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Llama-3-8B-Instruct-Coder-v…/README.md
ModelHub XC 5395e4a243 初始化项目,由ModelHub XC社区提供模型
Model: bartowski/Llama-3-8B-Instruct-Coder-v2-AWQ
Source: Original Platform
2026-06-14 14:46:13 +08:00

2.2 KiB

language, license, tags, base_model, quantized_by, pipeline_tag
language license tags base_model quantized_by pipeline_tag
en
apache-2.0
text-generation-inference
transformers
unsloth
llama
trl
sft
NousResearch/Meta-Llama-3-8B-Instruct bartowski text-generation

4-bit GEMM AWQ Quantizations of Llama-3-8B-Instruct-Coder-v2

Using AutoAWQ release v0.2.5 for quantization.

Original model: https://huggingface.co/rombodawg/Llama-3-8B-Instruct-Coder-v2

Prompt format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>


AWQ Parameters

  • q_group_size: 128
  • w_bit: 4
  • zero_point: True
  • version: GEMM

How to run

From the AutoAWQ repo here

First install autoawq pypi package:

pip install autoawq

Then run the following:

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer


quant_path = "models/Llama-3-8B-Instruct-Coder-v2-AWQ"

# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

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?"

chat = [
    {"role": "system", "content": "You are a concise assistant that helps answer questions."},
    {"role": "user", "content": prompt},
]

# <|eot_id|> used for llama 3 models
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

tokens = tokenizer.apply_chat_template(
    chat,
    return_tensors="pt"
).cuda()

# Generate output
generation_output = model.generate(
    tokens, 
    streamer=streamer,
    max_new_tokens=64,
    eos_token_id=terminators
)

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski