only
* Hugging Face Text Generation Inference (TGI)
* Transformers version 4.35.0 and later, from any code or client that supports Transformers
* AutoAWQ - for use from Python code
Repositories available
----------------------
* AWQ model(s) for GPU inference.
* GPTQ models for GPU inference, with multiple quantisation parameter options.
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: ChatML
-----------------------
Provided files, and AWQ parameters
----------------------------------
I currently release 128g GEMM models only. The addition of group\_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
How to easily download and use this model in text-generation-webui
------------------------------------------------------------------
Please make sure you're using the latest version of text-generation-webui.
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-AWQ'.
3. Click Download.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to Model.
6. In the Model dropdown, choose the model you just downloaded: 'Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-AWQ'
7. Select Loader: AutoAWQ.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
10. Once you're ready, click the Text Generation tab and enter a prompt to get started!
Multi-user inference server: vLLM
---------------------------------
Documentation on installing and using vLLM can be found here.
* Please ensure you are using vLLM version 0.2 or later.
* When using vLLM as a server, pass the '--quantization awq' parameter.
For example:
* When using vLLM from Python code, again set 'quantization=awq'.
For example:
Multi-user inference server: Hugging Face Text Generation Inference (TGI)
-------------------------------------------------------------------------
Use TGI version 1.1.0 or later. The official Docker container is: 'URL
Example Docker parameters:
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
Inference from Python code using Transformers
---------------------------------------------
### Install the necessary packages
* Requires: Transformers 4.35.0 or later.
* Requires: AutoAWQ 0.1.6 or later.
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:
### Transformers example code (requires Transformers 4.35.0 and later)
Compatibility
-------------
The files provided are tested to work with:
* text-generation-webui using 'Loader: AutoAWQ'.
* vLLM version 0.2.0 and later.
* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.
* Transformers version 4.35.0 and later.
* AutoAWQ version 0.1.1 and later.
Discord
-------
For further support, and discussions on these models and AI in general, join us at:
TheBloke AI's Discord server
Thanks, and how to contribute
-----------------------------
Thanks to the URL team!
Thanks to Clay from URL!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: URL
* Ko-Fi: URL
Special thanks to: Aemon Algiz.
Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
=========================================================================
Version
-------
How to use
----------
| [
"### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - Llama and Mistral models only\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: ChatML\n-----------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------",
"### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:",
"### Transformers example code (requires Transformers 4.35.0 and later)\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with:\n\n\n* text-generation-webui using 'Loader: AutoAWQ'.\n* vLLM version 0.2.0 and later.\n* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.\n* Transformers version 4.35.0 and later.\n* AutoAWQ version 0.1.1 and later.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1\n=========================================================================\n\n\nVersion\n-------\n\n\nHow to use\n----------"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - Llama and Mistral models only\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: ChatML\n-----------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------",
"### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:",
"### Transformers example code (requires Transformers 4.35.0 and later)\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with:\n\n\n* text-generation-webui using 'Loader: AutoAWQ'.\n* vLLM version 0.2.0 and later.\n* Hugging Face Text Generation Inference (TGI) version 1.1.0 and later.\n* Transformers version 4.35.0 and later.\n* AutoAWQ version 0.1.1 and later.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1\n=========================================================================\n\n\nVersion\n-------\n\n\nHow to use\n----------"
] | [
160,
762,
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"passage: ### About AWQ\n\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\n\nIt is supported by:\n\n\n* Text Generation Webui - using Loader: AutoAWQ\n* vLLM - Llama and Mistral models only\n* Hugging Face Text Generation Inference (TGI)\n* Transformers version 4.35.0 and later, from any code or client that supports Transformers\n* AutoAWQ - for use from Python code\n\n\nRepositories available\n----------------------\n\n\n* AWQ model(s) for GPU inference.\n* GPTQ models for GPU inference, with multiple quantisation parameter options.\n* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference\n* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions\n\n\nPrompt template: ChatML\n-----------------------\n\n\nProvided files, and AWQ parameters\n----------------------------------\n\n\nI currently release 128g GEMM models only. The addition of group\\_size 32 models, and GEMV kernel models, is being actively considered.\n\n\nModels are released as sharded safetensors files.\n\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-AWQ'.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-AWQ'\n7. Select Loader: AutoAWQ.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n10. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nMulti-user inference server: vLLM\n---------------------------------\n\n\nDocumentation on installing and using vLLM can be found here.\n\n\n* Please ensure you are using vLLM version 0.2 or later.\n* When using vLLM as a server, pass the '--quantization awq' parameter.\n\n\nFor example:\n\n\n* When using vLLM from Python code, again set 'quantization=awq'.\n\n\nFor example:\n\n\nMulti-user inference server: Hugging Face Text Generation Inference (TGI)\n-------------------------------------------------------------------------\n\n\nUse TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nInference from Python code using Transformers\n---------------------------------------------### Install the necessary packages\n\n\n* Requires: Transformers 4.35.0 or later.\n* Requires: AutoAWQ 0.1.6 or later.\n\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n\nIf you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:"
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null | null | transformers | <!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Mistral 7B OpenOrca oasst Top1 2023 08 25 v1 - GPTQ
- Model creator: [Nicky](https://huggingface.co/NickyNicky)
- Original model: [Mistral 7B OpenOrca oasst Top1 2023 08 25 v1](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GGUF)
* [Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1) | 4096 | 4.30 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ`:
```shell
mkdir Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ
huggingface-cli download TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ --local-dir Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ
huggingface-cli download TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
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
mkdir Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ --local-dir Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ --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>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
```
reference-data-model:
datasets:
- OpenAssistant/oasst_top1_2023-08-25:
Lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
Link: https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25
model:
- Open-Orca/Mistral-7B-OpenOrca
Link: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca
100 examples of generating:
Link: https://docs.google.com/spreadsheets/d/1_4rqFnhgvjA7trwAaEidaRWczAMzuKpw/edit?usp=sharing&ouid=116592149115238887304&rtpof=true&sd=true
Version 2:
Link: https://huggingface.co/NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2
```
## Version
```py
import torch, transformers,torchvision
torch.__version__,transformers.__version__, torchvision.__version__
#OUTPUTS: ('2.0.1+cu118', '4.34.0.dev0', '0.15.2+cu118')
```
## How to use
```py
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
GenerationConfig,
TextIteratorStreamer,
)
import torch
# model_id = 'Open-Orca/Mistral-7B-OpenOrca'
model_id='NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1'
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
load_in_4bit=True,
low_cpu_mem_usage= True,
)
max_length=2048
print("max_length",max_length)
tokenizer = AutoTokenizer.from_pretrained(model_id,
# use_fast = False,
max_length=max_length,)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'right'
#EXAMPLE #1
txt="""<|im_start|>user
I'm looking for an efficient Python script to output prime numbers. Can you help me out? I'm interested in a script that can handle large numbers and output them quickly. Also, it would be great if the script could take a range of numbers as input and output all the prime numbers within that range. Can you generate a script that fits these requirements? Thanks!<|im_end|>
<|im_start|>assistant
"""
#EXAMPLE #2
txt="""<|im_start|>user
Estoy desarrollando una REST API con Nodejs, y estoy tratando de aplicar algún sistema de seguridad, ya sea con tokens o algo similar, me puedes ayudar?<|im_end|>
<|im_start|>assistant
"""
inputs = tokenizer.encode(txt, return_tensors="pt").to("cuda")
generation_config = GenerationConfig(
max_new_tokens=max_new_tokens,
temperature=0.7,
top_p=0.9,
top_k=len_tokens,
repetition_penalty=1.11,
do_sample=True,
# pad_token_id=tokenizer.eos_token_id,
# eos_token_id=tokenizer.eos_token_id,
# use_cache=True,
# stopping_criteria= StoppingCriteriaList([stopping_criteria]),
)
outputs = model.generate(generation_config=generation_config,
input_ids=inputs,)
tokenizer.decode(outputs[0], skip_special_tokens=False) #True
```
| {"language": ["bg", "ca", "cs", "da", "de", "en", "es", "fr", "hr", "hu", "it", "nl", "pl", "pt", "ro", "ru", "sl", "sr", "sv", "uk"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["Open-Orca/OpenOrca", "OpenAssistant/oasst_top1_2023-08-25"], "model_name": "Mistral 7B OpenOrca oasst Top1 2023 08 25 v1", "base_model": "NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1", "inference": false, "model_creator": "Nicky", "model_type": "mistral", "prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", "quantized_by": "TheBloke"} | text-generation | TheBloke/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1-GPTQ | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"bg",
"ca",
"cs",
"da",
"de",
"en",
"es",
"fr",
"hr",
"hu",
"it",
"nl",
"pl",
"pt",
"ro",
"ru",
"sl",
"sr",
"sv",
"uk",
"dataset:Open-Orca/OpenOrca",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"base_model:NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-12T00:21:39+00:00 | [] | [
"bg",
"ca",
"cs",
"da",
"de",
"en",
"es",
"fr",
"hr",
"hu",
"it",
"nl",
"pl",
"pt",
"ro",
"ru",
"sl",
"sr",
"sv",
"uk"
] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us
|
[[TheBloke's LLM work is generously supported by a grant from [andreessen horowitz (a16z)](URL)](URL to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style=)](URL & support: TheBloke's Discord server</a></p>
</div>
<div style=)
---
Mistral 7B OpenOrca oasst Top1 2023 08 25 v1 - GPTQ
===================================================
* Model creator: Nicky
* Original model: Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
Description
-----------
This repo contains GPTQ model files for Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1.
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by Massed Compute.
Repositories available
----------------------
* AWQ model(s) for GPU inference.
* GPTQ models for GPU inference, with multiple quantisation parameter options.
* 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
* Nicky's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: ChatML
-----------------------
Known compatible clients / servers
----------------------------------
These GPTQ models are known to work in the following inference servers/webuis.
* text-generation-webui
* KoboldAI United
* LoLLMS Web UI
* Hugging Face Text Generation Inference (TGI)
This may not be a complete list; if you know of others, please let me know!
Provided files, and GPTQ parameters
-----------------------------------
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
Explanation of GPTQ parameters
* Bits: The bit size of the quantised model.
* GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
* Act Order: True or False. Also known as 'desc\_act'. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
* Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
* GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
* Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
* ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
How to download, including from branches
----------------------------------------
### In text-generation-webui
To download from the 'main' branch, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-GPTQ' in the "Download model" box.
To download from another branch, add ':branchname' to the end of the download name, eg 'TheBloke/Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder\_True'
### From the command line
I recommend using the 'huggingface-hub' Python library:
To download the 'main' branch to a folder called 'Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-GPTQ':
To download from a different branch, add the '--revision' parameter:
More advanced huggingface-cli download usage
If you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the 'HF\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.
For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\_transfer':
And set environment variable 'HF\_HUB\_ENABLE\_HF\_TRANSFER' to '1':
Windows Command Line users: You can set the environment variable by running 'set HF\_HUB\_ENABLE\_HF\_TRANSFER=1' before the download command.
### With 'git' (not recommended)
To clone a specific branch with 'git', use a command like this:
Note that using Git with HF repos is strongly discouraged. It will be much slower than using 'huggingface-hub', and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the '.git' folder as a blob.)
How to easily download and use this model in text-generation-webui
------------------------------------------------------------------
Please make sure you're using the latest version of text-generation-webui.
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the Model tab.
2. Under Download custom model or LoRA, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-GPTQ'.
* To download from a specific branch, enter for example 'TheBloke/Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder\_True'
* see Provided Files above for the list of branches for each option.
3. Click Download.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to Model.
6. In the Model dropdown, choose the model you just downloaded: 'Mistral-7B-OpenOrca-oasst\_top1\_2023-08-25-v1-GPTQ'
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file 'quantize\_config.json'.
9. Once you're ready, click the Text Generation tab and enter a prompt to get started!
Serving this model from Text Generation Inference (TGI)
-------------------------------------------------------
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: 'URL
Example Docker parameters:
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
How to use this GPTQ model from Python code
-------------------------------------------
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
### You can then use the following code
Compatibility
-------------
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
ExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
Discord
-------
For further support, and discussions on these models and AI in general, join us at:
TheBloke AI's Discord server
Thanks, and how to contribute
-----------------------------
Thanks to the URL team!
Thanks to Clay from URL!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: URL
* Ko-Fi: URL
Special thanks to: Aemon Algiz.
Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1
=========================================================================
Version
-------
How to use
----------
| [
"### In text-generation-webui\n\n\nTo download from the 'main' branch, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ' in the \"Download model\" box.\n\n\nTo download from another branch, add ':branchname' to the end of the download name, eg 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder\\_True'",
"### From the command line\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nTo download the 'main' branch to a folder called 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ':\n\n\nTo download from a different branch, add the '--revision' parameter:\n\n\n\nMore advanced huggingface-cli download usage\nIf you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\n\nThe cache location can be changed with the 'HF\\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.",
"### With 'git' (not recommended)\n\n\nTo clone a specific branch with 'git', use a command like this:\n\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using 'huggingface-hub', and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the '.git' folder as a blob.)\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ'.\n\n\n\t* To download from a specific branch, enter for example 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder\\_True'\n\t* see Provided Files above for the list of branches for each option.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ'\n7. The model will automatically load, and is now ready for use!\n8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n\n\n\t* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file 'quantize\\_config.json'.\n9. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nServing this model from Text Generation Inference (TGI)\n-------------------------------------------------------\n\n\nIt's recommended to use TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nHow to use this GPTQ model from Python code\n-------------------------------------------",
"### Install the necessary packages\n\n\nRequires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n\nIf you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:",
"### You can then use the following code\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.\n\n\nExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.\n\n\nFor a list of clients/servers, please see \"Known compatible clients / servers\", above.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1\n=========================================================================\n\n\nVersion\n-------\n\n\nHow to use\n----------"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us \n",
"### In text-generation-webui\n\n\nTo download from the 'main' branch, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ' in the \"Download model\" box.\n\n\nTo download from another branch, add ':branchname' to the end of the download name, eg 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder\\_True'",
"### From the command line\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nTo download the 'main' branch to a folder called 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ':\n\n\nTo download from a different branch, add the '--revision' parameter:\n\n\n\nMore advanced huggingface-cli download usage\nIf you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\n\nThe cache location can be changed with the 'HF\\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.",
"### With 'git' (not recommended)\n\n\nTo clone a specific branch with 'git', use a command like this:\n\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using 'huggingface-hub', and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the '.git' folder as a blob.)\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ'.\n\n\n\t* To download from a specific branch, enter for example 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder\\_True'\n\t* see Provided Files above for the list of branches for each option.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ'\n7. The model will automatically load, and is now ready for use!\n8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n\n\n\t* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file 'quantize\\_config.json'.\n9. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nServing this model from Text Generation Inference (TGI)\n-------------------------------------------------------\n\n\nIt's recommended to use TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nHow to use this GPTQ model from Python code\n-------------------------------------------",
"### Install the necessary packages\n\n\nRequires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n\nIf you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:",
"### You can then use the following code\n\n\nCompatibility\n-------------\n\n\nThe files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.\n\n\nExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.\n\n\nFor a list of clients/servers, please see \"Known compatible clients / servers\", above.\n\n\nDiscord\n-------\n\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n\nTheBloke AI's Discord server\n\n\nThanks, and how to contribute\n-----------------------------\n\n\nThanks to the URL team!\n\n\nThanks to Clay from URL!\n\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n\n* Patreon: URL\n* Ko-Fi: URL\n\n\nSpecial thanks to: Aemon Algiz.\n\n\nPatreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S\\_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, URL, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\n\nAnd thank you again to a16z for their generous grant.\n\n\nOriginal model card: Nicky's Mistral 7B OpenOrca oasst Top1 2023 08 25 v1\n=========================================================================\n\n\nVersion\n-------\n\n\nHow to use\n----------"
] | [
160,
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #bg #ca #cs #da #de #en #es #fr #hr #hu #it #nl #pl #pt #ro #ru #sl #sr #sv #uk #dataset-Open-Orca/OpenOrca #dataset-OpenAssistant/oasst_top1_2023-08-25 #base_model-NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #4-bit #region-us \n### In text-generation-webui\n\n\nTo download from the 'main' branch, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ' in the \"Download model\" box.\n\n\nTo download from another branch, add ':branchname' to the end of the download name, eg 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder\\_True'",
"passage: ### From the command line\n\n\nI recommend using the 'huggingface-hub' Python library:\n\n\nTo download the 'main' branch to a folder called 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ':\n\n\nTo download from a different branch, add the '--revision' parameter:\n\n\n\nMore advanced huggingface-cli download usage\nIf you remove the '--local-dir-use-symlinks False' parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: '~/.cache/huggingface'), and symlinks will be added to the specified '--local-dir', pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.\n\n\nThe cache location can be changed with the 'HF\\_HOME' environment variable, and/or the '--cache-dir' parameter to 'huggingface-cli'.\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\\_transfer':\n\n\nAnd set environment variable 'HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER' to '1':\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF\\_HUB\\_ENABLE\\_HF\\_TRANSFER=1' before the download command.",
"passage: ### With 'git' (not recommended)\n\n\nTo clone a specific branch with 'git', use a command like this:\n\n\nNote that using Git with HF repos is strongly discouraged. It will be much slower than using 'huggingface-hub', and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the '.git' folder as a blob.)\n\n\nHow to easily download and use this model in text-generation-webui\n------------------------------------------------------------------\n\n\nPlease make sure you're using the latest version of text-generation-webui.\n\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n\n1. Click the Model tab.\n2. Under Download custom model or LoRA, enter 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ'.\n\n\n\t* To download from a specific branch, enter for example 'TheBloke/Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ:gptq-4bit-32g-actorder\\_True'\n\t* see Provided Files above for the list of branches for each option.\n3. Click Download.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to Model.\n6. In the Model dropdown, choose the model you just downloaded: 'Mistral-7B-OpenOrca-oasst\\_top1\\_2023-08-25-v1-GPTQ'\n7. The model will automatically load, and is now ready for use!\n8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.\n\n\n\t* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file 'quantize\\_config.json'.\n9. Once you're ready, click the Text Generation tab and enter a prompt to get started!\n\n\nServing this model from Text Generation Inference (TGI)\n-------------------------------------------------------\n\n\nIt's recommended to use TGI version 1.1.0 or later. The official Docker container is: 'URL\n\n\nExample Docker parameters:\n\n\nExample Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):\n\n\nHow to use this GPTQ model from Python code\n-------------------------------------------### Install the necessary packages\n\n\nRequires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.\n\n\nIf you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:"
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null | null | diffusers |
# LoRA text2image fine-tuning - prushton/logo-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the myradeng/random100logos dataset. You can find some example images in the following.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true} | text-to-image | prushton/logo-lora | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T00:27:53+00:00 | [] | [] | TAGS
#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us
|
# LoRA text2image fine-tuning - prushton/logo-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the myradeng/random100logos dataset. You can find some example images in the following.
!img_0
| [
"# LoRA text2image fine-tuning - prushton/logo-lora\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the myradeng/random100logos dataset. You can find some example images in the following. \n\n!img_0"
] | [
"TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n",
"# LoRA text2image fine-tuning - prushton/logo-lora\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the myradeng/random100logos dataset. You can find some example images in the following. \n\n!img_0"
] | [
68,
80
] | [
"passage: TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - prushton/logo-lora\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the myradeng/random100logos dataset. You can find some example images in the following. \n\n!img_0"
] | [
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null | 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. -->
# bert-financial-sentiment-analysis
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on ic-fspml/fpb.
It achieves the following results on the evaluation set:
- Loss: 0.7950
- Accuracy: 0.8474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5398 | 0.41 | 200 | 0.5137 | 0.8072 |
| 0.4619 | 0.82 | 400 | 0.4086 | 0.8320 |
| 0.2698 | 1.24 | 600 | 0.6088 | 0.8268 |
| 0.2799 | 1.65 | 800 | 0.6722 | 0.8340 |
| 0.2429 | 2.06 | 1000 | 0.7032 | 0.8392 |
| 0.0831 | 2.47 | 1200 | 0.7950 | 0.8474 |
| 0.144 | 2.89 | 1400 | 0.8083 | 0.8423 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-financial-sentiment-analysis", "results": []}]} | text-classification | carloszansavio/bert-financial-sentiment-analysis | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T00:38:38+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-financial-sentiment-analysis
=================================
This model is a fine-tuned version of bert-base-uncased on ic-fspml/fpb.
It achieves the following results on the evaluation set:
* Loss: 0.7950
* Accuracy: 0.8474
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
64,
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"passage: TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | transformers | library_name: transformers
---
# ibleducation/ibl-tutoring-chat-7B
ibleducation/ibl-tutoring-chat-7B is a model finetuned on top of openchat/openchat_3.5
This model is finetuned to give responses in a way befitting of a professional teacher.
It is finetuned to exhibit characteristics and virtues such as compassion, encouragement, friendliness and more.
## Example Conversations
1. Question: I'm having trouble understanding this art movement. Can you help me? \
Answer:
```code
Yes I can assist you, that is what I am here for. We can start by analyzing the artists' techniques, their motivations, and find creative ways to connect with the movement. Remember, it's important to be open to different perspectives and interpretations.
```
2. Question: I am thinking of stopping school do you think it is right? \
Answer:
```code
I understand that school can be challenging, but I believe that education is important. I'm here to for you. Where you have troubles, I will be happy to help
```
## Model Details
- **Developed by:** [IBL Education](https://ibl.ai)
- **Model type:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Base Model:** [OpenChat 3.5](https://huggingface.co/openchat/openchat_3.5)
- **Language:** English
- **Finetuned from weights:** [OpenChat 3.5](https://huggingface.co/openchat/openchat_3.5)
- **Finetuned on data:**
- [ibleducation/ibl-best-practices-instructor-dataset](https://huggingface.co/datasets/ibleducation/ibl-best-practices-instructor-dataset)
- **Model License:** Apache 2.0
## How to Use ibl-tutoring-chat-7B Model from Python Code (HuggingFace transformers) ##
### Install the necessary packages
Requires: [transformers](https://pypi.org/project/transformers/) 4.35.0 or later, and [accelerate](https://pypi.org/project/accelerate/) 0.23.0 or later.
```shell
pip install transformers==4.35.0
pip install accelerate==0.23.0
```
### You can then try the following example code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch
model_id = "ibleducation/ibl-tutoring-chat-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
prompt = "<s>What makes a good teacher?</s>"
response = pipeline(prompt)
print(response['generated_text'])
```
**Important** - Use the prompt template below for ibl-tutoring-chat-7B:
```
<s>{prompt}</s>
``` | {"datasets": ["ibleducation/ibl-best-practices-instructor-dataset"], "metrics": ["rouge", "bleu", "bleurt"], "model-index": [{"name": "ibleducation/ibl-tutoring-chat-7B", "results": [{"task": {"type": "text-generation", "name": "truthfulqa_gen"}, "dataset": {"name": "Truthful QA", "type": "truthful_qa"}, "metrics": [{"type": "bleurt", "value": -0.5572, "name": "bleurt_max"}, {"type": "bleurt", "value": 0.4321, "name": "bleurt_acc"}, {"type": "bleurt", "value": -0.0725, "name": "bleurt_diff"}, {"type": "bleu", "value": 22.5935, "name": "bleu_max"}, {"type": "bleu", "value": 0.3758, "name": "bleu_acc"}, {"type": "bleu", "value": -2.5541, "name": "bleu_diff"}, {"type": "rouge", "value": 50.0851, "name": "rouge1_max"}, {"type": "rouge", "value": 0.3978, "name": "rouge1_acc"}, {"type": "rouge", "value": -3.5142, "name": "rouge1_diff"}, {"type": "rouge", "value": 34.7473, "name": "rouge2_max"}, {"type": "rouge", "value": 0.339, "name": "rouge2_acc"}, {"type": "rouge", "value": -4.5082, "name": "rouge2_diff"}, {"type": "rouge", "value": 46.1054, "name": "rougeL_max"}, {"type": "rouge", "value": 0.3745, "name": "rougeL_acc"}, {"type": "rouge", "value": -4.0046, "name": "rougeL_diff"}]}]}]} | text-generation | iblai/ibl-tutoring-chat-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"dataset:ibleducation/ibl-best-practices-instructor-dataset",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T00:39:46+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #dataset-ibleducation/ibl-best-practices-instructor-dataset #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| library_name: transformers
---
# ibleducation/ibl-tutoring-chat-7B
ibleducation/ibl-tutoring-chat-7B is a model finetuned on top of openchat/openchat_3.5
This model is finetuned to give responses in a way befitting of a professional teacher.
It is finetuned to exhibit characteristics and virtues such as compassion, encouragement, friendliness and more.
## Example Conversations
1. Question: I'm having trouble understanding this art movement. Can you help me? \
Answer:
2. Question: I am thinking of stopping school do you think it is right? \
Answer:
## Model Details
- Developed by: IBL Education
- Model type: Mistral-7B-v0.1
- Base Model: OpenChat 3.5
- Language: English
- Finetuned from weights: OpenChat 3.5
- Finetuned on data:
- ibleducation/ibl-best-practices-instructor-dataset
- Model License: Apache 2.0
## How to Use ibl-tutoring-chat-7B Model from Python Code (HuggingFace transformers) ##
### Install the necessary packages
Requires: transformers 4.35.0 or later, and accelerate 0.23.0 or later.
### You can then try the following example code
Important - Use the prompt template below for ibl-tutoring-chat-7B:
| [
"# ibleducation/ibl-tutoring-chat-7B\nibleducation/ibl-tutoring-chat-7B is a model finetuned on top of openchat/openchat_3.5\n\nThis model is finetuned to give responses in a way befitting of a professional teacher. \nIt is finetuned to exhibit characteristics and virtues such as compassion, encouragement, friendliness and more.",
"## Example Conversations\n1. Question: I'm having trouble understanding this art movement. Can you help me? \\\n Answer:\n \n \n2. Question: I am thinking of stopping school do you think it is right? \\\n Answer:",
"## Model Details\n\n- Developed by: IBL Education\n- Model type: Mistral-7B-v0.1\n- Base Model: OpenChat 3.5\n- Language: English\n- Finetuned from weights: OpenChat 3.5\n- Finetuned on data:\n - ibleducation/ibl-best-practices-instructor-dataset\n- Model License: Apache 2.0",
"## How to Use ibl-tutoring-chat-7B Model from Python Code (HuggingFace transformers) ##",
"### Install the necessary packages\n\nRequires: transformers 4.35.0 or later, and accelerate 0.23.0 or later.",
"### You can then try the following example code\n\n\nImportant - Use the prompt template below for ibl-tutoring-chat-7B:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #dataset-ibleducation/ibl-best-practices-instructor-dataset #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ibleducation/ibl-tutoring-chat-7B\nibleducation/ibl-tutoring-chat-7B is a model finetuned on top of openchat/openchat_3.5\n\nThis model is finetuned to give responses in a way befitting of a professional teacher. \nIt is finetuned to exhibit characteristics and virtues such as compassion, encouragement, friendliness and more.",
"## Example Conversations\n1. Question: I'm having trouble understanding this art movement. Can you help me? \\\n Answer:\n \n \n2. Question: I am thinking of stopping school do you think it is right? \\\n Answer:",
"## Model Details\n\n- Developed by: IBL Education\n- Model type: Mistral-7B-v0.1\n- Base Model: OpenChat 3.5\n- Language: English\n- Finetuned from weights: OpenChat 3.5\n- Finetuned on data:\n - ibleducation/ibl-best-practices-instructor-dataset\n- Model License: Apache 2.0",
"## How to Use ibl-tutoring-chat-7B Model from Python Code (HuggingFace transformers) ##",
"### Install the necessary packages\n\nRequires: transformers 4.35.0 or later, and accelerate 0.23.0 or later.",
"### You can then try the following example code\n\n\nImportant - Use the prompt template below for ibl-tutoring-chat-7B:"
] | [
74,
96,
49,
79,
27,
26,
29
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #dataset-ibleducation/ibl-best-practices-instructor-dataset #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ibleducation/ibl-tutoring-chat-7B\nibleducation/ibl-tutoring-chat-7B is a model finetuned on top of openchat/openchat_3.5\n\nThis model is finetuned to give responses in a way befitting of a professional teacher. \nIt is finetuned to exhibit characteristics and virtues such as compassion, encouragement, friendliness and more.## Example Conversations\n1. Question: I'm having trouble understanding this art movement. Can you help me? \\\n Answer:\n \n \n2. Question: I am thinking of stopping school do you think it is right? \\\n Answer:## Model Details\n\n- Developed by: IBL Education\n- Model type: Mistral-7B-v0.1\n- Base Model: OpenChat 3.5\n- Language: English\n- Finetuned from weights: OpenChat 3.5\n- Finetuned on data:\n - ibleducation/ibl-best-practices-instructor-dataset\n- Model License: Apache 2.0## How to Use ibl-tutoring-chat-7B Model from Python Code (HuggingFace transformers) ##### Install the necessary packages\n\nRequires: transformers 4.35.0 or later, and accelerate 0.23.0 or later.### You can then try the following example code\n\n\nImportant - Use the prompt template below for ibl-tutoring-chat-7B:"
] | [
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null | 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. -->
# MT-zany-pond-109
This model is a fine-tuned version of [toobiza/MT-smart-feather-100](https://huggingface.co/toobiza/MT-smart-feather-100) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2338
- eval_loss_ce: 0.0008
- eval_loss_bbox: 0.0278
- eval_cardinality_error: 1.0
- eval_giou: 95.3678
- eval_runtime: 49.5262
- eval_samples_per_second: 2.241
- eval_steps_per_second: 0.565
- epoch: 0.09
- step: 40
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "base_model": "toobiza/MT-smart-feather-100", "model-index": [{"name": "MT-zany-pond-109", "results": []}]} | object-detection | AmineAllo/MT-zany-pond-109 | [
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"base_model:toobiza/MT-smart-feather-100",
"endpoints_compatible",
"region:us"
] | 2023-11-12T00:47:55+00:00 | [] | [] | TAGS
#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us
|
# MT-zany-pond-109
This model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2338
- eval_loss_ce: 0.0008
- eval_loss_bbox: 0.0278
- eval_cardinality_error: 1.0
- eval_giou: 95.3678
- eval_runtime: 49.5262
- eval_samples_per_second: 2.241
- eval_steps_per_second: 0.565
- epoch: 0.09
- step: 40
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| [
"# MT-zany-pond-109\n\nThis model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2338\n- eval_loss_ce: 0.0008\n- eval_loss_bbox: 0.0278\n- eval_cardinality_error: 1.0\n- eval_giou: 95.3678\n- eval_runtime: 49.5262\n- eval_samples_per_second: 2.241\n- eval_steps_per_second: 0.565\n- epoch: 0.09\n- step: 40",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.33.2\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us \n",
"# MT-zany-pond-109\n\nThis model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2338\n- eval_loss_ce: 0.0008\n- eval_loss_bbox: 0.0278\n- eval_cardinality_error: 1.0\n- eval_giou: 95.3678\n- eval_runtime: 49.5262\n- eval_samples_per_second: 2.241\n- eval_steps_per_second: 0.565\n- epoch: 0.09\n- step: 40",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.33.2\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.13.3"
] | [
54,
152,
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] | [
"passage: TAGS\n#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us \n# MT-zany-pond-109\n\nThis model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.2338\n- eval_loss_ce: 0.0008\n- eval_loss_bbox: 0.0278\n- eval_cardinality_error: 1.0\n- eval_giou: 95.3678\n- eval_runtime: 49.5262\n- eval_samples_per_second: 2.241\n- eval_steps_per_second: 0.565\n- epoch: 0.09\n- step: 40## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Framework versions\n\n- Transformers 4.33.2\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.13.3"
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null | 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. -->
# MT-bumbling-jazz-110
This model is a fine-tuned version of [toobiza/MT-smart-feather-100](https://huggingface.co/toobiza/MT-smart-feather-100) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3325
- Loss Ce: 0.0008
- Loss Bbox: 0.0411
- Cardinality Error: 1.0
- Giou: 93.8060
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- 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 | Loss Ce | Loss Bbox | Cardinality Error | Giou |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:-----------------:|:-------:|
| 3.0324 | 0.21 | 100 | 0.2384 | 0.0006 | 0.0280 | 1.0 | 95.1515 |
| 3.0821 | 0.43 | 200 | 0.2353 | 0.0005 | 0.0270 | 1.0 | 95.0779 |
| 3.1303 | 0.64 | 300 | 0.2509 | 0.0005 | 0.0297 | 1.0 | 94.9938 |
| 3.1438 | 0.85 | 400 | 0.2649 | 0.0004 | 0.0316 | 1.0 | 94.7667 |
| 3.0505 | 1.07 | 500 | 0.3075 | 0.0007 | 0.0368 | 1.0 | 93.9513 |
| 3.3453 | 1.28 | 600 | 0.3260 | 0.0007 | 0.0401 | 1.0 | 93.8608 |
| 2.9246 | 1.49 | 700 | 0.2985 | 0.0009 | 0.0357 | 1.0 | 94.1213 |
| 2.8508 | 1.71 | 800 | 0.2933 | 0.0008 | 0.0349 | 1.0 | 94.1778 |
| 2.9657 | 1.92 | 900 | 0.3315 | 0.0009 | 0.0410 | 1.0 | 93.8321 |
| 3.1487 | 2.13 | 1000 | 0.3340 | 0.0008 | 0.0411 | 1.0 | 93.7168 |
| 3.1254 | 2.35 | 1100 | 0.3098 | 0.0008 | 0.0379 | 1.0 | 94.1191 |
| 2.4966 | 2.56 | 1200 | 0.3171 | 0.0008 | 0.0384 | 1.0 | 93.8997 |
| 2.8596 | 2.77 | 1300 | 0.3294 | 0.0008 | 0.0404 | 1.0 | 93.7750 |
| 3.2516 | 2.99 | 1400 | 0.3325 | 0.0008 | 0.0411 | 1.0 | 93.8060 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "base_model": "toobiza/MT-smart-feather-100", "model-index": [{"name": "MT-bumbling-jazz-110", "results": []}]} | object-detection | AmineAllo/MT-bumbling-jazz-110 | [
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"base_model:toobiza/MT-smart-feather-100",
"endpoints_compatible",
"region:us"
] | 2023-11-12T00:53:28+00:00 | [] | [] | TAGS
#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us
| MT-bumbling-jazz-110
====================
This model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3325
* Loss Ce: 0.0008
* Loss Bbox: 0.0411
* Cardinality Error: 1.0
* Giou: 93.8060
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.33.2
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
] | [
54,
98,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.33.2\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.13.3"
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null | 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. -->
# NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2557
- Precision: 0.4943
- Recall: 0.5046
- F1: 0.4994
- Accuracy: 0.9407
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 71 | 0.2423 | 0.1951 | 0.1433 | 0.1653 | 0.9109 |
| No log | 2.0 | 142 | 0.2177 | 0.2905 | 0.3474 | 0.3164 | 0.9138 |
| No log | 3.0 | 213 | 0.1822 | 0.3912 | 0.3701 | 0.3804 | 0.9325 |
| No log | 4.0 | 284 | 0.1845 | 0.3839 | 0.4367 | 0.4086 | 0.9298 |
| No log | 5.0 | 355 | 0.2033 | 0.4533 | 0.4271 | 0.4398 | 0.9367 |
| No log | 6.0 | 426 | 0.2005 | 0.4535 | 0.4736 | 0.4633 | 0.9365 |
| No log | 7.0 | 497 | 0.2297 | 0.4352 | 0.5155 | 0.4720 | 0.9321 |
| 0.1436 | 8.0 | 568 | 0.2236 | 0.4854 | 0.4656 | 0.4753 | 0.9395 |
| 0.1436 | 9.0 | 639 | 0.2335 | 0.4935 | 0.5101 | 0.5016 | 0.9397 |
| 0.1436 | 10.0 | 710 | 0.2413 | 0.4829 | 0.5075 | 0.4949 | 0.9405 |
| 0.1436 | 11.0 | 781 | 0.2557 | 0.4849 | 0.5239 | 0.5036 | 0.9383 |
| 0.1436 | 12.0 | 852 | 0.2557 | 0.4943 | 0.5046 | 0.4994 | 0.9407 |
### Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "emilyalsentzer/Bio_ClinicalBERT", "model-index": [{"name": "NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model", "results": []}]} | token-classification | GuCuChiara/NLP-HIBA_DisTEMIST_fine_tuned_ClinicalBERT-pretrained-model | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:emilyalsentzer/Bio_ClinicalBERT",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T00:57:14+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-emilyalsentzer/Bio_ClinicalBERT #license-mit #autotrain_compatible #endpoints_compatible #region-us
| NLP-HIBA\_DisTEMIST\_fine\_tuned\_ClinicalBERT-pretrained-model
===============================================================
This model is a fine-tuned version of emilyalsentzer/Bio\_ClinicalBERT on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2557
* Precision: 0.4943
* Recall: 0.5046
* F1: 0.4994
* Accuracy: 0.9407
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 12
### Training results
### Framework versions
* Transformers 4.35.1
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.1\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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null | 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. -->
# speecht5_finetuned_voxpopuli_sk
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4320
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4844 | 27.03 | 1000 | 0.4425 |
| 0.4567 | 54.05 | 2000 | 0.4351 |
| 0.4533 | 81.08 | 3000 | 0.4340 |
| 0.4511 | 108.11 | 4000 | 0.4320 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["voxpopuli"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "speecht5_finetuned_voxpopuli_sk", "results": []}]} | text-to-audio | joshmazen/speecht5_finetuned_voxpopuli_sk | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-12T01:12:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
| speecht5\_finetuned\_voxpopuli\_sk
==================================
This model is a fine-tuned version of microsoft/speecht5\_tts on the voxpopuli dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4320
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
72,
158,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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null | null | transformers | 33% pruning on RedPajama 3B linear layers
The pruned layers are:
1. attention linear layers (query, key, value computation)
2. attention dense layer
3. MLP layers
