README.md

---
base_model: karakuri-ai/karakuri-lm-70b-chat-v0.1
license: llama2
language:
- ja
pipeline_tag: text-generation
---

# CapyBaraHermes 2.5 Mistral 7B - GPTQ
- Model creator: [karakuri-ai](https://huggingface.co./karakuri-ai)
- Original model: [KARAKURI LM 70B Chat v0.1](https://huggingface.co./karakuri-ai/karakuri-lm-70b-chat-v0.1)

# Description

This repo contains AWQ model files for [KARAKURI LM 70B Chat v0.1](https://huggingface.co./karakuri-ai/karakuri-lm-70b-chat-v0.1).

## How to get the AWQ model

I created AWQ model files by using used autoawq==0.2.3.
```bash
pip install autoawq==0.2.3
```

This is the Python code to create AWQ model.
```bash
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_path = "karakuri-ai/karakuri-lm-70b-chat-v0.1"

quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }

# Load model
model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# Quantize
model.quantize(tokenizer, quant_config=quant_config, calib_data="mmnga/wikipedia-ja-20230720-1k")

quant_path = "karakuri-lm-70b-v0.1-AWQ"
model.save_quantized(quant_path)
tokenizer.save_pretrained(quant_path)
```

## Usage

```bash
from vllm import LLM, SamplingParams

sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
llm = LLM(model="masao1211/karakuri-lm-70b-chat-v0.1-AWQ", max_model_len=4096)

system_prompt = "System prompt"


messages = [{"role": "system", "content": "System prompt"}]
messages.append({"role": "user", "content": "User Prompt"})
prompt = llm.llm_engine.tokenizer.tokenizer.apply_chat_template(conversation=messages, add_generation_prompt=True, tokenize=False)
prompts = [prompt]

outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```