---
license: apache-2.0
language:
- zh
library_name: transformers
quantized_by: chienweichang
---

# Breeze-7B-Instruct-64k-v0_1-AWQ

- Model creator: [MediaTek Research](https://huggingface.co/MediaTek-Research)
- Original model: [Breeze-7B-Instruct-64k-v0.1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0.1)

## Description

This repo contains AWQ model files for MediaTek Research's [Breeze-7B-Instruct-64k-v0.1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0.1).

### 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.
- [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

* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/audreyt/Breeze-7B-Instruct-64k-v0.1-GGUF)
<!-- repositories-available 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 chienweichang/Breeze-7B-Instruct-64k-v0_1-AWQ \
    --quantization awq \
    --max-model-len 2048 \
    --dtype auto
```

- When using vLLM from Python code, again set `quantization=awq`.

For example:

```python
from vllm import LLM, SamplingParams
prompts = [
    "告訴我AI是什麼",
    "(291 - 150) 是多少?",
    "台灣最高的山是哪座?",
]
prompt_template='''[INST] {prompt} [/INST]
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="chienweichang/Breeze-7B-Instruct-64k-v0_1-AWQ", quantization="awq", dtype="half", max_model_len=2048)
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-python start -->
## Inference from Python code using Transformers

### Install the necessary packages

- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.37.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.8 or later.

```shell
pip3 install --upgrade "autoawq>=0.1.8" "transformers>=4.37.0"
```

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 4.37.0 and later)

```python
from transformers import AutoTokenizer, pipeline, TextStreamer, AutoModelForCausalLM

checkpoint = "chienweichang/Breeze-7B-Instruct-64k-v0_1-AWQ"
model: AutoModelForCausalLM = AutoModelForCausalLM.from_pretrained(
    checkpoint,
    device_map="auto",
    use_safetensors=True,
)
tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)

streamer = TextStreamer(tokenizer, skip_prompt=True)

# 創建一個用於文本生成的pipeline。
text_generation_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    use_cache=True,
    device_map="auto",
    max_length=32768,
    do_sample=True,
    top_k=5,
    num_return_sequences=1,
    streamer=streamer,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
)
# Inference is also possible via Transformers' pipeline
print("pipeline output: ", text_generation_pipeline.predict("請問台灣最高的山是?"))
```