Pruning is done in all decoder modules. Pruning is unstructured magnitude pruning | {} | text-generation | Advaith28/Linear_pruned_RedPajama3B | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | 2023-11-12T01:16:20+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us
| 33% pruning on RedPajama 3B linear layers
The pruned layers are:
1. attention linear layers (query, key, value computation)
2. attention dense layer
3. MLP layers
Pruning is done in all decoder modules. Pruning is unstructured magnitude pruning | [] | [
"TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n"
] | [
54
] | [
"passage: TAGS\n#transformers #safetensors #gpt_neox #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us \n"
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] |
null | null | transformers |
internlm-chat-7b-v1_1をGPTQ変換したモデルです<br>
利用に当たってはhttps://huggingface.co/internlm/internlm-chat-7b-v1_1 のライセンスに従って下さい<br>
<br>
推論用コード<br>
```
import torch
import time
from transformers import AutoTokenizer, AutoModelForCausalLM,GPTQConfig
model_path = r".\internlm-chat-7b-v1_1-gptq"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
gptq_config = GPTQConfig(bits= 4 , disable_exllama= True )
model = AutoModelForCausalLM.from_pretrained( model_path , device_map= "auto" , quantization_config = gptq_config,trust_remote_code=True)
model = model.eval()
history = []
while True:
txt = input("msg:")
start_time = time.perf_counter()
response, history = model.chat(tokenizer, txt, history=history)
print(response)
end_time = time.perf_counter()
elapsed_time = end_time - start_time
print(f"worktime:{elapsed_time}")
```
| {"license": "other", "license_name": "internlm-license", "license_link": "https://huggingface.co/internlm/internlm-chat-7b-v1_1"} | feature-extraction | wizcat/internlm-chat-7b-v1_1-gptq | [
"transformers",
"safetensors",
"internlm",
"feature-extraction",
"custom_code",
"license:other",
"4-bit",
"region:us"
] | 2023-11-12T01:16:22+00:00 | [] | [] | TAGS
#transformers #safetensors #internlm #feature-extraction #custom_code #license-other #4-bit #region-us
|
internlm-chat-7b-v1_1をGPTQ変換したモデルです<br>
利用に当たってはhttps://URL のライセンスに従って下さい<br>
<br>
推論用コード<br>
| [] | [
"TAGS\n#transformers #safetensors #internlm #feature-extraction #custom_code #license-other #4-bit #region-us \n"
] | [
37
] | [
"passage: TAGS\n#transformers #safetensors #internlm #feature-extraction #custom_code #license-other #4-bit #region-us \n"
] | [
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null | 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. -->
# xlnet_finetuned
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6564
- Accuracy: 0.8098
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 310 | 0.7622 | 0.7797 |
| 1.0144 | 2.0 | 620 | 0.6564 | 0.8098 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.11.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "xlnet-base-cased", "model-index": [{"name": "xlnet_finetuned", "results": []}]} | text-classification | Alec42/xlnet_finetuned | [
"transformers",
"pytorch",
"xlnet",
"text-classification",
"generated_from_trainer",
"base_model:xlnet-base-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T01:17:50+00:00 | [] | [] | TAGS
#transformers #pytorch #xlnet #text-classification #generated_from_trainer #base_model-xlnet-base-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlnet\_finetuned
================
This model is a fine-tuned version of xlnet-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6564
* Accuracy: 0.8098
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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.33.3
* Pytorch 2.1.0
* Datasets 2.14.6
* Tokenizers 0.11.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.11.0"
] | [
"TAGS\n#transformers #pytorch #xlnet #text-classification #generated_from_trainer #base_model-xlnet-base-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.11.0"
] | [
61,
98,
4,
30
] | [
"passage: TAGS\n#transformers #pytorch #xlnet #text-classification #generated_from_trainer #base_model-xlnet-base-cased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.33.3\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.11.0"
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null | null | null | Some GGUF V3 quantizations of the model [vihangd/shearedplats-2.7b-v1](https://huggingface.co/vihangd/shearedplats-2.7b-v1)
<p><h1> ShearedPlats-2.7b-v1 </h1></p>
An experimental finetune of Sheared LLaMA 2.7b with Alpaca-QLoRA
<h2> Datasets </h2>
Trained on alpca style datasets
<p><h2> Prompt Template </h2></p>
Uses alpaca style prompt template | {"license": "llama2"} | null | Aryanne/Shearedplats-2.7B-v1-gguf | [
"gguf",
"license:llama2",
"region:us"
] | 2023-11-12T01:30:11+00:00 | [] | [] | TAGS
#gguf #license-llama2 #region-us
| Some GGUF V3 quantizations of the model vihangd/shearedplats-2.7b-v1
<p><h1> ShearedPlats-2.7b-v1 </h1></p>
An experimental finetune of Sheared LLaMA 2.7b with Alpaca-QLoRA
<h2> Datasets </h2>
Trained on alpca style datasets
<p><h2> Prompt Template </h2></p>
Uses alpaca style prompt template | [] | [
"TAGS\n#gguf #license-llama2 #region-us \n"
] | [
16
] | [
"passage: TAGS\n#gguf #license-llama2 #region-us \n"
] | [
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] |
null | null | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.15 +/- 0.08", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | MarkChen1214/a2c-PandaReachDense-v3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T01:34:57+00:00 | [] | [] | TAGS
#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing PandaReachDense-v3
This is a trained model of a A2C agent playing PandaReachDense-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
41,
45,
17
] | [
"passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | 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. -->
# longt5_xl_gov_bp_15
This model is a fine-tuned version of [/exports/eddie/scratch/s1970716/models/summarization/longt5_xl_gov_bp_10/checkpoint-680](https://huggingface.co//exports/eddie/scratch/s1970716/models/summarization/longt5_xl_gov_bp_10/checkpoint-680) on the learn3r/gov_report_bp dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7126
## 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: 0.001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2051 | 1.0 | 136 | 1.7126 |
| 0.1732 | 1.99 | 272 | 1.8857 |
| 0.1777 | 3.0 | 409 | 1.9036 |
| 0.1122 | 4.0 | 545 | 1.9538 |
| 0.1098 | 4.99 | 680 | 2.1134 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "datasets": ["learn3r/gov_report_bp"], "base_model": "/exports/eddie/scratch/s1970716/models/summarization/longt5_xl_gov_bp_10/checkpoint-680", "model-index": [{"name": "longt5_xl_gov_bp_15", "results": []}]} | text2text-generation | learn3r/longt5_xl_gov_bp_15 | [
"transformers",
"pytorch",
"longt5",
"text2text-generation",
"generated_from_trainer",
"dataset:learn3r/gov_report_bp",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T01:39:17+00:00 | [] | [] | TAGS
#transformers #pytorch #longt5 #text2text-generation #generated_from_trainer #dataset-learn3r/gov_report_bp #autotrain_compatible #endpoints_compatible #region-us
| longt5\_xl\_gov\_bp\_15
=======================
This model is a fine-tuned version of /exports/eddie/scratch/s1970716/models/summarization/longt5\_xl\_gov\_bp\_10/checkpoint-680 on the learn3r/gov\_report\_bp dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7126
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: 0.001
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 64
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: constant
* num\_epochs: 5.0
### Training results
### Framework versions
* Transformers 4.34.1
* Pytorch 2.1.0+cu121
* Datasets 2.14.5
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #pytorch #longt5 #text2text-generation #generated_from_trainer #dataset-learn3r/gov_report_bp #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1"
] | [
63,
125,
4,
33
] | [
"passage: TAGS\n#transformers #pytorch #longt5 #text2text-generation #generated_from_trainer #dataset-learn3r/gov_report_bp #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 64\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 5.0### Training results### Framework versions\n\n\n* Transformers 4.34.1\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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 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. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.1
| {"library_name": "peft", "base_model": "huggyllama/llama-7b"} | null | noble6/saiga_siberia1200_llama2_7b | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"region:us"
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-huggyllama/llama-7b #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.1
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null | 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. -->
# deberta-v3-base-survey-new_fact_main_passage-rater-half-human
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3083
- Krippendorff: 0.9575
- Spearman: 0.9507
- Absolute Agreement: 0.9195
- Agreement Within One: 0.9892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Krippendorff | Spearman | Absolute Agreement | Agreement Within One |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:--------:|:------------------:|:--------------------:|
| No log | 1.0 | 104 | 1.8890 | -0.1345 | nan | 0.3889 | 0.6667 |
| No log | 2.0 | 208 | 1.8493 | -0.5370 | nan | 0.2222 | 1.0 |
| No log | 3.0 | 312 | 2.0969 | -0.5370 | nan | 0.2222 | 1.0 |
| No log | 4.0 | 416 | 2.3136 | -0.5370 | nan | 0.2222 | 1.0 |
| 1.5524 | 5.0 | 520 | 2.2296 | -0.4087 | 0.2456 | 0.25 | 1.0 |
| 1.5524 | 6.0 | 624 | 2.3653 | -0.2226 | -0.1941 | 0.1667 | 0.6667 |
| 1.5524 | 7.0 | 728 | 2.3394 | -0.1613 | -0.0448 | 0.2222 | 0.6111 |
| 1.5524 | 8.0 | 832 | 2.1587 | -0.0351 | -0.0171 | 0.2222 | 0.7222 |
| 1.5524 | 9.0 | 936 | 2.3720 | -0.0528 | 0.0 | 0.25 | 0.6944 |
| 1.084 | 10.0 | 1040 | 2.2998 | 0.1051 | 0.1813 | 0.2778 | 0.7222 |
| 1.084 | 11.0 | 1144 | 2.3080 | 0.0503 | 0.0915 | 0.25 | 0.7222 |
| 1.084 | 12.0 | 1248 | 2.5717 | 0.1832 | 0.2575 | 0.3056 | 0.75 |
| 1.084 | 13.0 | 1352 | 2.4900 | 0.1595 | 0.1687 | 0.2778 | 0.7222 |
| 1.084 | 14.0 | 1456 | 2.1492 | 0.4542 | 0.4865 | 0.4444 | 0.8611 |
| 0.7337 | 15.0 | 1560 | 2.6174 | 0.1915 | 0.1490 | 0.3611 | 0.7222 |
| 0.7337 | 16.0 | 1664 | 2.9222 | 0.1796 | 0.1670 | 0.3056 | 0.7222 |
| 0.7337 | 17.0 | 1768 | 3.0746 | 0.1899 | 0.1721 | 0.3056 | 0.7222 |
| 0.7337 | 18.0 | 1872 | 3.1140 | 0.1871 | 0.1549 | 0.3333 | 0.7222 |
| 0.7337 | 19.0 | 1976 | 3.1968 | 0.2181 | 0.1440 | 0.4167 | 0.7222 |
| 0.3911 | 20.0 | 2080 | 3.2334 | 0.1915 | 0.1490 | 0.3611 | 0.7222 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-v3-base-survey-new_fact_main_passage-rater-half-human", "results": []}]} | text-classification | domenicrosati/deberta-v3-base-survey-new_fact_main_passage-rater-half-human | [
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T01:56:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-base-survey-new\_fact\_main\_passage-rater-half-human
================================================================
This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3083
* Krippendorff: 0.9575
* Spearman: 0.9507
* Absolute Agreement: 0.9195
* Agreement Within One: 0.9892
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 6e-06
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 20
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 1.11.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 1.11.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 20\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 1.11.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
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null | 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. -->
# MT-olive-glade-111
This model is a fine-tuned version of [toobiza/MT-smart-feather-100](https://huggingface.co/toobiza/MT-smart-feather-100) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7205
- eval_loss_ce: 0.0008
- eval_loss_bbox: 0.0923
- eval_cardinality_error: 1.0
- eval_giou: 87.2702
- eval_runtime: 45.9717
- eval_samples_per_second: 2.415
- eval_steps_per_second: 0.609
- epoch: 0.11
- step: 50
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "base_model": "toobiza/MT-smart-feather-100", "model-index": [{"name": "MT-olive-glade-111", "results": []}]} | object-detection | AmineAllo/MT-olive-glade-111 | [
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"base_model:toobiza/MT-smart-feather-100",
"endpoints_compatible",
"region:us"
] | 2023-11-12T01:57:45+00:00 | [] | [] | TAGS
#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us
|
# MT-olive-glade-111
This model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7205
- eval_loss_ce: 0.0008
- eval_loss_bbox: 0.0923
- eval_cardinality_error: 1.0
- eval_giou: 87.2702
- eval_runtime: 45.9717
- eval_samples_per_second: 2.415
- eval_steps_per_second: 0.609
- epoch: 0.11
- step: 50
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
| [
"# MT-olive-glade-111\n\nThis model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.7205\n- eval_loss_ce: 0.0008\n- eval_loss_bbox: 0.0923\n- eval_cardinality_error: 1.0\n- eval_giou: 87.2702\n- eval_runtime: 45.9717\n- eval_samples_per_second: 2.415\n- eval_steps_per_second: 0.609\n- epoch: 0.11\n- step: 50",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10",
"### Framework versions\n\n- Transformers 4.33.2\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us \n",
"# MT-olive-glade-111\n\nThis model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.7205\n- eval_loss_ce: 0.0008\n- eval_loss_bbox: 0.0923\n- eval_cardinality_error: 1.0\n- eval_giou: 87.2702\n- eval_runtime: 45.9717\n- eval_samples_per_second: 2.415\n- eval_steps_per_second: 0.609\n- epoch: 0.11\n- step: 50",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10",
"### Framework versions\n\n- Transformers 4.33.2\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.13.3"
] | [
54,
154,
6,
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89,
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] | [
"passage: TAGS\n#transformers #pytorch #table-transformer #object-detection #generated_from_trainer #base_model-toobiza/MT-smart-feather-100 #endpoints_compatible #region-us \n# MT-olive-glade-111\n\nThis model is a fine-tuned version of toobiza/MT-smart-feather-100 on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.7205\n- eval_loss_ce: 0.0008\n- eval_loss_bbox: 0.0923\n- eval_cardinality_error: 1.0\n- eval_giou: 87.2702\n- eval_runtime: 45.9717\n- eval_samples_per_second: 2.415\n- eval_steps_per_second: 0.609\n- epoch: 0.11\n- step: 50## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10### Framework versions\n\n- Transformers 4.33.2\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.13.3"
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] |
null | null | spacy | RE with tok2vec
| Feature | Description |
| --- | --- |
| **Name** | `en_nerry_rel_tok2vec` |
| **Version** | `2.0.0` |
| **spaCy** | `>=3.6.1,<3.7.0` |
| **Default Pipeline** | `tok2vec`, `ner`, `relation_extractor` |
| **Components** | `tok2vec`, `ner`, `relation_extractor` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [HjAnthony]() |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `CRIME`, `PERSON`, `PROCECUTION` |
| **`relation_extractor`** | `INVOVLED_IN` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 70.42 |
| `ENTS_P` | 54.35 |
| `ENTS_R` | 100.00 |
| `REL_MICRO_P` | 55.56 |
| `REL_MICRO_R` | 100.00 |
| `REL_MICRO_F` | 71.43 |
| `TOK2VEC_LOSS` | 0.00 |
| `RELATION_EXTRACTOR_LOSS` | 105.57 | | {"language": ["en"], "tags": ["spacy", "token-classification"]} | token-classification | hjianganthony/en_nerry_rel_tok2vec | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | 2023-11-12T02:03:18+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
| RE with tok2vec
### Label Scheme
View label scheme (4 labels for 2 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4 labels for 2 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 2 components)",
"### Accuracy"
] | [
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null | null | null |
The dataset of just the speech was very small (1~ minute), so there are artifacts in the model. But given such a small amount of data, it doesn't sound too bad
**Dataset Amount**: 01:55
**Steps**: 8910
**Batch File Size**: 8
**Training Time**: 2 hours
**Creator**: Grausamkeeit (Me)
**Voice Actor**: Patrick Warburton
**Сharacter from**: League of Legends
Do not upload this model anywhere without my permission. Private messages are always open so write me and I will reply for sure. | {"language": ["en"], "license": "cc-by-nc-nd-4.0"} | null | Grausamkeeit/Zac_ENG | [
"en",
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2023-11-12T02:04:04+00:00 | [] | [
"en"
] | TAGS
#en #license-cc-by-nc-nd-4.0 #region-us
|
The dataset of just the speech was very small (1~ minute), so there are artifacts in the model. But given such a small amount of data, it doesn't sound too bad
Dataset Amount: 01:55
Steps: 8910
Batch File Size: 8
Training Time: 2 hours
Creator: Grausamkeeit (Me)
Voice Actor: Patrick Warburton
Сharacter from: League of Legends
Do not upload this model anywhere without my permission. Private messages are always open so write me and I will reply for sure. | [] | [
"TAGS\n#en #license-cc-by-nc-nd-4.0 #region-us \n"
] | [
21
] | [
"passage: TAGS\n#en #license-cc-by-nc-nd-4.0 #region-us \n"
] | [
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null | null | spacy | RE with transformer (sentence bert)
| Feature | Description |
| --- | --- |
| **Name** | `en_nerry_rel_trf_sentBert` |
| **Version** | `2.1.0` |
| **spaCy** | `>=3.6.1,<3.7.0` |
| **Default Pipeline** | `transformer`, `ner`, `relation_extractor` |
| **Components** | `transformer`, `ner`, `relation_extractor` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [HjAnthony]() |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `CRIME`, `PERSON`, `PROCECUTION` |
| **`relation_extractor`** | `INVOVLED_IN` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 96.15 |
| `ENTS_P` | 92.59 |
| `ENTS_R` | 100.00 |
| `REL_MICRO_P` | 88.24 |
| `REL_MICRO_R` | 100.00 |
| `REL_MICRO_F` | 93.75 |
| `TRANSFORMER_LOSS` | 0.00 |
| `RELATION_EXTRACTOR_LOSS` | 366.91 | | {"language": ["en"], "tags": ["spacy", "token-classification"]} | token-classification | hjianganthony/en_nerry_rel_trf_sentBert | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | 2023-11-12T02:06:31+00:00 | [] | [
"en"
] | TAGS
#spacy #token-classification #en #model-index #region-us
| RE with transformer (sentence bert)
### Label Scheme
View label scheme (4 labels for 2 components)
### Accuracy
| [
"### Label Scheme\n\n\n\nView label scheme (4 labels for 2 components)",
"### Accuracy"
] | [
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 2 components)",
"### Accuracy"
] | [
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5
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null | null | peft |
# Model Card for Model ID
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- **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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
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|
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## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
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- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
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- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
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] |
null | null | transformers |
ExllamaV2 version of the model created by Heralax!
