Instructions to use stevenlearns/qwen3.5-4B-super-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use stevenlearns/qwen3.5-4B-super-coder 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 stevenlearns/qwen3.5-4B-super-coder:BF16
# Run inference directly in the terminal:
llama cli -hf stevenlearns/qwen3.5-4B-super-coder:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf stevenlearns/qwen3.5-4B-super-coder:BF16
# Run inference directly in the terminal:
llama cli -hf stevenlearns/qwen3.5-4B-super-coder:BF16

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 stevenlearns/qwen3.5-4B-super-coder:BF16
# Run inference directly in the terminal:
./llama-cli -hf stevenlearns/qwen3.5-4B-super-coder:BF16

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 stevenlearns/qwen3.5-4B-super-coder:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf stevenlearns/qwen3.5-4B-super-coder:BF16

Use Docker

docker model run hf.co/stevenlearns/qwen3.5-4B-super-coder:BF16

LM Studio
Jan

vLLM

How to use stevenlearns/qwen3.5-4B-super-coder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/stevenlearns/qwen3.5-4B-super-coder:BF16

Ollama
How to use stevenlearns/qwen3.5-4B-super-coder with Ollama:
```
ollama run hf.co/stevenlearns/qwen3.5-4B-super-coder:BF16
```

Unsloth Studio

How to use stevenlearns/qwen3.5-4B-super-coder 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 stevenlearns/qwen3.5-4B-super-coder 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 stevenlearns/qwen3.5-4B-super-coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for stevenlearns/qwen3.5-4B-super-coder to start chatting

How to use stevenlearns/qwen3.5-4B-super-coder with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf stevenlearns/qwen3.5-4B-super-coder:BF16

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": "stevenlearns/qwen3.5-4B-super-coder:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use stevenlearns/qwen3.5-4B-super-coder with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf stevenlearns/qwen3.5-4B-super-coder:BF16

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 "stevenlearns/qwen3.5-4B-super-coder:BF16" \
  --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 stevenlearns/qwen3.5-4B-super-coder with Docker Model Runner:
```
docker model run hf.co/stevenlearns/qwen3.5-4B-super-coder:BF16
```

Lemonade

How to use stevenlearns/qwen3.5-4B-super-coder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull stevenlearns/qwen3.5-4B-super-coder:BF16

Run and chat with the model

lemonade run user.qwen3.5-4B-super-coder-BF16

List all available models

lemonade list

Hermes Agent

How to use stevenlearns/qwen3.5-4B-super-coder with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf stevenlearns/qwen3.5-4B-super-coder:BF16

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 stevenlearns/qwen3.5-4B-super-coder:BF16

Run Hermes

hermes

Atomic Chat

qwen3.5-4B-super-coder / README.md

stevenlearns

Duplicate from jica98/qwen3.5-4B-super-coder

88a9b53 22 days ago

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	---
	base_model: Qwen/Qwen3.5-4B
	tags:
	- coding
	- tool-calling
	- gguf
	- unsloth
	- llama-cpp
	- thinking-mode
	- Gated-DeltaNet
	- mobile-fit
	licence: apache-2.0
	pipeline_tag: text-generation
	---

	# qwen3.5-4B-super-coder (Q_4.0 GGUF)

	`qwen3.5-4B-super-coder` is a 4-bit quantized GGUF model optimized for fast, reliable coding, structured tool calling, and active reasoning (thinking mode) on consumer/mobile hardware. It is distilled from Claude Sonnet 4.6 & Opus 4.6, and merged/quantized using Unsloth.

	## Model Summary & Architecture

	- Base Model: `Qwen/Qwen3.5-4B`
	- Format: GGUF (Q_4.0 Quantization)
	- Size: ~2.6 GB
	- Context Window: 32K (optimized for mobile RAM budgets, natively supports up to 262K/1M context via YaRN)
	- Key Architectural Advantage: The base `Qwen3.5-4B` model uses a hybrid architecture combining Gated DeltaNet (3 layers) and Full Attention (1 layer) repeating. Since only 8 of the 32 layers store a full KV cache, the KV cache footprint is incredibly small (~0.4GB for 32K context), making it exceptionally well-suited for high-context coding on mobile devices (e.g., iPhone 15 Pro+, flagship Android, iPad Pro).

	## Distillation & Training Procedure

	This model was trained using a staged Supervised Fine-Tuning (SFT) pipeline to systematically inject reasoning capability, coding specialization, and tool-calling precision:

	```
	┌──────────────────────────────────────────┐
	│ Phase A: │
	│ General Distillation (Claude Style) │
	│ Dataset: Claude-Distills (140K) │
	└────────────────────┬─────────────────────┘
	│
	▼
	┌──────────────────────────────────────────┐
	│ Phase B: │
	│ Specialization (Coding & Tool Calling) │
	│ Dataset: Curated Replay Mix (77K) │
	└────────────────────┬─────────────────────┘
	│
	▼
	┌──────────────────────────────────────────┐
	│ Phase C: │
	│ Tool Precision & Schema Conformance │
	│ Dataset: Tool-focused Mix (~20K) │
	└──────────────────────────────────────────┘
	```

