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