Original Model https://huggingface.co/Heralax/Augmental-Unholy-13b
Requires ExllamaV2, which is being developed by turboderp https://github.com/turboderp/exllamav2 under an MIT license.
Main branch is 8bpw 8h | {"license": "llama2"} | text-generation | AzureBlack/Augmental-Unholy-13b-exl2 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T02:19:32+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
ExllamaV2 version of the model created by Heralax!
Original Model URL
Requires ExllamaV2, which is being developed by turboderp URL under an MIT license.
Main branch is 8bpw 8h | [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
54
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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] |
null | null | peft |
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### 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 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. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
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## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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- Carbon Emitted:
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[optional]
BibTeX:
APA:
## Glossary [optional]
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The following 'bitsandbytes' quantization config was used during training:
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- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
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] |
null | null | 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": "264.14 +/- 18.34", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | isotnek/ppo-LunarLander | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-12T02:57:55+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | 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. -->
# flan-t5-base-comma-correction
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0492
- Gen Len: 39.5298
- Em Ic: 0.9334
- Em: 0.6384
- Precision: 0.9141
- Recall: 0.8575
- F1: 0.8248
- Levinstein Ratio: 0.9955
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | Em Ic | Em | Precision | Recall | F1 | Levinstein Ratio |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:------:|:------:|:----------------:|
| 0.042 | 1.0 | 2159 | 0.0500 | 39.4971 | 0.9328 | 0.6256 | 0.9175 | 0.8452 | 0.8165 | 0.9954 |
| 0.0342 | 2.0 | 4318 | 0.0492 | 39.5158 | 0.9334 | 0.6361 | 0.9159 | 0.8546 | 0.8229 | 0.9955 |
| 0.0346 | 3.0 | 6477 | 0.0492 | 39.5298 | 0.9334 | 0.6384 | 0.9141 | 0.8575 | 0.8248 | 0.9955 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "base_model": "google/flan-t5-base", "model-index": [{"name": "flan-t5-base-comma-correction", "results": []}]} | text2text-generation | pavlichenko/flan-t5-base-comma-correction | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T02:59:53+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| flan-t5-base-comma-correction
=============================
This model is a fine-tuned version of google/flan-t5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0492
* Gen Len: 39.5298
* Em Ic: 0.9334
* Em: 0.6384
* Precision: 0.9141
* Recall: 0.8575
* F1: 0.8248
* Levinstein Ratio: 0.9955
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 200
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
80,
117,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/flan-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | transformers | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Made by finetuning [google/mt5-small](https://huggingface.co/google/mt5-small).
| {"license": "unknown", "metrics": ["bleu"], "pipeline_tag": "translation"} | translation | aboli-marathe/t5_finetuned | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"translation",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T03:08:01+00:00 | [] | [] | TAGS
#transformers #safetensors #mt5 #text2text-generation #translation #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Model ID
Made by finetuning google/mt5-small.
| [
"# Model Card for Model ID\n\n\n\nMade by finetuning google/mt5-small."
] | [
"TAGS\n#transformers #safetensors #mt5 #text2text-generation #translation #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID\n\n\n\nMade by finetuning google/mt5-small."
] | [
60,
19
] | [
"passage: TAGS\n#transformers #safetensors #mt5 #text2text-generation #translation #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID\n\n\n\nMade by finetuning google/mt5-small."
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null | null | ml-agents |
# **ppo** Agent playing **Crawler**
This is a trained model of a **ppo** agent playing **Crawler**
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: farzintava/unity-ML-agent-Crawler-PPO
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Crawler", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Crawler"]} | reinforcement-learning | farzintava/unity-ML-agent-Crawler-PPO | [
"ml-agents",
"Crawler",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Crawler",
"region:us"
] | 2023-11-12T03:19:14+00:00 | [] | [] | TAGS
#ml-agents #Crawler #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Crawler #region-us
|
# ppo Agent playing Crawler
This is a trained model of a ppo agent playing Crawler
using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
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: URL
- A *longer tutorial* to understand how works ML-Agents:
URL
### Resume the training
### 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 URL
2. Step 1: Find your model_id: farzintava/unity-ML-agent-Crawler-PPO
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play
| [
"# ppo Agent playing Crawler\n This is a trained model of a ppo agent playing Crawler\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: farzintava/unity-ML-agent-Crawler-PPO\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
"TAGS\n#ml-agents #Crawler #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Crawler #region-us \n",
"# ppo Agent playing Crawler\n This is a trained model of a ppo agent playing Crawler\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: farzintava/unity-ML-agent-Crawler-PPO\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
38,
210
] | [
"passage: TAGS\n#ml-agents #Crawler #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Crawler #region-us \n# ppo Agent playing Crawler\n This is a trained model of a ppo agent playing Crawler\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: farzintava/unity-ML-agent-Crawler-PPO\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play"
] | [
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null | 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. -->
# bert-base-uncased-emotion
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1303
- Accuracy: 0.9385
- F1: 0.9391
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5346 | 1.0 | 250 | 0.1825 | 0.931 | 0.9315 |
| 0.1336 | 2.0 | 500 | 0.1303 | 0.9385 | 0.9391 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9385, "name": "Accuracy"}, {"type": "f1", "value": 0.9391111929326489, "name": "F1"}]}]}]} | text-classification | RicoCHEH/bert-base-uncased-emotion | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T03:19:41+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #dataset-emotion #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-emotion
=========================
This model is a fine-tuned version of bert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1303
* Accuracy: 0.9385
* F1: 0.9391
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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #dataset-emotion #base_model-bert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | null | # mesolitica/malaysian-tinyllama-1.1b-16384-instructions-GGUF
Quantized GGUF model files for [malaysian-tinyllama-1.1b-16384-instructions](https://huggingface.co/mesolitica/malaysian-tinyllama-1.1b-16384-instructions) from [mesolitica](https://huggingface.co/mesolitica)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [malaysian-tinyllama-1.1b-16384-instructions.q2_k.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16384-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16384-instructions.q2_k.gguf) | q2_k | 482.14 MB |
| [malaysian-tinyllama-1.1b-16384-instructions.q3_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16384-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16384-instructions.q3_k_m.gguf) | q3_k_m | 549.85 MB |
| [malaysian-tinyllama-1.1b-16384-instructions.q4_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16384-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16384-instructions.q4_k_m.gguf) | q4_k_m | 667.81 MB |
| [malaysian-tinyllama-1.1b-16384-instructions.q5_k_m.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16384-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16384-instructions.q5_k_m.gguf) | q5_k_m | 782.04 MB |
| [malaysian-tinyllama-1.1b-16384-instructions.q6_k.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16384-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16384-instructions.q6_k.gguf) | q6_k | 903.41 MB |
| [malaysian-tinyllama-1.1b-16384-instructions.q8_0.gguf](https://huggingface.co/afrideva/malaysian-tinyllama-1.1b-16384-instructions-GGUF/resolve/main/malaysian-tinyllama-1.1b-16384-instructions.q8_0.gguf) | q8_0 | 1.17 GB |
## Original Model Card:
| {"tags": ["gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0"], "model_name": "malaysian-tinyllama-1.1b-16384-instructions", "base_model": "mesolitica/malaysian-tinyllama-1.1b-16384-instructions", "inference": false, "model_creator": "mesolitica", "pipeline_tag": "text-generation", "quantized_by": "afrideva"} | text-generation | afrideva/malaysian-tinyllama-1.1b-16384-instructions-GGUF | [
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"base_model:mesolitica/malaysian-tinyllama-1.1b-16384-instructions",
"region:us"
] | 2023-11-12T03:22:55+00:00 | [] | [] | TAGS
#gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #base_model-mesolitica/malaysian-tinyllama-1.1b-16384-instructions #region-us
| mesolitica/malaysian-tinyllama-1.1b-16384-instructions-GGUF
===========================================================
Quantized GGUF model files for malaysian-tinyllama-1.1b-16384-instructions from mesolitica
Name: malaysian-tinyllama-1.1b-16384-instructions.q2\_k.gguf, Quant method: q2\_k, Size: 482.14 MB
Name: malaysian-tinyllama-1.1b-16384-instructions.q3\_k\_m.gguf, Quant method: q3\_k\_m, Size: 549.85 MB
Name: malaysian-tinyllama-1.1b-16384-instructions.q4\_k\_m.gguf, Quant method: q4\_k\_m, Size: 667.81 MB
Name: malaysian-tinyllama-1.1b-16384-instructions.q5\_k\_m.gguf, Quant method: q5\_k\_m, Size: 782.04 MB
Name: malaysian-tinyllama-1.1b-16384-instructions.q6\_k.gguf, Quant method: q6\_k, Size: 903.41 MB
Name: malaysian-tinyllama-1.1b-16384-instructions.q8\_0.gguf, Quant method: q8\_0, Size: 1.17 GB
Original Model Card:
--------------------
| [] | [
"TAGS\n#gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #base_model-mesolitica/malaysian-tinyllama-1.1b-16384-instructions #region-us \n"
] | [
80
] | [
"passage: TAGS\n#gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #base_model-mesolitica/malaysian-tinyllama-1.1b-16384-instructions #region-us \n"
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null | null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# my_awesome_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.0442
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6357 | 0.0973 |
| No log | 1.87 | 7 | nan | 0.0442 |
| 6.6006 | 2.93 | 11 | nan | 0.0442 |
| 6.6006 | 4.0 | 15 | nan | 0.0442 |
| 6.6006 | 4.8 | 18 | nan | 0.0442 |
| 0.0 | 5.87 | 22 | nan | 0.0442 |
| 0.0 | 6.93 | 26 | nan | 0.0442 |
| 0.0 | 8.0 | 30 | nan | 0.0442 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["minds14"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "my_awesome_mind_model", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "minds14", "type": "minds14", "config": "en-US", "split": "train", "args": "en-US"}, "metrics": [{"type": "accuracy", "value": 0.04424778761061947, "name": "Accuracy"}]}]}]} | audio-classification | sorakna/my_awesome_mind_model | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-12T03:29:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us
| my\_awesome\_mind\_model
========================
This model is a fine-tuned version of facebook/wav2vec2-base on the minds14 dataset.
It achieves the following results on the evaluation set:
* Loss: nan
* Accuracy: 0.0442
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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | null | {} | null | Nestore/Proyecto_IA | [
"region:us"
] | 2023-11-12T03:34:42+00:00 | [] | [] | TAGS
#region-us
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
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||
null | null | diffusers | # fiamix
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/ikysu/fiamix/tree/main) them in the Files & versions tab. | {"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora", "image-to-image"], "widget": [{"text": "1boy, black eyes, black hair, blurry, blurry foreground, caution tape, clothes writing, depth of field, english text, glasses, holding, long sleeves, male focus, photo \\(medium\\), realistic, round eyewear, shirt, solo, upper body, yellow coat, yellow hoodie, yellow jacket, yellow raincoat, yellow shirt", "output": {"url": "images/00004-4286720701.png"}}], "base_model": "runwayml/stable-diffusion-v1-5", "pipeline_tag": "image-to-image"} | image-to-image | ikysu/fiamix | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"image-to-image",
"base_model:runwayml/stable-diffusion-v1-5",
"region:us"
] | 2023-11-12T03:36:00+00:00 | [] | [] | TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #image-to-image #base_model-runwayml/stable-diffusion-v1-5 #region-us
| # fiamix
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab. | [
"# fiamix\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #image-to-image #base_model-runwayml/stable-diffusion-v1-5 #region-us \n",
"# fiamix\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] | [
60,
8,
28
] | [
"passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #image-to-image #base_model-runwayml/stable-diffusion-v1-5 #region-us \n# fiamix\n\n<Gallery />## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
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null | null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# islam-LLM
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 320
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "base_model": "ybelkada/falcon-7b-sharded-bf16", "model-index": [{"name": "islam-LLM", "results": []}]} | null | kheder/islam-LLM | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"region:us"
] | 2023-11-12T03:44:12+00:00 | [] | [] | TAGS
#tensorboard #safetensors #generated_from_trainer #base_model-ybelkada/falcon-7b-sharded-bf16 #region-us
|
# islam-LLM
This model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 320
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# islam-LLM\n\nThis model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 320",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-ybelkada/falcon-7b-sharded-bf16 #region-us \n",
"# islam-LLM\n\nThis model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 320",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
42,
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128,
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33
] | [
"passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-ybelkada/falcon-7b-sharded-bf16 #region-us \n# islam-LLM\n\nThis model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 320### Training results### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.1.0+cu118\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | 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. -->
# zephyr-7b-sft-full
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9323
## 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
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1295 | 0.67 | 272 | 0.9321 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "zephyr-7b-sft-full", "results": []}]} | text-generation | pkarypis/zephyr-7b-sft-full | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T03:44:54+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| zephyr-7b-sft-full
==================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9323
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
* distributed\_type: multi-GPU
* num\_devices: 16
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 512
* total\_eval\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 1.0
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.0.1+cu117
* Datasets 2.14.5
* Tokenizers 0.14.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 512\n* total\\_eval\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.5\n* Tokenizers 0.14.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 512\n* total\\_eval\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.5\n* Tokenizers 0.14.0"
] | [
86,
161,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 16\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 512\n* total\\_eval\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 1.0### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.5\n* Tokenizers 0.14.0"
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null | null | diffusers | ### cloth04-sweater on Stable Diffusion via Dreambooth
#### model by Charles2023
This your the Stable Diffusion model fine-tuned the cloth04-sweater concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the `instance_prompt`: **photo of a <yzxyzx> sweater**
You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb).
