Text Generation
GGUF
English
hob-forge
edition
small-gpu
qwen3.5
llama.cpp
ollama
lm-studio
imatrix
iq4_xs
q4_k_m
q5_k_m
q6_k
q8_0
8gb
cpu
laptop
instruct
conversational
Instructions to use Hob-forge/Qwen3.5-4B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Hob-forge/Qwen3.5-4B-Instruct-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 Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Hob-forge/Qwen3.5-4B-Instruct-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 Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Hob-forge/Qwen3.5-4B-Instruct-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 Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Hob-forge/Qwen3.5-4B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Hob-forge/Qwen3.5-4B-Instruct-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": "Hob-forge/Qwen3.5-4B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
- Ollama
How to use Hob-forge/Qwen3.5-4B-Instruct-GGUF with Ollama:
ollama run hf.co/Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use Hob-forge/Qwen3.5-4B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Hob-forge/Qwen3.5-4B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use Hob-forge/Qwen3.5-4B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.5-4B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Hob-forge/Qwen3.5-4B-Instruct-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 Hob-forge/Qwen3.5-4B-Instruct-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 Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Hob-forge/Qwen3.5-4B-Instruct-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
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 "Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M" \ --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"
RUNNING.md — every step executed
Browse files- RUNNING.md +99 -0
RUNNING.md
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| 1 |
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# Running Qwen3.5-4B-Instruct (Hob Forge Edition) — zero to first tool call
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Every step below was executed on real hardware before this shipped. Two paths: **A** for
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any machine with ollama (easiest), **B** for llama.cpp directly (most control). Numbers
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in the card's tables were measured with path B on a 12GB RTX 5070; an 8GB card runs
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everything here — see the memory table in the card.
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---
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## Path A — ollama (any OS, 5 minutes)
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1. Install ollama: https://ollama.com/download (one installer, all platforms).
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2. Pull and run this edition directly from HF:
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```bash
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ollama run hf.co/Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M
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```
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First run downloads 2.6GB. You're chatting when the `>>>` appears.
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3. **Thinking mode**: the model reasons out loud by default. For clean answers via the API:
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```bash
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curl http://localhost:11434/api/chat -d '{
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"model": "hf.co/Hob-forge/Qwen3.5-4B-Instruct-GGUF:Q4_K_M",
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"messages": [{"role":"user","content":"Why is the sky blue? One sentence."}],
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"think": false, "stream": false
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}'
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```
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Note `think` sits at the **top level** of the body — not inside `options`. (Scar #1:
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we lost an afternoon to that once.)
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4. Context size: ollama defaults small. For the long context this arch is great at:
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`ollama run … ` then `/set parameter num_ctx 16384` — or bake it into a Modelfile.
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Check what you actually got: the server log prints the KV allocation.
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## Path B — llama.cpp (measured-numbers path)
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1. Get a **2026 build** — this is a hybrid-attention architecture; builds older than
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~March 2026 will fail with unknown-architecture errors (Scar #2: a December build
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converted this model into a file that crashed *newer* runtimes — toolchain vintage
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matters in both directions):
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```bash
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git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
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cmake -B build -DGGML_CUDA=ON && cmake --build build -j # drop -DGGML_CUDA=ON for CPU
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```
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2. Download a quant (Q4_K_M recommended — see the card's which-file table):
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```bash
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hf download Hob-forge/Qwen3.5-4B-Instruct-GGUF Qwen3.5-4B-Instruct-Q4_K_M.gguf --local-dir .
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```
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3. Chat, single-turn, GPU:
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```bash
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./build/bin/llama-cli -m Qwen3.5-4B-Instruct-Q4_K_M.gguf \
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-st -p "Explain mmap in one paragraph." -ngl 99 -c 8192
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```
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- `-st` (single-turn) matters: without it llama-cli may enter interactive conversation
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mode and appear to "hang" waiting at a `>` prompt (Scar #3 — we watched a script wait
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four hours for someone to type).
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- 8GB card: this fits whole (`-ngl 99`). If you're sharing the GPU, `-ngl 20` splits
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layers to CPU gracefully.
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4. Serve an OpenAI-compatible API:
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```bash
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./build/bin/llama-server -m Qwen3.5-4B-Instruct-Q4_K_M.gguf -ngl 99 -c 16384 --port 8080
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```
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## First tool call (the part most cards skip)
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The template supports native tool calling. Against llama-server:
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```bash
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curl http://localhost:8080/v1/chat/completions -d '{
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"model": "qwen3.5-4b",
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"messages": [{"role":"user","content":"What is 37.2% of 8412? Use the calculator."}],
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"tools": [{"type":"function","function":{"name":"calculator",
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"description":"Evaluate a math expression",
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"parameters":{"type":"object","properties":{"expression":{"type":"string"}},
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"required":["expression"]}}}]
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}'
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```
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Expected: a `tool_calls` entry with `{"expression":"8412*0.372"}`-style arguments. We ran
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exactly this before shipping. If you get prose instead of a tool call, your runtime is too
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old to render this template's tool block — see step B1.
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## Sampling that works (verified)
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| Mode | temp | top_p | top_k |
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|---|---|---|---|
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| Thinking (default) | 0.6 | 0.95 | 20 |
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| Non-thinking | 0.7 | 0.8 | 20 |
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## Troubleshooting (our scars, your shortcuts)
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| Symptom | Cause & fix |
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|---|---|
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| `unknown architecture` / load error | llama.cpp too old — build ≥ March 2026 (B1). |
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| `blk.32 … not found` on a self-converted file | You converted with a text-only load that dropped the MTP block — convert from the full snapshot, or use our files. |
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| Appears to hang at a `>` | Interactive mode — add `-st`, give `-p`. |
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| Painfully slow on CPU-only | The hybrid DeltaNet layers' CPU path is immature; this model *wants* a GPU. CPU works for testing, not serving. |
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| `<think>` text in answers | By design. `think:false` (ollama, top-level) / `enable_thinking=False` (transformers) / parse it out. |
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| IQ4_XS slower than Q4_K_M despite being smaller | Correct and measured (45 vs 138 t/s on RTX) — i-quant dequant cost. Use Q4_K_M unless the 200MB matters. |
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| Garbled/endless output | Check eos: template expects `<|im_end|>` — custom Modelfiles must include it as a stop. |
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*Something not covered? Open a discussion on the repo — we actually answer. — Hob Forge*
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