This model has been quantized using GPTQModel.
- bits: 4
- group_size: 128
- desc_act: true
- static_groups: false
- sym: true
- lm_head: false
- damp_percent: 0.0025
- true_sequential: true
- model_name_or_path: ""
- model_file_base_name: "model"
- quant_method: "gptq"
- checkpoint_format: "gptq"
- meta：
- quantizer: "gptqmodel:0.9.9-dev0"
Example:
from transformers import AutoTokenizer
from gptqmodel import GPTQModel
model_name = "ModelCloud/Meta-Llama-3.1-70B-Instruct-gptq-4bit"
prompt = [{"role": "user", "content": "I am in Shanghai, preparing to visit the natural history museum. Can you tell me the best way to"}]
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = GPTQModel.from_quantized(model_name)
input_tensor = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
lm-eval benchmark:
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|---------------------------------------|------:|------|-----:|----------|---|-----:|---|-----:|
|arc_challenge | 1|none | 0|acc |↑ |0.6186|± |0.0142|
| | |none | 0|acc_norm |↑ |0.6297|± |0.0141|
|arc_easy | 1|none | 0|acc |↑ |0.8628|± |0.0071|
| | |none | 0|acc_norm |↑ |0.8338|± |0.0076|
|boolq | 2|none | 0|acc |↑ |0.8761|± |0.0058|
|hellaswag | 1|none | 0|acc |↑ |0.6463|± |0.0048|
| | |none | 0|acc_norm |↑ |0.8389|± |0.0037|
|lambada_openai | 1|none | 0|acc |↑ |0.7561|± |0.0060|
| | |none | 0|perplexity|↓ |3.0311|± |0.0639|
|mmlu | 1|none | |acc |↑ |0.8100|± |0.0032|
| - humanities | 1|none | |acc |↑ |0.7981|± |0.0057|
| - formal_logic | 0|none | 0|acc |↑ |0.6349|± |0.0431|
| - high_school_european_history | 0|none | 0|acc |↑ |0.8545|± |0.0275|
| - high_school_us_history | 0|none | 0|acc |↑ |0.9412|± |0.0165|
| - high_school_world_history | 0|none | 0|acc |↑ |0.9198|± |0.0177|
| - international_law | 0|none | 0|acc |↑ |0.9008|± |0.0273|
| - jurisprudence | 0|none | 0|acc |↑ |0.8796|± |0.0315|
| - logical_fallacies | 0|none | 0|acc |↑ |0.8650|± |0.0268|
| - moral_disputes | 0|none | 0|acc |↑ |0.8266|± |0.0204|
| - moral_scenarios | 0|none | 0|acc |↑ |0.8559|± |0.0117|
| - philosophy | 0|none | 0|acc |↑ |0.8360|± |0.0210|
| - prehistory | 0|none | 0|acc |↑ |0.8827|± |0.0179|
| - professional_law | 0|none | 0|acc |↑ |0.6675|± |0.0120|
| - world_religions | 0|none | 0|acc |↑ |0.9181|± |0.0210|
| - other | 1|none | |acc |↑ |0.8304|± |0.0064|
| - business_ethics | 0|none | 0|acc |↑ |0.7900|± |0.0409|
| - clinical_knowledge | 0|none | 0|acc |↑ |0.8566|± |0.0216|
| - college_medicine | 0|none | 0|acc |↑ |0.7630|± |0.0324|
| - global_facts | 0|none | 0|acc |↑ |0.5800|± |0.0496|
| - human_aging | 0|none | 0|acc |↑ |0.8206|± |0.0257|
| - management | 0|none | 0|acc |↑ |0.8835|± |0.0318|
| - marketing | 0|none | 0|acc |↑ |0.9231|± |0.0175|
| - medical_genetics | 0|none | 0|acc |↑ |0.9400|± |0.0239|
| - miscellaneous | 0|none | 0|acc |↑ |0.9144|± |0.0100|
| - nutrition | 0|none | 0|acc |↑ |0.8660|± |0.0195|
