This model is a int4 weight-only quantization with group_size 128 and symmetric quantization of llmfan46/Nex-N2-mini-ultra-uncensored-heretic generated by TUNING. Please follow the license of the original model.

Quantization Details

Attribute	Value
Base Model	llmfan46/Nex-N2-mini-ultra-uncensored-heretic
Quantization Tool	TUNING
Quantization Scheme	W4A16
Quantized Size	19504 MB

Evaluation Results

Task	Accuracy
hellaswag	0.6289
mmlu	0.8168
mmlu_abstract_algebra	0.7000
mmlu_anatomy	0.8519
mmlu_astronomy	0.9276
mmlu_business_ethics	0.8300
mmlu_clinical_knowledge	0.8906
mmlu_college_biology	0.9444
mmlu_college_chemistry	0.6600
mmlu_college_computer_science	0.7700
mmlu_college_mathematics	0.6800
mmlu_college_medicine	0.8092
mmlu_college_physics	0.7255
mmlu_computer_security	0.8400
mmlu_conceptual_physics	0.9489
mmlu_econometrics	0.7719
mmlu_electrical_engineering	0.8276
mmlu_elementary_mathematics	0.8095
mmlu_formal_logic	0.7222
mmlu_global_facts	0.6400
mmlu_high_school_biology	0.9516
mmlu_high_school_chemistry	0.8227
mmlu_high_school_computer_science	0.9300
mmlu_high_school_european_history	0.8727
mmlu_high_school_geography	0.9343
mmlu_high_school_government_and_politics	0.9793
mmlu_high_school_macroeconomics	0.8923
mmlu_high_school_mathematics	0.5778
mmlu_high_school_microeconomics	0.9580
mmlu_high_school_physics	0.7748
mmlu_high_school_psychology	0.9560
mmlu_high_school_statistics	0.8287
mmlu_high_school_us_history	0.9363
mmlu_high_school_world_history	0.9325
mmlu_human_aging	0.8161
mmlu_human_sexuality	0.8626
mmlu_humanities	0.7358
mmlu_international_law	0.9091
mmlu_jurisprudence	0.8981
mmlu_logical_fallacies	0.8712
mmlu_machine_learning	0.8036
mmlu_management	0.9029
mmlu_marketing	0.9444
mmlu_medical_genetics	0.9500
mmlu_miscellaneous	0.9413
mmlu_moral_disputes	0.8324
mmlu_moral_scenarios	0.5307
mmlu_nutrition	0.8791
mmlu_other	0.8584
mmlu_philosophy	0.8875
mmlu_prehistory	0.9074
mmlu_professional_accounting	0.7163
mmlu_professional_law	0.6375
mmlu_professional_medicine	0.9191
mmlu_professional_psychology	0.8791
mmlu_public_relations	0.7636
mmlu_security_studies	0.7959
mmlu_social_sciences	0.8983
mmlu_sociology	0.9005
mmlu_stem	0.8173
mmlu_us_foreign_policy	0.9400
mmlu_virology	0.5723
mmlu_world_religions	0.9064
piqa	0.8085

How to Use

HF Usage

Step 1: Install AutoRound

pip install auto-round

Step 2: Load and run the quantized model

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Nex-N2-mini-ultra-uncensored-heretic-AutoRound-W4A16-Tuning"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

# prepare the model input
prompt = "Write a quick sort algorithm."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(**model_inputs, max_new_tokens=512)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :].tolist()

content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)

VLLM Usage

bash

vllm serve Nex-N2-mini-ultra-uncensored-heretic-AutoRound-W4A16-Tuning \
    --trust-remote-code \
    --dtype bfloat16 \
    --tensor_parallel_size 1

If you encounter any issues, feel free to open an issue on the AutoRound GitHub repo or provide feedback on the Low-Bit Open LLM Leaderboard.

Ethical Considerations and Limitations

The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing.

Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software:

Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

Cite

@article{cheng2023optimize,
  title={Optimize weight rounding via signed gradient descent for the quantization of llms},
  author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi},
  journal={arXiv preprint arXiv:2309.05516},
  year={2023}
}

arxiv github

This model is part of the Intel Low-Bit Open LLM Leaderboard initiative.

LeaderboardModel1/Nex-N2-mini-ultra-uncensored-heretic-AutoRound-W4A16-Tuning

Nex-N2-mini-ultra-uncensored-heretic-AutoRound-W4A16-Tuning

Model Details