And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts)
Here are the images used for training this concept:
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
| {"license": "creativeml-openrail-m", "tags": ["text-to-image"]} | text-to-image | Charles2023/cloth04-sweater | [
"diffusers",
"safetensors",
"text-to-image",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-12T04:11:00+00:00 | [] | [] | TAGS
#diffusers #safetensors #text-to-image #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### cloth04-sweater on Stable Diffusion via Dreambooth
#### model by Charles2023
This your the Stable Diffusion model fine-tuned the cloth04-sweater concept taught to Stable Diffusion with Dreambooth.
It can be used by modifying the 'instance_prompt': photo of a <yzxyzx> sweater
You can also train your own concepts and upload them to the library by using this notebook.
And you can run your new concept via 'diffusers': Colab Notebook for Inference, Spaces with the Public Concepts loaded
Here are the images used for training this concept:
!image 0.jpg)
!image 1.jpg)
!image 2.jpg)
!image 3.jpg)
!image 4.jpg)
!image 5.jpg)
!image 6.jpg)
!image 7.jpg)
!image 8.jpg)
!image 9.jpg)
| [
"### cloth04-sweater on Stable Diffusion via Dreambooth",
"#### model by Charles2023\nThis your the Stable Diffusion model fine-tuned the cloth04-sweater concept taught to Stable Diffusion with Dreambooth.\nIt can be used by modifying the 'instance_prompt': photo of a <yzxyzx> sweater\n\nYou can also train your own concepts and upload them to the library by using this notebook.\nAnd you can run your new concept via 'diffusers': Colab Notebook for Inference, Spaces with the Public Concepts loaded\n\nHere are the images used for training this concept:\n!image 0.jpg)\n!image 1.jpg)\n!image 2.jpg)\n!image 3.jpg)\n!image 4.jpg)\n!image 5.jpg)\n!image 6.jpg)\n!image 7.jpg)\n!image 8.jpg)\n!image 9.jpg)"
] | [
"TAGS\n#diffusers #safetensors #text-to-image #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### cloth04-sweater on Stable Diffusion via Dreambooth",
"#### model by Charles2023\nThis your the Stable Diffusion model fine-tuned the cloth04-sweater concept taught to Stable Diffusion with Dreambooth.\nIt can be used by modifying the 'instance_prompt': photo of a <yzxyzx> sweater\n\nYou can also train your own concepts and upload them to the library by using this notebook.\nAnd you can run your new concept via 'diffusers': Colab Notebook for Inference, Spaces with the Public Concepts loaded\n\nHere are the images used for training this concept:\n!image 0.jpg)\n!image 1.jpg)\n!image 2.jpg)\n!image 3.jpg)\n!image 4.jpg)\n!image 5.jpg)\n!image 6.jpg)\n!image 7.jpg)\n!image 8.jpg)\n!image 9.jpg)"
] | [
54,
19,
181
] | [
"passage: TAGS\n#diffusers #safetensors #text-to-image #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### cloth04-sweater on Stable Diffusion via Dreambooth#### model by Charles2023\nThis your the Stable Diffusion model fine-tuned the cloth04-sweater concept taught to Stable Diffusion with Dreambooth.\nIt can be used by modifying the 'instance_prompt': photo of a <yzxyzx> sweater\n\nYou can also train your own concepts and upload them to the library by using this notebook.\nAnd you can run your new concept via 'diffusers': Colab Notebook for Inference, Spaces with the Public Concepts loaded\n\nHere are the images used for training this concept:\n!image 0.jpg)\n!image 1.jpg)\n!image 2.jpg)\n!image 3.jpg)\n!image 4.jpg)\n!image 5.jpg)\n!image 6.jpg)\n!image 7.jpg)\n!image 8.jpg)\n!image 9.jpg)"
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null | null | transformers | This model if equal to the model created by nielsr. The objective is just for me to study and test the notebook he used.
Model link: https://huggingface.co/nielsr/codet5-small-code-summarization-ruby | {} | text2text-generation | JoaoJunior/codet5-small-code-summarization-ruby-personal-learning | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T04:11:30+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This model if equal to the model created by nielsr. The objective is just for me to study and test the notebook he used.
Model link: URL | [] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
49
] | [
"passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | 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. -->
# NotNighter/my_awesome_qa_model
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:
- Train Loss: 5.7443
- Validation Loss: 5.8060
- 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 10, '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 | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 5.8862 | 5.8561 | 0 |
| 5.7443 | 5.8060 | 1 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "NotNighter/my_awesome_qa_model", "results": []}]} | question-answering | Nighter/my_awesome_qa_model | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-12T04:26:20+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #has_space #region-us
| NotNighter/my\_awesome\_qa\_model
=================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 5.7443
* Validation Loss: 5.8060
* 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': False, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 10, '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
### Framework versions
* Transformers 4.35.0
* TensorFlow 2.14.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 10, '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}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 10, '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}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
67,
304,
4,
31
] | [
"passage: TAGS\n#transformers #tf #distilbert #question-answering #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 10, '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}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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] |
null | null | transformers | # GeneZC/MiniChat-3B-GGUF
Quantized GGUF model files for [MiniChat-3B](https://huggingface.co/GeneZC/MiniChat-3B) from [GeneZC](https://huggingface.co/GeneZC)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [minichat-3b.q2_k.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q2_k.gguf) | q2_k | 1.30 GB |
| [minichat-3b.q3_k_m.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q3_k_m.gguf) | q3_k_m | 1.51 GB |
| [minichat-3b.q4_k_m.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q4_k_m.gguf) | q4_k_m | 1.85 GB |
| [minichat-3b.q5_k_m.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q5_k_m.gguf) | q5_k_m | 2.15 GB |
| [minichat-3b.q6_k.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q6_k.gguf) | q6_k | 2.48 GB |
| [minichat-3b.q8_0.gguf](https://huggingface.co/afrideva/MiniChat-3B-GGUF/resolve/main/minichat-3b.q8_0.gguf) | q8_0 | 3.21 GB |
## Original Model Card:
## MiniChat-3B
📑 [arXiv]() | 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) | 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) | 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) | 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B)
❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2.
A language model distilled and finetuned from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models".
Outperforming a wide range of 3B competitors in GPT4 evaluation and even competing with several 7B chat models.
<img src="./teaser_b.jpg" alt="teaser_b" width="687" />
The following is an example code snippet to use MiniChat-3B:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from conversation import get_default_conv_template
# MiniChat
tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B", use_fast=False)
# GPU.
model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
# CPU.
# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval()
conv = get_default_conv_template("minichat")
question = "Implement a program to find the common elements in two arrays without using any extra data structures."
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer([prompt]).input_ids
output_ids = model.generate(
torch.as_tensor(input_ids).cuda(),
do_sample=True,
temperature=0.7,
max_new_tokens=1024,
)
output_ids = output_ids[0][len(input_ids[0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
# output: "def common_elements(arr1, arr2):\n if len(arr1) == 0:\n return []\n if len(arr2) == 0:\n return arr1\n\n common_elements = []\n for element in arr1:\n if element in arr2:\n common_elements.append(element)\n\n return common_elements"
# Multiturn conversation could be realized by continuously appending questions to `conv`.
```
## Bibtex
```bibtex
@article{zhang2023law,
title={Towards the Law of Capacity Gap in Distilling Language Models},
author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan},
year={2023},
url={}
}
``` | {"language": ["en", "zh"], "license": "apache-2.0", "library_name": "transformers", "tags": ["gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0"], "model_name": "MiniChat-3B", "base_model": "GeneZC/MiniChat-3B", "inference": false, "model_creator": "GeneZC", "pipeline_tag": "text-generation", "quantized_by": "afrideva"} | text-generation | afrideva/MiniChat-3B-GGUF | [
"transformers",
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"en",
"zh",
"base_model:GeneZC/MiniChat-3B",
"license:apache-2.0",
"has_space",
"region:us"
] | 2023-11-12T04:30:51+00:00 | [] | [
"en",
"zh"
] | TAGS
#transformers #gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #en #zh #base_model-GeneZC/MiniChat-3B #license-apache-2.0 #has_space #region-us
| GeneZC/MiniChat-3B-GGUF
=======================
Quantized GGUF model files for MiniChat-3B from GeneZC
Name: minichat-3b.q2\_k.gguf, Quant method: q2\_k, Size: 1.30 GB
Name: minichat-3b.q3\_k\_m.gguf, Quant method: q3\_k\_m, Size: 1.51 GB
Name: minichat-3b.q4\_k\_m.gguf, Quant method: q4\_k\_m, Size: 1.85 GB
Name: minichat-3b.q5\_k\_m.gguf, Quant method: q5\_k\_m, Size: 2.15 GB
Name: minichat-3b.q6\_k.gguf, Quant method: q6\_k, Size: 2.48 GB
Name: minichat-3b.q8\_0.gguf, Quant method: q8\_0, Size: 3.21 GB
Original Model Card:
--------------------
MiniChat-3B
-----------
arXiv | HuggingFace-MiniMA | HuggingFace-MiniChat | ModelScope-MiniMA | ModelScope-MiniChat
Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2.
A language model distilled and finetuned from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models".
Outperforming a wide range of 3B competitors in GPT4 evaluation and even competing with several 7B chat models.
The following is an example code snippet to use MiniChat-3B:
Bibtex
------
| [] | [
"TAGS\n#transformers #gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #en #zh #base_model-GeneZC/MiniChat-3B #license-apache-2.0 #has_space #region-us \n"
] | [
88
] | [
"passage: TAGS\n#transformers #gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #en #zh #base_model-GeneZC/MiniChat-3B #license-apache-2.0 #has_space #region-us \n"
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null | 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. -->
# Whisper Small Persian
This model is a fine-tuned version of [hwrpartai/Whisper-small-Mozilla](https://huggingface.co/hwrpartai/Whisper-small-Mozilla) on the Common Voice 15.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 14
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"language": ["fa"], "license": "apache-2.0", "tags": ["fa-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_15_0"], "base_model": "hwrpartai/Whisper-small-Mozilla", "model-index": [{"name": "Whisper Small Persian", "results": []}]} | automatic-speech-recognition | hwrpartai/Whisper-small-Mozilla | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"fa-asr-leaderboard",
"generated_from_trainer",
"fa",
"dataset:mozilla-foundation/common_voice_15_0",
"base_model:hwrpartai/Whisper-small-Mozilla",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-12T04:31:21+00:00 | [] | [
"fa"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #fa-asr-leaderboard #generated_from_trainer #fa #dataset-mozilla-foundation/common_voice_15_0 #base_model-hwrpartai/Whisper-small-Mozilla #license-apache-2.0 #endpoints_compatible #region-us
|
# Whisper Small Persian
This model is a fine-tuned version of hwrpartai/Whisper-small-Mozilla on the Common Voice 15.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 14
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 56
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# Whisper Small Persian\n\nThis model is a fine-tuned version of hwrpartai/Whisper-small-Mozilla on the Common Voice 15.0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 14\n- eval_batch_size: 10\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 56\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- training_steps: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.0.1+cu117\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #fa-asr-leaderboard #generated_from_trainer #fa #dataset-mozilla-foundation/common_voice_15_0 #base_model-hwrpartai/Whisper-small-Mozilla #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Whisper Small Persian\n\nThis model is a fine-tuned version of hwrpartai/Whisper-small-Mozilla on the Common Voice 15.0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 14\n- eval_batch_size: 10\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 56\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- training_steps: 5\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.0.1+cu117\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
105,
41,
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"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #fa-asr-leaderboard #generated_from_trainer #fa #dataset-mozilla-foundation/common_voice_15_0 #base_model-hwrpartai/Whisper-small-Mozilla #license-apache-2.0 #endpoints_compatible #region-us \n# Whisper Small Persian\n\nThis model is a fine-tuned version of hwrpartai/Whisper-small-Mozilla on the Common Voice 15.0 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 14\n- eval_batch_size: 10\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 56\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 50\n- training_steps: 5\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 2.0.1+cu117\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | 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. -->
# jmassot/masked-lm-tpu
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 9.8173
- Train Accuracy: 0.0164
- Validation Loss: 9.6999
- Validation Accuracy: 0.0210
- Epoch: 9
## 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 22325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 10.2539 | 0.0 | 10.2414 | 0.0000 | 0 |
| 10.2396 | 0.0000 | 10.2294 | 0.0000 | 1 |
| 10.2338 | 0.0000 | 10.2031 | 0.0000 | 2 |
| 10.2003 | 0.0000 | 10.1587 | 0.0000 | 3 |
| 10.1691 | 0.0 | 10.1081 | 0.0 | 4 |
| 10.1135 | 0.0000 | 10.0415 | 0.0001 | 5 |
| 10.0630 | 0.0001 | 9.9697 | 0.0013 | 6 |
| 9.9906 | 0.0011 | 9.8881 | 0.0097 | 7 |
| 9.9059 | 0.0070 | 9.7998 | 0.0183 | 8 |
| 9.8173 | 0.0164 | 9.6999 | 0.0210 | 9 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.12.0
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "roberta-base", "model-index": [{"name": "jmassot/masked-lm-tpu", "results": []}]} | fill-mask | jmassot/masked-lm-tpu | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T04:31:45+00:00 | [] | [] | TAGS
#transformers #tf #roberta #fill-mask #generated_from_keras_callback #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| jmassot/masked-lm-tpu
=====================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 9.8173
* Train Accuracy: 0.0164
* Validation Loss: 9.6999
* Validation Accuracy: 0.0210
* Epoch: 9
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': {'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_schedule\_fn': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_steps': 22325, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\_serialization**': True}, 'warmup\_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.001}
* training\_precision: float32
### Training results
### Framework versions
* Transformers 4.35.0
* TensorFlow 2.12.0
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_steps': 22325, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\\_serialization**': True}, 'warmup\\_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.001}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.12.0\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_steps': 22325, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\\_serialization**': True}, 'warmup\\_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.001}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.12.0\n* Tokenizers 0.14.1"
] | [
61,
285,
4,
25
] | [
"passage: TAGS\n#transformers #tf #roberta #fill-mask #generated_from_keras_callback #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_steps': 22325, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '**passive\\_serialization**': True}, 'warmup\\_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.001}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.12.0\n* Tokenizers 0.14.1"
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null | 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. -->
# 4248-spanBERT-Base
This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "datasets": ["squad"], "base_model": "SpanBERT/spanbert-base-cased", "model-index": [{"name": "4248-spanBERT-Base", "results": []}]} | question-answering | JMatthewChiam/4248-spanBERT-Base | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:SpanBERT/spanbert-base-cased",
"endpoints_compatible",
"region:us"
] | 2023-11-12T04:42:07+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-SpanBERT/spanbert-base-cased #endpoints_compatible #region-us
|
# 4248-spanBERT-Base
This model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.13.3
| [
"# 4248-spanBERT-Base\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 6",
"### Training results",
"### Framework versions\n\n- Transformers 4.32.1\n- Pytorch 2.1.0\n- Datasets 2.14.5\n- Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-SpanBERT/spanbert-base-cased #endpoints_compatible #region-us \n",
"# 4248-spanBERT-Base\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 6",
"### Training results",
"### Framework versions\n\n- Transformers 4.32.1\n- Pytorch 2.1.0\n- Datasets 2.14.5\n- Tokenizers 0.13.3"
] | [
59,
38,
6,
12,
8,
3,
106,
4,
30
] | [
"passage: TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-SpanBERT/spanbert-base-cased #endpoints_compatible #region-us \n# 4248-spanBERT-Base\n\nThis model is a fine-tuned version of SpanBERT/spanbert-base-cased on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 6### Training results### Framework versions\n\n- Transformers 4.32.1\n- Pytorch 2.1.0\n- Datasets 2.14.5\n- Tokenizers 0.13.3"
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null | 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. -->
# my_awesome_qa_model
This model is a fine-tuned version of [bhadresh-savani/electra-base-squad2](https://huggingface.co/bhadresh-savani/electra-base-squad2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1121
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 0.1052 |
| 0.2282 | 2.0 | 500 | 0.1061 |
| 0.2282 | 3.0 | 750 | 0.1121 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "base_model": "bhadresh-savani/electra-base-squad2", "model-index": [{"name": "my_awesome_qa_model", "results": []}]} | question-answering | pcgiang/my_awesome_qa_model | [
"transformers",
"tensorboard",
"safetensors",
"electra",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:bhadresh-savani/electra-base-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | 2023-11-12T04:42:56+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #electra #question-answering #generated_from_trainer #dataset-squad #base_model-bhadresh-savani/electra-base-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us
| my\_awesome\_qa\_model
======================
This model is a fine-tuned version of bhadresh-savani/electra-base-squad2 on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1121
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: 3
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu118
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #electra #question-answering #generated_from_trainer #dataset-squad #base_model-bhadresh-savani/electra-base-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
77,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #electra #question-answering #generated_from_trainer #dataset-squad #base_model-bhadresh-savani/electra-base-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu118\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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 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. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
| {"library_name": "peft", "base_model": "vilm/vietcuna-7b-v3"} | null | nero1342/vcn-7b-500it-explain-gpt4 | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:vilm/vietcuna-7b-v3",
"region:us"
] | 2023-11-12T04:44:38+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-vilm/vietcuna-7b-v3 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
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null | null | null | https://civitai.com/models/123573/hoolheyak-arknights | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/ho_olheyak_arknights | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T04:49:38+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | null | https://civitai.com/models/145400/leone-akame-ga-kill | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/leone-10 | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T04:54:15+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | 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. -->
# yinani24/my_distilbert_ft_model_2
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:
- Train Loss: 0.6244
- Validation Loss: 0.6133
- Train Accuracy: 0.8667
- 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 15, '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 | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6929 | 0.6831 | 0.8667 | 0 |
| 0.6791 | 0.6582 | 0.8667 | 1 |
| 0.6244 | 0.6133 | 0.8667 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "yinani24/my_distilbert_ft_model_2", "results": []}]} | multiple-choice | yinani24/my_distilbert_ft_model_2 | [
"transformers",
"tf",
"distilbert",
"multiple-choice",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-12T04:54:23+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #multiple-choice #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
| yinani24/my\_distilbert\_ft\_model\_2
=====================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.6244
* Validation Loss: 0.6133
* Train Accuracy: 0.8667
* 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', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': False, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 0.0001, 'decay\_steps': 15, '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
### Framework versions
* Transformers 4.35.2
* TensorFlow 2.14.0
* Datasets 2.16.1
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_steps': 15, '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}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.14.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tf #distilbert #multiple-choice #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_steps': 15, '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}\n* training\\_precision: float32",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.14.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
] | [
63,
303,
4,
31
] | [
"passage: TAGS\n#transformers #tf #distilbert #multiple-choice #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 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': False, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 0.0001, 'decay\\_steps': 15, '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}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.14.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1"
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null | null | null | https://civitai.com/models/111591/leone-akame-ga-kill | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/leone_v1 | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T04:55:14+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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"passage: TAGS\n#license-creativeml-openrail-m #region-us \n"
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null | null | null |
# Model Trained Using AutoTrain | {"tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]} | text-generation | Capstone-lpx/mistral-7b-mj-finetuned_tryy | [
"autotrain",
"text-generation",
"region:us"
] | 2023-11-12T05:00:23+00:00 | [] | [] | TAGS
#autotrain #text-generation #region-us
|
# Model Trained Using AutoTrain | [
"# Model Trained Using AutoTrain"
] | [
"TAGS\n#autotrain #text-generation #region-us \n",
"# Model Trained Using AutoTrain"
] | [
15,
9
] | [
"passage: TAGS\n#autotrain #text-generation #region-us \n# Model Trained Using AutoTrain"
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null | null | transformers | Made by finetuning [bart-large](https://huggingface.co/facebook/bart-large). | {} | text2text-generation | aboli-marathe/t5_finetuned_bart | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T05:01:56+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Made by finetuning bart-large. | [] | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
39
] | [
"passage: TAGS\n#transformers #safetensors #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | null | null | # Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).
