Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF
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Model: Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF
Source: Original Platform
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2026-04-11 17:12:55 +08:00
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---
language:
- en
library_name: mlx
license: mit
license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct-reasoning/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- math
- code
- mlx
widget:
- messages:
- role: user
content: How to solve 3*x^2+4*x+5=1?
base_model: 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](https://huggingface.co/DQN-Labs/dqnMath-v0.2-3.8B-HF).
## Downloads
| GGUF Link | Quantization | Description |
| ---- | ----- | ----------- |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q2_K.gguf) | Q2_K | Lowest quality |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q3_K_S.gguf) | Q3_K_S | |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.IQ3_S.gguf) | IQ3_S | Integer quant, preferable over Q3_K_S |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.IQ3_M.gguf) | IQ3_M | Integer quant |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q3_K_M.gguf) | Q3_K_M | |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q3_K_L.gguf) | Q3_K_L | |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.IQ4_XS.gguf) | IQ4_XS | Integer quant |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q4_K_S.gguf) | Q4_K_S | Fast with good performance |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q4_K_M.gguf) | Q4_K_M | **Recommended:** Perfect mix of speed and performance |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q5_K_S.gguf) | Q5_K_S | |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q5_K_M.gguf) | Q5_K_M | |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q6_K.gguf) | Q6_K | Very good quality |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.Q8_0.gguf) | Q8_0 | Best quality |
| [Download](https://huggingface.co/Flexan/DQN-Labs-dqnMath-v0.2-3.8B-HF-GGUF/resolve/main/dqnMath-v0.2-3.8B-HF.f16.gguf) | 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](https://huggingface.co/DQN-Labs/dqnMath-v0.2-3.8B-HF).
You can find more info about me and what I do [here](https://huggingface.co/Flexan/Flexan).
# DQN-Labs/dqnMath-v0.2-3.8B-HF
This model [DQN-Labs/dqnMath-v0.2-3.8B-HF](https://huggingface.co/DQN-Labs/dqnMath-v0.2-3.8B-HF) was
converted to MLX format from [microsoft/Phi-4-mini-reasoning](https://huggingface.co/microsoft/Phi-4-mini-reasoning)
using mlx-lm version **0.30.7**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
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)
```

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