nisten/qwen2.5-coder-7b-abliterated-128k-AWQ

quantized_by: nisten
pipeline_tag: text-generation
language:

• en
  license_link: https://huggingface.co/huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated/blob/main/LICENSE
  tags:
• chat
• abliterated
• uncensored
• AWQ
• 4bit
  base_model: huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated
  license: apache-2.0

Use this as a draft model, quant code provided, love you all.

4bit AWQ quant of model: https://huggingface.co/huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated

Code used to quantize it

from tqdm import tqdm
from datasets import load_dataset
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model_path = 'huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated'
quant_path = 'q7awqlocaldirname'
quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }

# Load model
model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

def load_openhermes_coding():
    data = load_dataset("alvarobartt/openhermes-preferences-coding", split="train")
    samples = []
    for sample in data:
        responses = [f'{response["role"]}: {response["content"]}' for response in sample["chosen"]]
        samples.append("\n".join(responses))

    return samples

# Quantize
model.quantize(
    tokenizer,
    quant_config=quant_config,
    calib_data=load_openhermes_coding(),
    # MODIFY these parameters if need be:
    # n_parallel_calib_samples=32,
    # max_calib_samples=128,
    # max_calib_seq_len=4096
)

# Save quantized model
model.save_quantized(quant_path)
tokenizer.save_pretrained(quant_path)

print(f'Model is quantized and saved at "{quant_path}"')