- Provide the model to an app such as **Mochi Diffusion** [Github](https://github.com/godly-devotion/MochiDiffusion) / [Discord](https://discord.gg/x2kartzxGv) to generate images.
- `split_einsum` version is compatible with all compute unit options including Neural Engine.
- `original` version is only compatible with `CPU & GPU` option.
- Different resolution versions are tagged accordingly.
<br>
# SSD-1B_8bit:
Source: [Segmind](https://blog.segmind.com/introducing-segmind-ssd-1b/amp/)<br>
## SSD-1B
## This is the original Segmind SSD-1B base model converted and quantized to 8-bits.
The SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.
SSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model.
This model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.
SSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours.
| {"license": "creativeml-openrail-m", "tags": ["coreml", "stable-diffusion", "text-to-image"]} | text-to-image | coreml-community/coreml-SSD-1B_8bit | [
"coreml",
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T05:06:23+00:00 | [] | [] | TAGS
#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us
| # Core ML Converted Model:
- This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.
- Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.
- 'split_einsum' version is compatible with all compute unit options including Neural Engine.
- 'original' version is only compatible with 'CPU & GPU' option.
- Different resolution versions are tagged accordingly.
<br>
# SSD-1B_8bit:
Source: Segmind<br>
## SSD-1B
## This is the original Segmind SSD-1B base model converted and quantized to 8-bits.
!image
!image
!image
!image
The SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.
SSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model.
This model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.
SSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours.
| [
"# Core ML Converted Model:\n\n - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.\n - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.\n - 'split_einsum' version is compatible with all compute unit options including Neural Engine.\n - 'original' version is only compatible with 'CPU & GPU' option.\n - Different resolution versions are tagged accordingly.\n\n<br>",
"# SSD-1B_8bit:\nSource: Segmind<br>",
"## SSD-1B",
"## This is the original Segmind SSD-1B base model converted and quantized to 8-bits.\n\n\n!image\n\n!image\n\n!image\n\n!image\n\nThe SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.\n\nSSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model. \n\nThis model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.\n\nSSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours."
] | [
"TAGS\n#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us \n",
"# Core ML Converted Model:\n\n - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.\n - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.\n - 'split_einsum' version is compatible with all compute unit options including Neural Engine.\n - 'original' version is only compatible with 'CPU & GPU' option.\n - Different resolution versions are tagged accordingly.\n\n<br>",
"# SSD-1B_8bit:\nSource: Segmind<br>",
"## SSD-1B",
"## This is the original Segmind SSD-1B base model converted and quantized to 8-bits.\n\n\n!image\n\n!image\n\n!image\n\n!image\n\nThe SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.\n\nSSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model. \n\nThis model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.\n\nSSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours."
] | [
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110,
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333
] | [
"passage: TAGS\n#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us \n# Core ML Converted Model:\n\n - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.\n - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.\n - 'split_einsum' version is compatible with all compute unit options including Neural Engine.\n - 'original' version is only compatible with 'CPU & GPU' option.\n - Different resolution versions are tagged accordingly.\n\n<br># SSD-1B_8bit:\nSource: Segmind<br>## SSD-1B## This is the original Segmind SSD-1B base model converted and quantized to 8-bits.\n\n\n!image\n\n!image\n\n!image\n\n!image\n\nThe SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.\n\nSSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model. \n\nThis model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.\n\nSSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours."
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null | null | null | # Core ML Converted Model:
- This model was converted to [Core ML for use on Apple Silicon devices](https://github.com/apple/ml-stable-diffusion). Conversion instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-or-safetensors-files-to-Core-ML).
- Provide the model to an app such as **Mochi Diffusion** [Github](https://github.com/godly-devotion/MochiDiffusion) / [Discord](https://discord.gg/x2kartzxGv) to generate images.
- `split_einsum` version is compatible with all compute unit options including Neural Engine.
- `original` version is only compatible with `CPU & GPU` option.
- Different resolution versions are tagged accordingly.
<br>
# SSD-1B-ZachsEmoji-v1_8bit:
Source: This trained model is unique to this repo!<br>
## SSD-1B Zach's Emoji v1.0
## This is a custom trained SSD-1B type base model converted and quantized to 8-bits.
The SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.
SSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model.
This model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.
SSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours.
**This particular version was trained by Zach Nagengast on a set of emoji images.** | {"license": "creativeml-openrail-m", "tags": ["coreml", "stable-diffusion", "text-to-image"]} | text-to-image | coreml-community/coreml-SSD-1B-ZachsEmoji-v1_8bit | [
"coreml",
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T05:09:22+00:00 | [] | [] | TAGS
#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us
| # Core ML Converted Model:
- This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.
- Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.
- 'split_einsum' version is compatible with all compute unit options including Neural Engine.
- 'original' version is only compatible with 'CPU & GPU' option.
- Different resolution versions are tagged accordingly.
<br>
# SSD-1B-ZachsEmoji-v1_8bit:
Source: This trained model is unique to this repo!<br>
## SSD-1B Zach's Emoji v1.0
## This is a custom trained SSD-1B type base model converted and quantized to 8-bits.
!image
!image
The SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.
SSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model.
This model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.
SSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours.
This particular version was trained by Zach Nagengast on a set of emoji images. | [
"# Core ML Converted Model:\n\n - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.\n - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.\n - 'split_einsum' version is compatible with all compute unit options including Neural Engine.\n - 'original' version is only compatible with 'CPU & GPU' option.\n - Different resolution versions are tagged accordingly.\n\n<br>",
"# SSD-1B-ZachsEmoji-v1_8bit:\nSource: This trained model is unique to this repo!<br>",
"## SSD-1B Zach's Emoji v1.0",
"## This is a custom trained SSD-1B type base model converted and quantized to 8-bits.\n\n!image\n\n!image\n\nThe SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.\n\nSSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model. \n\nThis model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.\n\nSSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours.\n\nThis particular version was trained by Zach Nagengast on a set of emoji images."
] | [
"TAGS\n#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us \n",
"# Core ML Converted Model:\n\n - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.\n - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.\n - 'split_einsum' version is compatible with all compute unit options including Neural Engine.\n - 'original' version is only compatible with 'CPU & GPU' option.\n - Different resolution versions are tagged accordingly.\n\n<br>",
"# SSD-1B-ZachsEmoji-v1_8bit:\nSource: This trained model is unique to this repo!<br>",
"## SSD-1B Zach's Emoji v1.0",
"## This is a custom trained SSD-1B type base model converted and quantized to 8-bits.\n\n!image\n\n!image\n\nThe SSD-1B model has been meticulously engineered with a strong focus on speed and efficiency. It delivers a remarkable 60% speed up in inference and fine-tuning compared to SDXL, rendering it an ideal choice for real-time applications and situations where rapid image generation is a critical requirement.\n\nSSD-1B is 50 percent more compact compared to SDXL at the same bit depth, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. It is a 1.3 bilion parameter model where several layers have been removed from the base SDXL model. \n\nThis model employs a knowledge distillation strategy, where it leverages the teachings of several expert models in succession, including SDXL 1.0, ZavyChromaXL, and JuggernautXL, to combine their strengths and produce impressive images. This model training also included data from a variety of datasets, including GRIT and Midjourney scrape data. A total of around 15 million data points (image-prompt pairs) were used during the training process. This diverse training data equips SSD-1B with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts.\n\nSSD-1B comes with strong generation abilities out of the box, but for the best performance on your specific task, we recommend fine-tuning the model on your private data. This process can be done in hours.\n\nThis particular version was trained by Zach Nagengast on a set of emoji images."
] | [
34,
110,
32,
11,
349
] | [
"passage: TAGS\n#coreml #stable-diffusion #text-to-image #license-creativeml-openrail-m #region-us \n# Core ML Converted Model:\n\n - This model was converted to Core ML for use on Apple Silicon devices. Conversion instructions can be found here.\n - Provide the model to an app such as Mochi Diffusion Github / Discord to generate images.\n - 'split_einsum' version is compatible with all compute unit options including Neural Engine.\n - 'original' version is only compatible with 'CPU & GPU' option.\n - Different resolution versions are tagged accordingly.\n\n<br># SSD-1B-ZachsEmoji-v1_8bit:\nSource: This trained model is unique to this repo!<br>## SSD-1B Zach's Emoji v1.0"
] | [
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null | null | null | https://civitai.com/models/190396/cliffheart-arknights | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/cliffheart_arknights | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T05:13:07+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | null | https://civitai.com/models/190420/blemishine-arknights | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/blemishine_arknights | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T05:13:39+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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# Model Card for Model ID
## Model Details
### Model Description
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
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## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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null | null | null | ComfyUI
安装
1 安装 Python 3.10.6 与 pip
访问Python3.10.6下载页面，把页面拉到底，找到【Windows installer (64-bit)】点击下载
安装是注意，到这一步，需要如下图这样勾选 Add Python to PATH
2 安装 Git
访问Git下载页
点击【Download for Windows】，【64-bit Git for Windows Setup】点击下载
一路下一步安装
命令行运行git --version,返回git version 2.XX.0.windows.1就是安装成功了。
3 安装 CUDA
命令行运行nvidia-smi,看下自己显卡支持的 CUDA版本
12.2 就是能下12.2.X的版本，要再高就升级显卡驱动
还要cudnn
4 Pytorch安装
打开pytorch官网找到最新版本，复制到命令行安装。
5 ComfyUI安装
找一个空间充足的目录，在资源管理器，地址栏里敲CMD，敲回车，启动命令提示行窗口，输入以下命令
Git clone https://github.com/comfyanonymous/ComfyUI.git
启动前准备(为了减少启动时报错，先提前做好下面准备)
6 安装插件
6.1 ComfyUI Manager插件
前往 *\ComfyUI\custom_nodes 目录在文件路径处输入cmd 按下回车会打开cmd命令行
通过该命令安装：git clone https://github.com/ltdrdata/ComfyUI-Manager.git 下图表示安装成功，重启ComfyUI。
6.2 ComfyUI汉化插件AIGODLIKE-ComfyUI-Translation
6.3 ComfyUI Efficiency插件减少节点
6.4 ComfyUI-Custom-Scripts 插件将工作流导成图片
操作：
1 从虚拟环境打开
打开 E:\ComfyUI cmd
已经安装了Stable diffuision并使用自己的 python venv，则可以使用该 venv 来运行 ComfyUI。您可以打开自己喜欢的终端并激活它：
使用 Powershell："path_to_other_sd_gui\venv\Scripts\Activate.ps1"
使用 cmd.exe："path_to_other_sd_gui\venv\Scripts\activate.bat"
打开python main.py
2 按住空格拖动画布
提示词
1括号和权重
括号包括（）{} []三类，具体作用
1.1（），格式（prompt:X）点中提示词后按快捷键ctrl+down/up，可对单个词增加权重比例，权重值每次+/-0.1倍，超过3后看不出想要的效果
1.2 [ ] ，无快捷键，权重值-0.75
1.3{ }，无快捷键，权重值+0.5
2 [ ]的控制能力
格式[prompt:X],
“：” 代表先等着，到达X才开始 ::代表开始时先执行，到达X后结束
“ X ” 按X控制迭代步数， 1以内按照迭代步X倍数控制，超过1指的是按步数控制
2.1 控制生效时间
[red: 0.7]指的是按照20步迭代计算，到达14步后（0.7X20步）才开始跑这个词
[red::0.7]指的是按照20步迭代计算，前14步（0.7X20步）跑这个词，14步后停止
想要花朵点缀石头，[stones:flowers:0.7] 70%阶段stones生效，然后30% flowers生效
2.2 交替控制，[red|blue] hair 交替采样，红蓝相间的头发
3提示词推荐格式
3.1 起手词画质和画风
画质词 [masterpiece:1.2],best quality,highres,extremely detail CG,perfect lighting,8k wallpaper,
真实系：photograph,photorealistic
插画风:illustration,painting,paintbrush
二次元:anime,comic,game CG
三维场景：3D,C4D,render,unreal engine,octane render
画风词
Cyberpunk赛博朋克
8bit/16BIT PIXEL 像素风
Studio ghibi 宫崎骏风格
Pixel style 皮克斯
Chinese ink style 水墨画
4负向提示词
blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
组件
1 Clip文本编辑器
转化文本为输入：这样就能将prompt输入框转化为连接器，共用提示词
左侧为由文本拖动引出的带edittext的primitive组件
2 ConditioningSetArea
设置图像绘制区域还有所在位置和强度
把不同的提示词区域组合绘制
小技巧：
稳定扩散在生成分辨率接近 512x512 的方形图像时图像一致。但是，如果我们想生成纵横比为 16：9 的图像怎么办？ 让我们生成一个 16：9 的图像，其中包含一个坐着的主体。如果正常生成，成功率会很低，四肢在图像上不自然地伸展，以及其他一致性问题。
2 Controlnet
AnimateDiff
| {} | null | chenyp/comfyui | [
"region:us"
] | 2023-11-12T05:16:58+00:00 | [] | [] | TAGS
#region-us
| ComfyUI
安装
1 安装 Python 3.10.6 与 pip
访问Python3.10.6下载页面，把页面拉到底，找到【Windows installer (64-bit)】点击下载
安装是注意，到这一步，需要如下图这样勾选 Add Python to PATH
2 安装 Git
访问Git下载页
点击【Download for Windows】，【64-bit Git for Windows Setup】点击下载
一路下一步安装
命令行运行git --version,返回git version 2.XX.0.windows.1就是安装成功了。
3 安装 CUDA
命令行运行nvidia-smi,看下自己显卡支持的 CUDA版本
12.2 就是能下12.2.X的版本，要再高就升级显卡驱动
还要cudnn
4 Pytorch安装
打开pytorch官网找到最新版本，复制到命令行安装。
5 ComfyUI安装
找一个空间充足的目录，在资源管理器，地址栏里敲CMD，敲回车，启动命令提示行窗口，输入以下命令
Git clone URL
启动前准备(为了减少启动时报错，先提前做好下面准备)
6 安装插件
6.1 ComfyUI Manager插件
前往 *\ComfyUI\custom_nodes 目录在文件路径处输入cmd 按下回车会打开cmd命令行
通过该命令安装：git clone URL 下图表示安装成功，重启ComfyUI。
6.2 ComfyUI汉化插件AIGODLIKE-ComfyUI-Translation
6.3 ComfyUI Efficiency插件减少节点
6.4 ComfyUI-Custom-Scripts 插件将工作流导成图片
操作：
1 从虚拟环境打开
打开 E:\ComfyUI cmd
已经安装了Stable diffuision并使用自己的 python venv，则可以使用该 venv 来运行 ComfyUI。您可以打开自己喜欢的终端并激活它：
使用 Powershell："path_to_other_sd_gui\venv\Scripts\Activate.ps1"
使用 URL："path_to_other_sd_gui\venv\Scripts\URL"
打开python URL
2 按住空格拖动画布
提示词
1括号和权重
括号包括（）{} []三类，具体作用
1.1（），格式（prompt:X）点中提示词后按快捷键ctrl+down/up，可对单个词增加权重比例，权重值每次+/-0.1倍，超过3后看不出想要的效果
1.2 [ ] ，无快捷键，权重值-0.75
1.3{ }，无快捷键，权重值+0.5
2 [ ]的控制能力
格式[prompt:X],
“：” 代表先等着，到达X才开始 ::代表开始时先执行，到达X后结束
“ X ” 按X控制迭代步数， 1以内按照迭代步X倍数控制，超过1指的是按步数控制
2.1 控制生效时间
[red: 0.7]指的是按照20步迭代计算，到达14步后（0.7X20步）才开始跑这个词
[red::0.7]指的是按照20步迭代计算，前14步（0.7X20步）跑这个词，14步后停止
想要花朵点缀石头，[stones:flowers:0.7] 70%阶段stones生效，然后30% flowers生效
2.2 交替控制，[red|blue] hair 交替采样，红蓝相间的头发
3提示词推荐格式
3.1 起手词画质和画风
画质词 [masterpiece:1.2],best quality,highres,extremely detail CG,perfect lighting,8k wallpaper,
真实系：photograph,photorealistic
插画风:illustration,painting,paintbrush
二次元:anime,comic,game CG
三维场景：3D,C4D,render,unreal engine,octane render
画风词
Cyberpunk赛博朋克
8bit/16BIT PIXEL 像素风
Studio ghibi 宫崎骏风格
Pixel style 皮克斯
Chinese ink style 水墨画
4负向提示词
blur, haze, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers, deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
组件
1 Clip文本编辑器
转化文本为输入：这样就能将prompt输入框转化为连接器，共用提示词
左侧为由文本拖动引出的带edittext的primitive组件
2 ConditioningSetArea
设置图像绘制区域还有所在位置和强度
把不同的提示词区域组合绘制
小技巧：
稳定扩散在生成分辨率接近 512x512 的方形图像时图像一致。但是，如果我们想生成纵横比为 16：9 的图像怎么办？ 让我们生成一个 16：9 的图像，其中包含一个坐着的主体。如果正常生成，成功率会很低，四肢在图像上不自然地伸展，以及其他一致性问题。
2 Controlnet
AnimateDiff
| [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
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null | null | null | https://civitai.com/models/190393/scathach-fategrand-order | {"license": "creativeml-openrail-m"} | null | LarryAIDraw/scathach_fgo | [
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-12T05:18:01+00:00 | [] | [] | TAGS
#license-creativeml-openrail-m #region-us
| URL | [] | [
"TAGS\n#license-creativeml-openrail-m #region-us \n"
] | [
18
] | [
"passage: TAGS\n#license-creativeml-openrail-m #region-us \n"
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] |
null | null | transformers |
# PhoGPT: Generative Pre-training for Vietnamese
We release a state-of-the-art 7.5B-parameter generative model series named PhoGPT for Vietnamese, which includes the base pre-trained monolingual model **PhoGPT-7B5** and its instruction-following variant **PhoGPT-7B5-Instruct**. For further information or requests, please go to [PhoGPT's homepage](https://github.com/VinAIResearch/PhoGPT)!
