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---
tags:
- finetuned
- quantized
- 4-bit
- AWQ
- transformers
- pytorch
- mistral
- text-generation
- conversational
- dataset:cognitivecomputations/samantha-data
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
base_model: cognitivecomputations/WestLake-7B-v2
license: apache-2.0
datasets:
- cognitivecomputations/samantha-data
library_name: transformers
model_creator: cognitivecomputations
model_name: Samantha 1.1 WestLake 7B - AWQ
model_type: mistral
pipeline_tag: text-generation
inference: false
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: Suparious
---
# Samantha 7B v1.1 laser - AWQ
- Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations)
- Original model: [WestLake 7B v2](https://huggingface.co/cognitivecomputations/WestLake-7B-v2)
- Fine Tuning: [cognitivecomputations](https://huggingface.co/cognitivecomputations/samantha-1.1-westlake-7b-laser)
It follows the implementation of [laserRMT](https://github.com/cognitivecomputations/laserRMT)
## Model description
This repo contains AWQ model files for [cognitivecomputations's Samantha 7B v1.1](https://huggingface.co/cognitivecomputations/samantha-1.1-westlake-7b-laser).
These files were quantised using hardware kindly provided by [SolidRusT Networks](https://solidrust.net/).
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/samantha-1.1-westlake-7b-laser-AWQ"
system_message = "Welcome to the Samantha AI. I am here to help you with any questions you may have."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [HF中国镜像站 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
## Prompt template: ChatML
```plaintext
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Also working with Basic Mistral format:
```plaintext
<|system|>
</s>
<|user|>
{prompt}</s>
<|assistant|>
```
|