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

• Model creator: MediaTek Research
• Original model: Breeze-7B-Instruct-64k-v0.1

Description

This repo contains AWQ model files for MediaTek Research's 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 - using Loader: AutoAWQ
• vLLM - version 0.2.2 or later for support for all model types.
• 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

• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference

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:

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:

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}")

Inference from Python code using Transformers

Install the necessary packages

• Requires: Transformers 4.37.0 or later.
• Requires: AutoAWQ 0.1.8 or later.

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

If you have problems installing AutoAWQ using the pre-built wheels, install it from source instead:

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)

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?"))