| {"pretty_name": "PhoGPT", "extra_gated_prompt": "Please read the [PhoGPT-7B5 License Agreement](https://github.com/VinAIResearch/PhoGPT/blob/PhoGPT-7B5/LICENSE) before accepting it.", "extra_gated_fields": {"Name": "text", "Email": "text", "Affiliation": "text", "Country": "text", "I accept the PhoGPT License Agreement": "checkbox"}} | null | vinai/PhoGPT-7B5-Instruct | [
"transformers",
"pytorch",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-12T05:20:27+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #has_space #region-us
|
# PhoGPT: Generative Pre-training for Vietnamese
We release a state-of-the-art 7.5B-parameter generative model series named PhoGPT for Vietnamese, which includes the base pre-trained monolingual model PhoGPT-7B5 and its instruction-following variant PhoGPT-7B5-Instruct. For further information or requests, please go to PhoGPT's homepage!
| [
"# PhoGPT: Generative Pre-training for Vietnamese \n\nWe release a state-of-the-art 7.5B-parameter generative model series named PhoGPT for Vietnamese, which includes the base pre-trained monolingual model PhoGPT-7B5 and its instruction-following variant PhoGPT-7B5-Instruct. For further information or requests, please go to PhoGPT's homepage!"
] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #has_space #region-us \n",
"# PhoGPT: Generative Pre-training for Vietnamese \n\nWe release a state-of-the-art 7.5B-parameter generative model series named PhoGPT for Vietnamese, which includes the base pre-trained monolingual model PhoGPT-7B5 and its instruction-following variant PhoGPT-7B5-Instruct. For further information or requests, please go to PhoGPT's homepage!"
] | [
25,
94
] | [
"passage: TAGS\n#transformers #pytorch #endpoints_compatible #has_space #region-us \n# PhoGPT: Generative Pre-training for Vietnamese \n\nWe release a state-of-the-art 7.5B-parameter generative model series named PhoGPT for Vietnamese, which includes the base pre-trained monolingual model PhoGPT-7B5 and its instruction-following variant PhoGPT-7B5-Instruct. For further information or requests, please go to PhoGPT's homepage!"
] | [
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null | null | transformers | # GeneZC/MiniMA-3B-GGUF
Quantized GGUF model files for [MiniMA-3B](https://huggingface.co/GeneZC/MiniMA-3B) from [GeneZC](https://huggingface.co/GeneZC)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [minima-3b.q2_k.gguf](https://huggingface.co/afrideva/MiniMA-3B-GGUF/resolve/main/minima-3b.q2_k.gguf) | q2_k | 1.30 GB |
| [minima-3b.q3_k_m.gguf](https://huggingface.co/afrideva/MiniMA-3B-GGUF/resolve/main/minima-3b.q3_k_m.gguf) | q3_k_m | 1.51 GB |
| [minima-3b.q4_k_m.gguf](https://huggingface.co/afrideva/MiniMA-3B-GGUF/resolve/main/minima-3b.q4_k_m.gguf) | q4_k_m | 1.85 GB |
| [minima-3b.q5_k_m.gguf](https://huggingface.co/afrideva/MiniMA-3B-GGUF/resolve/main/minima-3b.q5_k_m.gguf) | q5_k_m | 2.15 GB |
| [minima-3b.q6_k.gguf](https://huggingface.co/afrideva/MiniMA-3B-GGUF/resolve/main/minima-3b.q6_k.gguf) | q6_k | 2.48 GB |
| [minima-3b.q8_0.gguf](https://huggingface.co/afrideva/MiniMA-3B-GGUF/resolve/main/minima-3b.q8_0.gguf) | q8_0 | 3.21 GB |
## Original Model Card:
## MiniMA-3B
📑 [arXiv]() | 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) | 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) | 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) | 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B)
❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2.
A language model distilled from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models".
Establishing a new compute-performance pareto frontier.
<img src="./teaser_a.jpg" alt="teaser_a" width="700" />
The following is an example code snippet to use MiniMA-3B:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# MiniMA
tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniMA-3B", use_fast=False)
# GPU.
model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniMA-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
# CPU.
# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniMA-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval()
prompt = "Question: Sherrie tells the truth. Vernell says Sherrie tells the truth. Alexis says Vernell lies. Michaela says Alexis tells the truth. Elanor says Michaela tells the truth. Does Elanor tell the truth?\nAnswer: No\n\nQuestion: Kristian lies. Sherrie says Kristian lies. Delbert says Sherrie lies. Jerry says Delbert tells the truth. Shalonda says Jerry tells the truth. Does Shalonda tell the truth?\nAnswer: No\n\nQuestion: Vina tells the truth. Helene says Vina lies. Kandi says Helene tells the truth. Jamey says Kandi lies. Ka says Jamey lies. Does Ka tell the truth?\nAnswer: No\n\nQuestion: Christie tells the truth. Ka says Christie tells the truth. Delbert says Ka lies. Leda says Delbert tells the truth. Lorine says Leda tells the truth. Does Lorine tell the truth?\nAnswer:"
input_ids = tokenizer([prompt]).input_ids
output_ids = model.generate(
torch.as_tensor(input_ids).cuda(),
do_sample=True,
temperature=0.7,
max_new_tokens=1024,
)
output_ids = output_ids[0][len(input_ids[0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
# output: "No"
```
## Bibtex
```bibtex
@article{zhang2023law,
title={Towards the Law of Capacity Gap in Distilling Language Models},
author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan},
year={2023},
url={}
}
``` | {"language": ["en", "zh"], "license": "apache-2.0", "library_name": "transformers", "tags": ["gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0"], "datasets": ["EleutherAI/pile", "togethercomputer/RedPajama-Data-1T", "p208p2002/wudao"], "model_name": "MiniMA-3B", "base_model": "GeneZC/MiniMA-3B", "inference": false, "model_creator": "GeneZC", "pipeline_tag": "text-generation", "quantized_by": "afrideva"} | text-generation | afrideva/MiniMA-3B-GGUF | [
"transformers",
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"en",
"zh",
"dataset:EleutherAI/pile",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:p208p2002/wudao",
"base_model:GeneZC/MiniMA-3B",
"license:apache-2.0",
"region:us"
] | 2023-11-12T05:22:55+00:00 | [] | [
"en",
"zh"
] | TAGS
#transformers #gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #en #zh #dataset-EleutherAI/pile #dataset-togethercomputer/RedPajama-Data-1T #dataset-p208p2002/wudao #base_model-GeneZC/MiniMA-3B #license-apache-2.0 #region-us
| GeneZC/MiniMA-3B-GGUF
=====================
Quantized GGUF model files for MiniMA-3B from GeneZC
Name: minima-3b.q2\_k.gguf, Quant method: q2\_k, Size: 1.30 GB
Name: minima-3b.q3\_k\_m.gguf, Quant method: q3\_k\_m, Size: 1.51 GB
Name: minima-3b.q4\_k\_m.gguf, Quant method: q4\_k\_m, Size: 1.85 GB
Name: minima-3b.q5\_k\_m.gguf, Quant method: q5\_k\_m, Size: 2.15 GB
Name: minima-3b.q6\_k.gguf, Quant method: q6\_k, Size: 2.48 GB
Name: minima-3b.q8\_0.gguf, Quant method: q8\_0, Size: 3.21 GB
Original Model Card:
--------------------
MiniMA-3B
---------
arXiv | HuggingFace-MiniMA | HuggingFace-MiniChat | ModelScope-MiniMA | ModelScope-MiniChat
Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2.
A language model distilled from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models".
Establishing a new compute-performance pareto frontier.
The following is an example code snippet to use MiniMA-3B:
Bibtex
------
| [] | [
"TAGS\n#transformers #gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #en #zh #dataset-EleutherAI/pile #dataset-togethercomputer/RedPajama-Data-1T #dataset-p208p2002/wudao #base_model-GeneZC/MiniMA-3B #license-apache-2.0 #region-us \n"
] | [
121
] | [
"passage: TAGS\n#transformers #gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #en #zh #dataset-EleutherAI/pile #dataset-togethercomputer/RedPajama-Data-1T #dataset-p208p2002/wudao #base_model-GeneZC/MiniMA-3B #license-apache-2.0 #region-us \n"
] | [
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null | null | 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. -->
# vishnuKC/bert-finetune-spec5g
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7938
- Validation Loss: 2.7631
- 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: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -960, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.7938 | 2.7631 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-large-uncased", "model-index": [{"name": "vishnuKC/bert-finetune-spec5g", "results": []}]} | fill-mask | vishnuKC/bert-finetune-spec5g | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"base_model:bert-large-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T05:24:39+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #base_model-bert-large-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| vishnuKC/bert-finetune-spec5g
=============================
This model is a fine-tuned version of bert-large-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.7938
* Validation Loss: 2.7631
* 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: {'inner\_optimizer': {'module': 'transformers.optimization\_tf', 'class\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'transformers.optimization\_tf', 'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_schedule\_fn': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': -960, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'warmup\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\_name': 'WarmUp'}, 'decay': 0.0, 'beta\_1': 0.8999999761581421, 'beta\_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}, 'registered\_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial\_scale': 32768.0, 'dynamic\_growth\_steps': 2000}
* training\_precision: mixed\_float16
### Training results
### Framework versions
* Transformers 4.35.0
* TensorFlow 2.14.0
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': -960, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'decay': 0.0, 'beta\\_1': 0.8999999761581421, 'beta\\_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}, 'registered\\_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial\\_scale': 32768.0, 'dynamic\\_growth\\_steps': 2000}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #base_model-bert-large-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'inner\\_optimizer': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'transformers.optimization\\_tf', 'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': -960, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'warmup\\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\\_name': 'WarmUp'}, 'decay': 0.0, 'beta\\_1': 0.8999999761581421, 'beta\\_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}, 'registered\\_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial\\_scale': 32768.0, 'dynamic\\_growth\\_steps': 2000}\n* training\\_precision: mixed\\_float16",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* TensorFlow 2.14.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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] |
null | null | diffusers |
# Textual inversion text2image fine-tuning - xxxhy/textual_inversion_canny-10000
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
| {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "textual_inversion"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true} | text-to-image | xxxhy/textual_inversion_canny-10000 | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-12T05:26:39+00:00 | [] | [] | TAGS
#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# Textual inversion text2image fine-tuning - xxxhy/textual_inversion_canny-10000
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
| [
"# Textual inversion text2image fine-tuning - xxxhy/textual_inversion_canny-10000\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
"TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# Textual inversion text2image fine-tuning - xxxhy/textual_inversion_canny-10000\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
101,
62
] | [
"passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# Textual inversion text2image fine-tuning - xxxhy/textual_inversion_canny-10000\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following."
] | [
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null | 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. -->
# wav2vec2-large-tamil-native
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: nan
- eval_wer: 1.0
- eval_runtime: 58.2412
- eval_samples_per_second: 3.125
- eval_steps_per_second: 0.395
- epoch: 32.97
- step: 1500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0
- Pytorch 1.11.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-large-xlsr-53", "model-index": [{"name": "wav2vec2-large-tamil-native", "results": []}]} | automatic-speech-recognition | JairamKanna/wav2vec2-large-tamil-native | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-12T05:33:04+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-tamil-native
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: nan
- eval_wer: 1.0
- eval_runtime: 58.2412
- eval_samples_per_second: 3.125
- eval_steps_per_second: 0.395
- epoch: 32.97
- step: 1500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0
- Pytorch 1.11.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| [
"# wav2vec2-large-tamil-native\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: nan\n- eval_wer: 1.0\n- eval_runtime: 58.2412\n- eval_samples_per_second: 3.125\n- eval_steps_per_second: 0.395\n- epoch: 32.97\n- step: 1500",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 50\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 1.11.0\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-tamil-native\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: nan\n- eval_wer: 1.0\n- eval_runtime: 58.2412\n- eval_samples_per_second: 3.125\n- eval_steps_per_second: 0.395\n- epoch: 32.97\n- step: 1500",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 50\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 1.11.0\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
] | [
77,
119,
6,
12,
8,
3,
140,
30
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-large-xlsr-53 #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-large-tamil-native\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: nan\n- eval_wer: 1.0\n- eval_runtime: 58.2412\n- eval_samples_per_second: 3.125\n- eval_steps_per_second: 0.395\n- epoch: 32.97\n- step: 1500## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 50\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.35.0\n- Pytorch 1.11.0\n- Datasets 2.14.6\n- Tokenizers 0.14.1"
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null | null | peft |
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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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### 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. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
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## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
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### Model Sources [optional]
- Repository:
- Paper [optional]:
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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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Use the code below to get started with the model.
