language, library_name, license, license_link, pipeline_tag, tags, widget, base_model

language	library_name	license	license_link	pipeline_tag	tags	widget	base_model
en	mlx	mit	https://huggingface.co/microsoft/Phi-4-mini-instruct-reasoning/resolve/main/LICENSE	text-generation	nlp math code mlx	messages role content user How to solve 3*x^2+4*x+5=1?	DQN-Labs/dqnMath-v0.2-3.8B-HF

GGUF Files for dqnMath-v0.2-3.8B-HF

These are the GGUF files for DQN-Labs/dqnMath-v0.2-3.8B-HF.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.

You can find more info about me and what I do here.

DQN-Labs/dqnMath-v0.2-3.8B-HF

This model DQN-Labs/dqnMath-v0.2-3.8B-HF was converted to MLX format from microsoft/Phi-4-mini-reasoning using mlx-lm version 0.30.7.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("DQN-Labs/dqnMath-v0.2-3.8B-HF")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)