# Model Card for llama-7b-hf-28q_4bit-128g_WVU

## Model Description

`llama-7b-hf-28q_4bit-128g_WVU` is a model based on the 
Llama architecture with 7 billion parameters. 
This model adopts a quantization in which the first 28 layers 
of the decoder have been quantized with the [`gptq`](https://github.com/qwopqwop200/GPTQ-for-LLaMa) method, 
which uses 4-bit precision and 128 groups. 
Then, the last 4 decoder layers (1/8 of decoding layers), and lm_head have been fine-tuned using the [wizard_vicuna_70k_unfiltered dataset](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered), 1 epoch. 

## Note

Quantization effectively reduces memory usage, however, it may result in differences in the parameters. 
Additionally, fine-tuning only the last few layers lowers memory requirements for training but could lead to minor performance degradation.

Several alternatives exist for fine-tuning and quantizing the Llama models. The specific method utilized here—quantizing several layers, 
followed by fine-tuning the last few layers—is designed to account for errors introduced during quantization (which sometimes can result in unexpected answers), 
and enables the last few layers to be fine-tuned considering both the quantization error and the dataset.

It is worth mentioning that other methods may yield superior performance. For instance:
1. Fine-tuning the entire model for `X` epochs
2. Quantizing the first `K` layers
3. Fine-tuning the remaining layers for `Y` epochs

Nonetheless, as fine-tuning the entire model requires considerable resources (for example, 4 GPUs with 80GB VRAM is required for 7B LLaMa), 
this model omit the first step from the method described above, and it works.

## Using the Model

To load the model, a custom `LlamaForCausalLM` is required. 
You can find quantized llama [here](https://github.com/LearnItAnyway/quantized_llama).

## References

1. Meta - LLaMA
2. [WizardLM](https://github.com/nlpxucan/WizardLM)
3. [GPTQ for LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa)
4. [Wizard Vicuna Unfiltered Dataset](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
5. Various unlisted but great works, researches, and projects.

{"license": "other"}
transformers pytorch llama text-generation license:other autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# Model Card for llama-7b-hf-28q_4bit-128g_WVU

## Model Description

'llama-7b-hf-28q_4bit-128g_WVU' is a model based on the 
Llama architecture with 7 billion parameters. 
This model adopts a quantization in which the first 28 layers 
of the decoder have been quantized with the 'gptq' method, 
which uses 4-bit precision and 128 groups. 
Then, the last 4 decoder layers (1/8 of decoding layers), and lm_head have been fine-tuned using the wizard_vicuna_70k_unfiltered dataset, 1 epoch. 

## Note

Quantization effectively reduces memory usage, however, it may result in differences in the parameters. 
Additionally, fine-tuning only the last few layers lowers memory requirements for training but could lead to minor performance degradation.

Several alternatives exist for fine-tuning and quantizing the Llama models. The specific method utilized here—quantizing several layers, 
followed by fine-tuning the last few layers—is designed to account for errors introduced during quantization (which sometimes can result in unexpected answers), 
and enables the last few layers to be fine-tuned considering both the quantization error and the dataset.

It is worth mentioning that other methods may yield superior performance. For instance:
1. Fine-tuning the entire model for 'X' epochs
2. Quantizing the first 'K' layers
3. Fine-tuning the remaining layers for 'Y' epochs

Nonetheless, as fine-tuning the entire model requires considerable resources (for example, 4 GPUs with 80GB VRAM is required for 7B LLaMa), 
this model omit the first step from the method described above, and it works.

## Using the Model

To load the model, a custom 'LlamaForCausalLM' is required. 
You can find quantized llama here.

## References

1. Meta - LLaMA
2. WizardLM
3. GPTQ for LLaMa
4. Wizard Vicuna Unfiltered Dataset
5. Various unlisted but great works, researches, and projects.

# Model Card for llama-7b-hf-28q_4bit-128g_WVU ## Model Description

'llama-7b-hf-28q_4bit-128g_WVU' is a model based on the 
Llama architecture with 7 billion parameters. 
This model adopts a quantization in which the first 28 layers 
of the decoder have been quantized with the 'gptq' method, 
which uses 4-bit precision and 128 groups. 
Then, the last 4 decoder layers (1/8 of decoding layers), and lm_head have been fine-tuned using the wizard_vicuna_70k_unfiltered dataset, 1 epoch. ## Note

Quantization effectively reduces memory usage, however, it may result in differences in the parameters. 
Additionally, fine-tuning only the last few layers lowers memory requirements for training but could lead to minor performance degradation.

Several alternatives exist for fine-tuning and quantizing the Llama models. The specific method utilized here—quantizing several layers, 
followed by fine-tuning the last few layers—is designed to account for errors introduced during quantization (which sometimes can result in unexpected answers), 
and enables the last few layers to be fine-tuned considering both the quantization error and the dataset.

It is worth mentioning that other methods may yield superior performance. For instance:
1. Fine-tuning the entire model for 'X' epochs
2. Quantizing the first 'K' layers
3. Fine-tuning the remaining layers for 'Y' epochs

Nonetheless, as fine-tuning the entire model requires considerable resources (for example, 4 GPUs with 80GB VRAM is required for 7B LLaMa), 
this model omit the first step from the method described above, and it works. ## Using the Model

To load the model, a custom 'LlamaForCausalLM' is required. 
You can find quantized llama here. ## References

1. Meta - LLaMA
2. WizardLM
3. GPTQ for LLaMa
4. Wizard Vicuna Unfiltered Dataset
5. Various unlisted but great works, researches, and projects.
TAGS
#transformers #pytorch #llama #text-generation #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for llama-7b-hf-28q_4bit-128g_WVU ## Model Description

'llama-7b-hf-28q_4bit-128g_WVU' is a model based on the 
Llama architecture with 7 billion parameters. 
This model adopts a quantization in which the first 28 layers 
of the decoder have been quantized with the 'gptq' method, 
which uses 4-bit precision and 128 groups. 
Then, the last 4 decoder layers (1/8 of decoding layers), and lm_head have been fine-tuned using the wizard_vicuna_70k_unfiltered dataset, 1 epoch. ## Note

Quantization effectively reduces memory usage, however, it may result in differences in the parameters. 
Additionally, fine-tuning only the last few layers lowers memory requirements for training but could lead to minor performance degradation.

Several alternatives exist for fine-tuning and quantizing the Llama models. The specific method utilized here—quantizing several layers, 
followed by fine-tuning the last few layers—is designed to account for errors introduced during quantization (which sometimes can result in unexpected answers), 
and enables the last few layers to be fine-tuned considering both the quantization error and the dataset.

It is worth mentioning that other methods may yield superior performance. For instance:
1. Fine-tuning the entire model for 'X' epochs
2. Quantizing the first 'K' layers
3. Fine-tuning the remaining layers for 'Y' epochs

Nonetheless, as fine-tuning the entire model requires considerable resources (for example, 4 GPUs with 80GB VRAM is required for 7B LLaMa), 
this model omit the first step from the method described above, and it works. ## Using the Model

To load the model, a custom 'LlamaForCausalLM' is required. 
You can find quantized llama here. ## References

1. Meta - LLaMA
2. WizardLM
3. GPTQ for LLaMa
4. Wizard Vicuna Unfiltered Dataset
5. Various unlisted but great works, researches, and projects.