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### Training Data
### Training Procedure
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
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### Results
#### Summary
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APA:
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## Training procedure
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### Framework versions
- PEFT 0.6.2.dev0
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null | null | transformers |
## Usage:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("TinyPixel/stablelm-ft2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TinyPixel/stablelm-ft2", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
text = '''### System:
You are a helpful AI assistant.
### User:
Why is sky blue?
### Assistant:
'''
device = "cuda:0"
inputs = tokenizer(text, return_tensors="pt").to(device)
outputs = model.generate(**inputs,
max_new_tokens=512,
do_sample=True,
top_p=0.95,
temperature=0.7,
top_k=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
``` | {"datasets": ["totally-not-an-llm/EverythingLM-data-V3"]} | text-generation | TinyPixel/stablelm-ft2 | [
"transformers",
"safetensors",
"stablelm_epoch",
"text-generation",
"custom_code",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"autotrain_compatible",
"region:us"
] | 2023-11-12T06:00:24+00:00 | [] | [] | TAGS
#transformers #safetensors #stablelm_epoch #text-generation #custom_code #dataset-totally-not-an-llm/EverythingLM-data-V3 #autotrain_compatible #region-us
|
## Usage:
| [
"## Usage:"
] | [
"TAGS\n#transformers #safetensors #stablelm_epoch #text-generation #custom_code #dataset-totally-not-an-llm/EverythingLM-data-V3 #autotrain_compatible #region-us \n",
"## Usage:"
] | [
61,
4
] | [
"passage: TAGS\n#transformers #safetensors #stablelm_epoch #text-generation #custom_code #dataset-totally-not-an-llm/EverythingLM-data-V3 #autotrain_compatible #region-us \n## Usage:"
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null | null | null |
This is Roll's voice from Megaman.EXE This was created with RVC V2, by Rejekts, trained on 300 epochs. If you would like to use the model, go here:
https://huggingface.co/PhoenixStormJr/RVC-V2-easy-gui-tutorial
Unfortunately, Roll doesn't talk much. Therefore, I had to use Tortoise-TTS, to generate extra speech for Roll. I am NOT going to sit through hours of footage searching for ONE characters voice. If you would like a better model, you go on ahead, and cut the sections of Roll's voice using audacity, or any other audio editing software, and upload the .wav file here. I will remove the background noise, enhance the audio, and train the model all myself, but I will NOT spend hours looking for Roll's voice. I need at least 6 minutes of Roll's speech to train the model.
Download Zip model here:
https://huggingface.co/PhoenixStormJr/Megaman-Roll-EXE-RVC/resolve/main/RollEXE.zip?download=true
Download .pth file here:
https://huggingface.co/PhoenixStormJr/Megaman-Roll-EXE-RVC/resolve/main/RollEXE.pth?download=true
Download .index here:
https://huggingface.co/PhoenixStormJr/Megaman-Roll-EXE-RVC/resolve/main/added_IVF479_Flat_nprobe_1_RollEXE_v2.index?download=true
Listen to a sample audio here:
<audio controls src="https://huggingface.co/PhoenixStormJr/Megaman-Roll-EXE-RVC/resolve/main/RollEXESample.wav"></audio> | {"license": "mit"} | null | PhoenixStormJr/Megaman-Roll-EXE-RVC | [
"license:mit",
"region:us"
] | 2023-11-12T06:02:41+00:00 | [] | [] | TAGS
#license-mit #region-us
|
!image/png
This is Roll's voice from Megaman.EXE This was created with RVC V2, by Rejekts, trained on 300 epochs. If you would like to use the model, go here:
URL
Unfortunately, Roll doesn't talk much. Therefore, I had to use Tortoise-TTS, to generate extra speech for Roll. I am NOT going to sit through hours of footage searching for ONE characters voice. If you would like a better model, you go on ahead, and cut the sections of Roll's voice using audacity, or any other audio editing software, and upload the .wav file here. I will remove the background noise, enhance the audio, and train the model all myself, but I will NOT spend hours looking for Roll's voice. I need at least 6 minutes of Roll's speech to train the model.
Download Zip model here:
URL
Download .pth file here:
URL
Download .index here:
URL
Listen to a sample audio here:
<audio controls src="URL | [] | [
"TAGS\n#license-mit #region-us \n"
] | [
11
] | [
"passage: TAGS\n#license-mit #region-us \n"
] | [
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null | null | transformers | Made by finetuning [ai4bharat/IndicBART-XXEN](https://huggingface.co/ai4bharat/IndicBART-XXEN). | {} | text2text-generation | aboli-marathe/indicbart_finetuned | [
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T06:13:00+00:00 | [] | [] | TAGS
#transformers #safetensors #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| Made by finetuning ai4bharat/IndicBART-XXEN. | [] | [
"TAGS\n#transformers #safetensors #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
40
] | [
"passage: TAGS\n#transformers #safetensors #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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null | 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. -->
# Whisper Small Persian
This model is a fine-tuned version of [makhataei/Whisper-Small-Common-Voice](https://huggingface.co/makhataei/Whisper-Small-Common-Voice) on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7215
- Wer: 43.6054
## 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-07
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0006 | 0.16 | 100 | 0.7099 | 42.5759 |
| 0.0016 | 0.31 | 200 | 0.7139 | 42.6710 |
| 0.0051 | 0.47 | 300 | 0.7114 | 42.6803 |
| 0.004 | 0.62 | 400 | 0.7109 | 42.6130 |
| 0.0047 | 0.78 | 500 | 0.7101 | 42.3139 |
| 0.0029 | 0.93 | 600 | 0.7105 | 42.4113 |
| 0.0016 | 1.09 | 700 | 0.7101 | 42.4067 |
| 0.0016 | 1.25 | 800 | 0.7129 | 42.3997 |
| 0.0028 | 1.4 | 900 | 0.7145 | 42.3951 |
| 0.0019 | 1.56 | 1000 | 0.7135 | 42.3557 |
| 0.0019 | 1.71 | 1100 | 0.7132 | 42.3534 |
| 0.0021 | 1.87 | 1200 | 0.7128 | 42.4739 |
| 0.0023 | 2.03 | 1300 | 0.7144 | 42.9330 |
| 0.0021 | 2.18 | 1400 | 0.7151 | 42.8403 |
| 0.0019 | 2.34 | 1500 | 0.7158 | 42.8820 |
| 0.0014 | 2.49 | 1600 | 0.7166 | 42.9121 |
| 0.0262 | 2.65 | 1700 | 0.7171 | 42.9933 |
| 0.0022 | 2.8 | 1800 | 0.7183 | 42.9608 |
| 0.0012 | 2.96 | 1900 | 0.7185 | 43.3921 |
| 0.0018 | 3.12 | 2000 | 0.7187 | 43.3295 |
| 0.0015 | 3.27 | 2100 | 0.7191 | 43.3480 |
| 0.0013 | 3.43 | 2200 | 0.7198 | 43.3898 |
| 0.0019 | 3.58 | 2300 | 0.7200 | 43.4106 |
| 0.0017 | 3.74 | 2400 | 0.7206 | 43.4315 |
| 0.0015 | 3.9 | 2500 | 0.7209 | 43.4268 |
| 0.0016 | 4.05 | 2600 | 0.7210 | 43.6031 |
| 0.0267 | 4.21 | 2700 | 0.7212 | 43.5891 |
| 0.001 | 4.36 | 2800 | 0.7213 | 43.5938 |
| 0.0015 | 4.52 | 2900 | 0.7215 | 43.6031 |
| 0.0019 | 4.67 | 3000 | 0.7215 | 43.6054 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"language": ["fa"], "license": "apache-2.0", "tags": ["fa-asr", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_0"], "metrics": ["wer"], "base_model": "makhataei/Whisper-Small-Common-Voice", "model-index": [{"name": "Whisper Small Persian", "results": []}]} | automatic-speech-recognition | makhataei/Whisper-Small-Common-Voice | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"fa-asr",
"generated_from_trainer",
"fa",
"dataset:mozilla-foundation/common_voice_16_0",
"base_model:makhataei/Whisper-Small-Common-Voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-12T06:16:14+00:00 | [] | [
"fa"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #fa-asr #generated_from_trainer #fa #dataset-mozilla-foundation/common_voice_16_0 #base_model-makhataei/Whisper-Small-Common-Voice #license-apache-2.0 #endpoints_compatible #region-us
| Whisper Small Persian
=====================
This model is a fine-tuned version of makhataei/Whisper-Small-Common-Voice on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7215
* Wer: 43.6054
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-07
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 40
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 3000
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.0.1+cu117
* Datasets 2.15.0
* Tokenizers 0.15.0
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 3000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #fa-asr #generated_from_trainer #fa #dataset-mozilla-foundation/common_voice_16_0 #base_model-makhataei/Whisper-Small-Common-Voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 3000",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
] | [
102,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #fa-asr #generated_from_trainer #fa #dataset-mozilla-foundation/common_voice_16_0 #base_model-makhataei/Whisper-Small-Common-Voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 40\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 3000### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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 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. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
| {"library_name": "peft", "base_model": "beomi/polyglot-ko-12.8b-safetensors"} | null | bbokyeong/qlora-koalpaca-polyglot-12.8b-1000step-jkzzang | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | 2023-11-12T06:20:00+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-beomi/polyglot-ko-12.8b-safetensors #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
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null | 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. -->
# pythia-70m-sft-function-calling
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9858
## 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: 5
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2294 | 0.11 | 2000 | 1.2844 |
| 1.115 | 0.22 | 4000 | 1.1689 |
| 1.0368 | 0.33 | 6000 | 1.1194 |
| 1.0346 | 0.44 | 8000 | 1.0797 |
| 0.9951 | 0.55 | 10000 | 1.0522 |
| 0.9832 | 0.66 | 12000 | 1.0325 |
| 0.9931 | 0.77 | 14000 | 1.0093 |
| 0.9502 | 0.88 | 16000 | 0.9944 |
| 0.9649 | 0.99 | 18000 | 0.9858 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-70m", "model-index": [{"name": "pythia-70m-sft-function-calling", "results": []}]} | text-generation | borkh/pythia-70m-sft-function-calling | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T06:22:24+00:00 | [] | [] | TAGS
#transformers #safetensors #gpt_neox #text-generation #generated_from_trainer #base_model-EleutherAI/pythia-70m #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| pythia-70m-sft-function-calling
===============================
This model is a fine-tuned version of EleutherAI/pythia-70m on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9858
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: 5
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu121
* Datasets 2.14.6
* Tokenizers 0.14.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
"TAGS\n#transformers #safetensors #gpt_neox #text-generation #generated_from_trainer #base_model-EleutherAI/pythia-70m #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
] | [
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98,
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33
] | [
"passage: TAGS\n#transformers #safetensors #gpt_neox #text-generation #generated_from_trainer #base_model-EleutherAI/pythia-70m #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 5\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.14.1"
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null | 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. -->
# smolm-autoreg-bpe-babylm-no_aann-all-det-removal-1e-4
This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-no_aann-all-det-removal-1e-4/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-no_aann-all-det-removal-1e-4/config.json) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1691
- Accuracy: 0.4267
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.3751 | 1.0 | 9181 | 4.4450 | 0.2950 |
| 3.7256 | 2.0 | 18362 | 3.7974 | 0.3566 |
| 3.4254 | 3.0 | 27543 | 3.5373 | 0.3827 |
| 3.2619 | 4.0 | 36724 | 3.3986 | 0.3966 |
| 3.1544 | 5.0 | 45905 | 3.3188 | 0.4050 |
| 3.077 | 6.0 | 55086 | 3.2705 | 0.4104 |
| 3.0248 | 7.0 | 64267 | 3.2364 | 0.4145 |
| 2.9784 | 8.0 | 73448 | 3.2167 | 0.4174 |
| 2.9383 | 9.0 | 82629 | 3.1965 | 0.4195 |
| 2.9091 | 10.0 | 91810 | 3.1853 | 0.4214 |
| 2.8791 | 11.0 | 100991 | 3.1802 | 0.4226 |
| 2.848 | 12.0 | 110172 | 3.1747 | 0.4234 |
| 2.8335 | 13.0 | 119353 | 3.1727 | 0.4242 |
| 2.806 | 14.0 | 128534 | 3.1694 | 0.4248 |
| 2.7833 | 15.0 | 137715 | 3.1709 | 0.4250 |
| 2.7651 | 16.0 | 146896 | 3.1659 | 0.4257 |
| 2.7501 | 17.0 | 156077 | 3.1652 | 0.4261 |
| 2.7293 | 18.0 | 165258 | 3.1653 | 0.4265 |
| 2.7132 | 19.0 | 174439 | 3.1677 | 0.4265 |
| 2.6994 | 20.0 | 183620 | 3.1691 | 0.4267 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "models/smolm-autoreg-bpe-babylm-no_aann-all-det-removal-1e-4/config.json", "model-index": [{"name": "smolm-autoreg-bpe-babylm-no_aann-all-det-removal-1e-4", "results": []}]} | text-generation | kanishka/smolm-autoreg-bpe-babylm-no_aann-all-det-removal-1e-4 | [
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-12T06:22:42+00:00 | [] | [] | TAGS
#transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| smolm-autoreg-bpe-babylm-no\_aann-all-det-removal-1e-4
======================================================
This model is a fine-tuned version of models/smolm-autoreg-bpe-babylm-no\_aann-all-det-removal-1e-4/URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.1691
* Accuracy: 0.4267
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 64
* eval\_batch\_size: 256
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 32000
* num\_epochs: 20.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.0
* Pytorch 2.1.0+cu121
* Datasets 2.12.0
* Tokenizers 0.14.1
| [
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.12.0\n* Tokenizers 0.14.1"
] | [
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"passage: TAGS\n#transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 256\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.12.0\n* Tokenizers 0.14.1"
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null | null | null | # vihangd/smartyplats-1.1b-v1-GGUF
Quantized GGUF model files for [smartyplats-1.1b-v1](https://huggingface.co/vihangd/smartyplats-1.1b-v1) from [vihangd](https://huggingface.co/vihangd)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [smartyplats-1.1b-v1.q2_k.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q2_k.gguf) | q2_k | 482.14 MB |
| [smartyplats-1.1b-v1.q3_k_m.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q3_k_m.gguf) | q3_k_m | 549.85 MB |
| [smartyplats-1.1b-v1.q4_k_m.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q4_k_m.gguf) | q4_k_m | 667.81 MB |
| [smartyplats-1.1b-v1.q5_k_m.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q5_k_m.gguf) | q5_k_m | 782.04 MB |
| [smartyplats-1.1b-v1.q6_k.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q6_k.gguf) | q6_k | 903.41 MB |
| [smartyplats-1.1b-v1.q8_0.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q8_0.gguf) | q8_0 | 1.17 GB |
## Original Model Card:
<p><h1> SmartyPlats-1.1b V1 </h1></p>
An experimental finetune of TinyLLaMA 1T with QLoRA
<h2> Datasets </h2>
Trained on alpca style datasets
<p><h2> Prompt Template </h2></p>
Uses alpaca style prompt template | {"license": "apache-2.0", "tags": ["gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0"], "model_name": "smartyplats-1.1b-v1", "base_model": "vihangd/smartyplats-1.1b-v1", "inference": false, "model_creator": "vihangd", "pipeline_tag": "text-generation", "quantized_by": "afrideva"} | text-generation | afrideva/smartyplats-1.1b-v1-GGUF | [
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"base_model:vihangd/smartyplats-1.1b-v1",
"license:apache-2.0",
"region:us"
] | 2023-11-12T06:29:05+00:00 | [] | [] | TAGS
#gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #base_model-vihangd/smartyplats-1.1b-v1 #license-apache-2.0 #region-us
| vihangd/smartyplats-1.1b-v1-GGUF
================================
Quantized GGUF model files for smartyplats-1.1b-v1 from vihangd
Name: smartyplats-1.1b-v1.q2\_k.gguf, Quant method: q2\_k, Size: 482.14 MB
Name: smartyplats-1.1b-v1.q3\_k\_m.gguf, Quant method: q3\_k\_m, Size: 549.85 MB
Name: smartyplats-1.1b-v1.q4\_k\_m.gguf, Quant method: q4\_k\_m, Size: 667.81 MB
Name: smartyplats-1.1b-v1.q5\_k\_m.gguf, Quant method: q5\_k\_m, Size: 782.04 MB
Name: smartyplats-1.1b-v1.q6\_k.gguf, Quant method: q6\_k, Size: 903.41 MB
Name: smartyplats-1.1b-v1.q8\_0.gguf, Quant method: q8\_0, Size: 1.17 GB
Original Model Card:
--------------------
SmartyPlats-1.1b V1
====================
An experimental finetune of TinyLLaMA 1T with QLoRA
Datasets
---------
Trained on alpca style datasets
Prompt Template
----------------
Uses alpaca style prompt template | [] | [
"TAGS\n#gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #base_model-vihangd/smartyplats-1.1b-v1 #license-apache-2.0 #region-us \n"
] | [
82
] | [
"passage: TAGS\n#gguf #ggml #quantized #q2_k #q3_k_m #q4_k_m #q5_k_m #q6_k #q8_0 #text-generation #base_model-vihangd/smartyplats-1.1b-v1 #license-apache-2.0 #region-us \n"
] | [
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Subsets and Splits