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dropbox-dash/
Qwen2.5-7B-Instruct_gemlite-ao_a16w4_gs_128_pack_32bit

Name: Qwen2.5-7B-Instruct_gemlite-ao_a16w4_gs_128_pack_32bit
Availability: InStock

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This is an HQQ all 4-bit (group-size=128) quantized Qwen2.5-7B-Instruct model, via TorchAO and GemLite as a backend.

Usage

First, install the dependecies:

pip install torchao;
pip install git+https://github.com/mobiusml/gemlite.git;

Then you can use the sample code below:

Python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig 

model_id = "mobiuslabsgmbh/Qwen2.5-7B-Instruct_gemlite-ao_a16w4_gs_128_pack_32bit"
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    device_map='cuda', 
)

tokenizer = AutoTokenizer.from_pretrained(model_id)

Use in vLLM:

Python

from vllm import LLM
from vllm.sampling_params import SamplingParams

model_id = "mobiuslabsgmbh/Qwen2.5-7B-Instruct_gemlite-ao_a16w4_gs_128_pack_32bit"

llm = LLM(model=model_id, max_model_len=4096)
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=1024)
outputs = llm.generate(["What is the capital of Germany?"], sampling_params)
print(outputs[0].outputs[0].text)

dropbox-dash/Qwen2.5-7B-Instruct_gemlite-ao_a16w4_gs_128_pack_32bit

Usage

dropbox-dash/
Qwen2.5-7B-Instruct_gemlite-ao_a16w4_gs_128_pack_32bit