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4bit AWQ Quantized Version of parlance-labs/hc-mistral-alpaca-merged
This is how to use AutoAWQ to quantize the model.
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
# setup
quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }
quant_path="hc-mistral-alpaca-merged-awq"
model_path="parlance-labs/hc-mistral-alpaca-merged"
model = AutoAWQForCausalLM.from_pretrained(model_path, **{"low_cpu_mem_usage": True})
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# quantize and save model
model.quantize(tokenizer, quant_config=quant_config)
model.save_quantized(quant_path)
tokenizer.save_pretrained(quant_path)
After you save the model you can upload it to the hub
cd hc-mistral-alpaca-merged-awq
huggingface-cli upload parlance-labs/hc-mistral-alpaca-merged-awq .
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