| - professional_accounting | 0|none | 0|acc |↑ |0.6454|± |0.0285|
| - professional_medicine | 0|none | 0|acc |↑ |0.8971|± |0.0185|
| - virology | 0|none | 0|acc |↑ |0.5602|± |0.0386|
| - social sciences | 1|none | |acc |↑ |0.8736|± |0.0059|
| - econometrics | 0|none | 0|acc |↑ |0.7018|± |0.0430|
| - high_school_geography | 0|none | 0|acc |↑ |0.9242|± |0.0189|
| - high_school_government_and_politics| 0|none | 0|acc |↑ |0.9741|± |0.0115|
| - high_school_macroeconomics | 0|none | 0|acc |↑ |0.8410|± |0.0185|
| - high_school_microeconomics | 0|none | 0|acc |↑ |0.8992|± |0.0196|
| - high_school_psychology | 0|none | 0|acc |↑ |0.9229|± |0.0114|
| - human_sexuality | 0|none | 0|acc |↑ |0.8779|± |0.0287|
| - professional_psychology | 0|none | 0|acc |↑ |0.8497|± |0.0145|
| - public_relations | 0|none | 0|acc |↑ |0.7273|± |0.0427|
| - security_studies | 0|none | 0|acc |↑ |0.8163|± |0.0248|
| - sociology | 0|none | 0|acc |↑ |0.9154|± |0.0197|
| - us_foreign_policy | 0|none | 0|acc |↑ |0.9300|± |0.0256|
| - stem | 1|none | |acc |↑ |0.7456|± |0.0075|
| - abstract_algebra | 0|none | 0|acc |↑ |0.6300|± |0.0485|
| - anatomy | 0|none | 0|acc |↑ |0.7926|± |0.0350|
| - astronomy | 0|none | 0|acc |↑ |0.8947|± |0.0250|
| - college_biology | 0|none | 0|acc |↑ |0.9444|± |0.0192|
| - college_chemistry | 0|none | 0|acc |↑ |0.5800|± |0.0496|
| - college_computer_science | 0|none | 0|acc |↑ |0.6700|± |0.0473|
| - college_mathematics | 0|none | 0|acc |↑ |0.5400|± |0.0501|
| - college_physics | 0|none | 0|acc |↑ |0.6275|± |0.0481|
| - computer_security | 0|none | 0|acc |↑ |0.8200|± |0.0386|
| - conceptual_physics | 0|none | 0|acc |↑ |0.7830|± |0.0269|
| - electrical_engineering | 0|none | 0|acc |↑ |0.7862|± |0.0342|
| - elementary_mathematics | 0|none | 0|acc |↑ |0.7593|± |0.0220|
| - high_school_biology | 0|none | 0|acc |↑ |0.9194|± |0.0155|
| - high_school_chemistry | 0|none | 0|acc |↑ |0.7143|± |0.0318|
| - high_school_computer_science | 0|none | 0|acc |↑ |0.9200|± |0.0273|
| - high_school_mathematics | 0|none | 0|acc |↑ |0.5185|± |0.0305|
| - high_school_physics | 0|none | 0|acc |↑ |0.6556|± |0.0388|
| - high_school_statistics | 0|none | 0|acc |↑ |0.7361|± |0.0301|
| - machine_learning | 0|none | 0|acc |↑ |0.7054|± |0.0433|
|openbookqa | 1|none | 0|acc |↑ |0.3660|± |0.0216|
| | |none | 0|acc_norm |↑ |0.4620|± |0.0223|
|piqa | 1|none | 0|acc |↑ |0.8264|± |0.0088|
| | |none | 0|acc_norm |↑ |0.8319|± |0.0087|
|rte | 1|none | 0|acc |↑ |0.7184|± |0.0271|
|truthfulqa_mc1 | 2|none | 0|acc |↑ |0.3917|± |0.0171|
|winogrande | 1|none | 0|acc |↑ |0.7924|± |0.0114|
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 1|none | |acc |↑ |0.8100|± |0.0032|
| - humanities | 1|none | |acc |↑ |0.7981|± |0.0057|
| - other | 1|none | |acc |↑ |0.8304|± |0.0064|
| - social sciences| 1|none | |acc |↑ |0.8736|± |0.0059|
| - stem | 1|none | |acc |↑ |0.7456|± |0.0075|
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