Instructions to use Jackrong/Qwen3.5-4B-Neo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/Qwen3.5-4B-Neo-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jackrong/Qwen3.5-4B-Neo-GGUF",
	filename="Qwen3.5-4B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Jackrong/Qwen3.5-4B-Neo-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

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 Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

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 Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Jackrong/Qwen3.5-4B-Neo-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/Qwen3.5-4B-Neo-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": "Jackrong/Qwen3.5-4B-Neo-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

Ollama
How to use Jackrong/Qwen3.5-4B-Neo-GGUF with Ollama:
```
ollama run hf.co/Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M
```

Unsloth Studio

How to use Jackrong/Qwen3.5-4B-Neo-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 Jackrong/Qwen3.5-4B-Neo-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 Jackrong/Qwen3.5-4B-Neo-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/Qwen3.5-4B-Neo-GGUF to start chatting

How to use Jackrong/Qwen3.5-4B-Neo-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

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": "Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jackrong/Qwen3.5-4B-Neo-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

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 Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Jackrong/Qwen3.5-4B-Neo-GGUF with Docker Model Runner:
```
docker model run hf.co/Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M
```

Lemonade

How to use Jackrong/Qwen3.5-4B-Neo-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jackrong/Qwen3.5-4B-Neo-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-Neo-GGUF-Q4_K_M

List all available models

lemonade list

A newer version of this model is available: Jackrong/Qwen3.5-4B-Neo

🌟 Qwen3.5-4B-Neo

Model Introduction

Qwen3.5-4B-Neo is a reasoning-focused fine-tune of Qwen3.5-4B. It is designed to make the model’s reasoning process more concise and efficient, while keeping overall accuracy competitive.

On a 250-question MMLU-Pro subset covering five categories, Qwen3.5-4B-Neo achieved 82.00% pass@1 (205/250), compared with 80.40% (201/250) for the base Qwen3.5-4B. The gain is modest, but Neo also shows a much shorter reasoning process overall.

On non-truncated outputs, the average think-chain length was reduced from 6,962 to 3,955 characters, and the median length dropped from 4,600 to 1,951 characters. In efficiency terms, this corresponds to 2.31 correct solutions per 10k think characters, compared with 1.03 for the base model.

Across the five categories, Neo performed better in biology, computer science, mathematics, and other sciences, while trailing in physics. Overall, the results suggest that Qwen3.5-4B-Neo offers slightly better accuracy than the base model, with a substantially more efficient reasoning style.

⚠️ Note: The evaluation results shown here are based on a sampled subset of MMLU-Pro rather than the full benchmark. While the subset was kept balanced across five categories, the reported numbers are intended mainly for relative comparison under this specific setting and may not fully represent the model’s performance on the complete benchmark.

MMLU-Pro Benchmark Analysis 🪐

🗺️ Training Pipeline Overview

Base Model (Qwen/Qwen3.5-4B)
 │
 ▼
Qwen3.5-4B fine-tuned with Unsloth
 │
 ▼
Supervised Fine-Tuning (SFT) + LoRA
(Response-Only Training masked on "<|im_start|>assistant\n<think>")
 │
 ▼
Jackrong/Qwen3.5-4B-Neo

🧠 Example of Learned Reasoning Scaffold

Through robust data cleaning and formatting, the model was conditioned to explicitly structure its thought processes inside <think>...</think> tags before emitting the final answer. This forces the model to methodically break down complex programming or logical problems without repetitive thoughts.

<|im_start|>user
[User Query here]<|im_end|>
<|im_start|>assistant
<think>
  .
  .
  .
    ...
</think>
[Final concise and accurate answer]

📚 All Datasets Used

The dataset consists of high-quality, filtered reasoning distillation data merged during the pipeline. Our pipeline dynamically sampled and structured conversations, strictly maintaining the intended layout.

stepfun-ai/Step-3.5-Flash-SFT
Jackrong/Competitive-Programming-python-blend (A custom curated blend specifically for Python competitive programming and logic).

Detailed breakdown of the Competitive-Programming-python-blend:

Source	Role in the Blend
`nohurry/Opus-4.6-Reasoning-3000x-filtered`	Reasoning-heavy synthetic SFT data
`Jackrong/Qwen3.5-reasoning-700x`	Distilled reasoning and instruction-following data
`nvidia/Nemotron-SFT-Competitive-Programming-v2` (`competitive_coding_python`)	Primary Python competitive-programming supervision
`nvidia/Nemotron-SFT-Competitive-Programming-v2` (`competitive_coding_cpp`)	Small cross-language competitive-programming supplement
`nvidia/Nemotron-SFT-SWE-v2` (`agentless`)	Lightweight agentless SWE-style supervision
`nvidia/Nemotron-SFT-Instruction-Following-Chat-v2` (`reasoning_on`)	Small reasoning-oriented chat supplement

⚠️ Limitations & Intended Use

Hallucination Risk: While reasoning is strong, the model remains an autoregressive LLM; external facts provided during the thinking sequence may occasionally contain hallucinations if verifying real-world events.
Context Boundaries: In rare cases of extremely complex logic where the model struggles to converge, it may exhibit truncation events from excessive circular thinking.
Intended Scenario: Best suited for offline analytical tasks, coding, competitive programming, math, and heavy logic-dependent prompting where the user needs to transparently follow the AI's internal logic with high token efficiency.
This model is a test version intended solely for learning and demonstration purposes, and is for academic research and technical exploration use only.

🙏 Acknowledgements

Significant thanks to the Unsloth AI team for making rapid fine-tuning of large LLM models accessible. Additionally, we acknowledge Qwen internally, and the open-source community developers producing exceptional distilled datasets.

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GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Jackrong/Qwen3.5-4B-Neo-GGUF

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