Instructions to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Use Docker

docker model run hf.co/naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Ollama
How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with Ollama:
```
ollama run hf.co/naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K
```

Unsloth Studio

How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF to start chatting

How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with Docker Model Runner:
```
docker model run hf.co/naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K
```

Lemonade

How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Run and chat with the model

lemonade run user.yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF-Q6_K

List all available models

lemonade list

Hermes Agent

How to use naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default naksyu/yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat

yui-math-python-qwen3.5-4b-v0.5d-fft-GGUF / README.md

naksyu

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metadata

license: apache-2.0
library_name: gguf
pipeline_tag: text-generation
base_model:
  - naksyu/yui-math-python-qwen35-4b-v0.5d-fft
  - Qwen/Qwen3.5-4B
datasets:
  - drlee1/deepseek-v4-distill-ko-1k
  - Jongsim/claude-opus-4.6-reasoning-12k-ko-filtered-v2
language:
  - ko
  - en
tags:
  - gguf
  - qwen3.5
  - qwen35
  - q6_k
  - llama.cpp
  - lm-studio
  - math
  - python
  - korean
  - reasoning
model_name: yui-math-python-qwen35-4b-v0.5d-fft-GGUF

yui-math-python-qwen35-4b-v0.5d-fft GGUF

GGUF export of naksyu/yui-math-python-qwen35-4b-v0.5d-fft, a full-parameter SFT of Qwen/Qwen3.5-4B for Korean/English math reasoning, Python-assisted calculation habits, and compact practical answers.

Recommended File

Use this file:

File	Quantization	Size	Status
`yui-math-python-qwen35-4b-v0.5d-fft-no-mtp-Q6_K.gguf`	Q6_K	3.23 GiB	validated with `llama-server`

This is the intended LM Studio / llama.cpp file for normal local inference.

Compatibility

This model uses the qwen35 GGUF architecture. Use a recent llama.cpp build or an LM Studio build with Qwen3.5 GGUF support.

Important: the source Transformers config contains mtp_num_hidden_layers=1, but the exported checkpoint used here does not contain separate mtp.* tensors. A plain GGUF conversion can produce metadata that expects a non-existent blk.32.* layer and then fail during load.

This GGUF was therefore converted with --no-mtp. The validated metadata is:

Field	Value
`general.architecture`	`qwen35`
`general.file_type`	`18` / Q6_K
`qwen35.block_count`	`32`
`qwen35.context_length`	`262144`
`qwen35.attention.head_count`	`16`
`qwen35.attention.head_count_kv`	`4`
`qwen35.rope.dimension_sections`	`[11, 11, 10, 0]`
`qwen35.nextn_predict_layers`	absent
tensor count	`426`

llama.cpp Example

CPU load smoke:

llama-server \
  -m yui-math-python-qwen35-4b-v0.5d-fft-no-mtp-Q6_K.gguf \
  --host 127.0.0.1 \
  --port 18080 \
  -c 2048 \
  -ngl 0

Use a larger context only when you have enough RAM/VRAM. The model config advertises 262K context, but this GGUF was only smoke-tested locally at a small context for load compatibility.

LM Studio

In LM Studio, import or place:

yui-math-python-qwen35-4b-v0.5d-fft-no-mtp-Q6_K.gguf

If loading fails, first check that the runtime supports qwen35. If the error mentions missing blk.32.* tensors, it is the MTP metadata mismatch; use the no-mtp-Q6_K file rather than a plain conversion.

Source Model

The source model card is here:

naksyu/yui-math-python-qwen35-4b-v0.5d-fft

Base model:

Qwen/Qwen3.5-4B

Training Data Summary

The source checkpoint was trained on qwen35_sft_v0_5d_system_mix_with_all_data_cutoff2048.jsonl.

Data bucket	Retained rows
DeepSeek-derived Korean sample	462
Claude/trace Korean reasoning dataset	11,973
Local/Yui/Lime/generated data	37,994
Total	50,429

Public data sources included:

Limitations

Experimental local SFT conversion, not a benchmarked production release.
Q6_K quantization changes numerical behavior from the bf16 source checkpoint.
Requires current GGUF runtime support for Qwen3.5 / qwen35.
The model can still make arithmetic, reasoning, and code mistakes. Verify important outputs externally.

Training Data

This model was fine-tuned for experimental math/Python reasoning behavior.

The training mix includes:

User-created Lime/Yui math-python SFT data
Public Hugging Face datasets including:
- drlee1/deepseek-v4-distill-ko-1k (MIT)
- Jongsim/claude-opus-4.6-reasoning-12k-ko-filtered-v2 (Apache-2.0)

Some upstream samples are synthetic or translated model outputs. This release is intended as a research/experimental small-model fine-tune, not as a commercial substitute for the upstream model providers.