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Meta-Llama-3-8B-Instruct-awq-w4g64
yujiepan/Meta-Llama-3-8B-Instruct-awq-w4g64
Meta-Llama-3-8B-Instruct-awq-w4g64 is a text generation model from yujiepan. Use it when you need the model to write or continue text. It is set up for transformers.
This model applies AutoAWQ on meta-llama/Meta-Llama-3-8B-Instruct.
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Model card
From the Hugging Face model README
yujiepan/Meta-Llama-3-8B-Instruct-awq-w4g64
This model applies AutoAWQ on meta-llama/Meta-Llama-3-8B-Instruct.
- 4-bit asymmetric weight only quantization
- group_size=64
- calibration set: pileval
Accuracy
| model | precision | wikitext ppl (↓) |
|---|---|---|
| meta-llama/Meta-Llama-3-8B-Instruct | FP16 | 10.842 |
| yujiepan/Meta-Llama-3-8B-Instruct-awq-w4g64 | w4g64 | 10.943 |
Note:
- Evaluated on lm-evaluation-harness "wikitext" task
- Wikitext PPL does not guarantee actual accuracy, but helps to check the distortion after quantization.
Usage
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized('yujiepan/Meta-Llama-3-8B-awq-w4g64-Instruct')
Codes
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
model_path = "meta-llama/Meta-Llama-3-8B-Instruct"
quant_config = {"zero_point": True, "q_group_size": 64, "w_bit": 4, "version": "GEMM"}
model = AutoAWQForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model.quantize(tokenizer, quant_config=quant_config)
- Task
- Text Generation
- Library
- transformers
- Type
- llama
- Created
- Apr 21, 2024
- Updated
- Apr 21, 2024