	1. Phase 1: Distillation (Claude Behavior)
	- Dataset: `clzoro/Claude-Distills` (140K samples; Sonnet 4.6 + Opus 4.6).
	- Objective: Transfer general instruction-following, Claude-like formatting/tone, and reasoning capabilities. The Opus subset (21K samples) provided the crucial `<think>` block traces to establish thinking capabilities.
	2. Phase 2: Specialization (Coding & Tools)
	- Dataset: Curated 77K sample mix (55K coding instructions, 13K tool calling, and 9K general anti-forgetting replay samples).
	- Objective: Specialize the model on coding accuracy across Python, JS, Shell, etc., and introduce structured tool-calling.
	3. Phase 3: Tool Precision
	- Dataset: Focused tool-calling dataset (~20K samples) with schema variations, neg/no-tool examples, and strict JSON format targets.
	- Objective: Ensure precise JSON schema conformance and reduce tool false-positives.
	4. Phase 4: Coding/Tool Specialization Continuation
	- Starting point: `jica98/qwen3.5-4b-claude-distill-lora` Phase 3 LoRA.
	- Output adapter: `qwen3.5-4b-phase4-specialize-lora`.
	- Training mix: local filtered coding/tool data from `filtered_dataset/train.jsonl`, Claude distillation replay from `data/claude_distill.jsonl`, and an Opus replay slice to retain visible reasoning behavior.
	- Objective: Continue the distilled LoRA into a stronger coding/tool-specialized adapter while preserving anti-forgetting replay.
	- Default recipe: 1024 max sequence length, batch size 1, gradient accumulation 8, learning rate `1e-4`, 1 epoch, checkpointing every 200 steps.

	## Phase 5 Fable Reasoning Fine-Tune

	The latest adapter was further fine-tuned for Fable reasoning and agentic coding traces after the Phase 4 specialization pass.

	Phase 5 training data:
	- `kelexine/fable-5-sft-traces` for cleaned Fable reasoning/SFT traces.
	- `armand0e/claude-fable-5-claude-code` for raw Claude/Fable-5 agent traces.
	- `victor/fable-5-boeing-747-trace` for the Boeing 747 Claude Code/Fable-5 trace.

	Training summary:
	- Starting point: `qwen3.5-4b-phase4-specialize-lora`.
	- Output adapter: `qwen3.5-4b-phase5-fable-lora`.
	- After dedupe/sample in the recorded run: 4,721 examples.
	- After max-length filtering at 4096 tokens: 4,267 examples.
	- Default recipe: batch size 1, gradient accumulation 8, learning rate `5e-5`, 1 epoch, BF16, `adamw_8bit`.

	The Phase 5 data loader normalizes traces into Qwen chat-template text, groups raw Claude event logs into session conversations, deduplicates samples, filters by token length, and skips checkpoint artifacts during Hub upload by default.

	## Strengths & What It Is Good At

	- 💻 Conversational Programming: Excel at writing clean, efficient, and well-commented code in Python, C++, Rust, JavaScript, Shell, and more.
	- 🧠 Visible Reasoning (Thinking Mode): When faced with complex reasoning or coding tasks, the model engages a `<think>...</think>` block to outline its plan before writing code.
	- 🛠️ Reliable Tool Calling: Specially tuned to parse and output valid JSON tool parameters conforming to provided function schemas.
	- 📱 Mobile & Edge Execution: With a weight footprint of ~2.6GB and extremely low KV cache overhead, it fits comfortably on 8GB+ RAM edge devices.

	## Recommended Inference Settings

	For the best balance of reasoning depth and formatting precision, use the following generation parameters:

	- Temperature: `0.6`
	- Top-P: `0.95`
	- Top-K: `20`
	- Min-P: `0.0`
	- Flash Attention: Enable `-fa` in llama.cpp/llama-cli for optimal speeds.
	- System Prompt: Set system prompt to guide the assistant (e.g. `You are a helpful coding assistant.`).


	## Benchmark Results (Q4_0 GGUF via LM Studio)

	Benchmark run against GGUF Q4_0 quant served through LM Studio on consumer AMD ROCm hardware. Results file: [`benchmark/lmstudio_q4_benchmark/benchmark_report.md`](https://huggingface.co/jica98/qwen3.5-4B-super-coder/blob/main/benchmark/lmstudio_q4_benchmark/benchmark_report.md)

	\| Benchmark \| Score \| Status \|
	\|---\|---\|---\|
	\| HumanEval+ Pass@1 \| 0.00 \| ok \|
	\| MBPP+ Pass@1 \| 0.00 \| ok \|
	\| BigCodeBench-Hard \| — \| needs_review \|
	\| LiveCodeBench v6 \| — \| not_run \|
	\| BFCL v4 \| — \| needs_review \|
	\| IFEval \| — \| needs_review \|
	\| MMLU-Pro \| — \| needs_review \|
	\| JSON validity \| 40.00% \| ok \|
	\| No-tool accuracy \| 87.50% \| ok \|

	Notes:
	- Several benchmarks require environment setup that wasn't completed (IFEval, MMLU-Pro, BFCL, BigCodeBench-Hard).
	- HumanEval+ and MBPP+ scored 0.00 — the Q4_0 quant may degrade code generation significantly; evaluation with the BF16 base is needed for comparison.
	- JSON validity and No-tool accuracy are custom deterministic diagnostics.