Instructions to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF",
	filename="Qwen3-Coder-30B-A3B-Instruct-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-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 KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_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 KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_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 KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M

Use Docker

docker model run hf.co/KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M

LM Studio
Jan
Ollama
How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF with Ollama:
```
ollama run hf.co/KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M
```

Unsloth Studio

How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-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 KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-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 KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF to start chatting

How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_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": "KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-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 KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_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 KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_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 "KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_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"

Docker Model Runner
How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF with Docker Model Runner:
```
docker model run hf.co/KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M
```

Lemonade

How to use KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF:IQ2_M

Run and chat with the model

lemonade run user.Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF-IQ2_M

List all available models

lemonade list

Qwen3-Coder-30B-A3B-Instruct — IQ2_M GGUF (agentic-verified)

An IQ2_M (imatrix) quant of Qwen/Qwen3-Coder-30B-A3B-Instruct at ~10 GB — runs on a normal laptop, stays a capable agentic coder at 2-bit. Weights & training © Qwen (Apache-2.0); independent quant + eval, full credit. (Other quants: unsloth — this one adds an independent agentic eval, the exact test set, and configurable-context Modelfiles.)

📦 The quant

Qwen3-Coder-30B-A3B-Instruct-IQ2_M.gguf — ~10 GB, imatrix-quantized (2-bit "I-quant": smallest size that stays sharp; needs the bundled imatrix, a touch more CPU than K-quants). 30B total / 3B active (MoE → fast inference despite the size).

🧮 Will it fit? (RAM/VRAM cheat-sheet)

Total ≈ 10 GB weights + KV-cache (grows with context):

you have	context you can run
12 GB	~8–16K
16 GB	~32–64K
24 GB	~128K
32 GB+	full native (262K)

🧠 Context size (262K native)

Configurable — Modelfiles at 8K / 32K / 128K / 256K included; full guide in CONTEXT.md.

ollama create qc-128k -f Modelfile-128k && ollama run qc-128k
# or per-session:  >>> /set parameter num_ctx 262144

🚀 How to run it

ollama run hf.co/KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF   # default 32K
llama-server -m Qwen3-Coder-30B-A3B-Instruct-IQ2_M.gguf -c 131072 --jinja

Recommended sampling: temperature 0.7, top_p 0.8. Agentic use: this model emits the <function=...> (OpenHands/Qwen-Coder) tool format. In OpenHands / Qwen-Agent it's native. In a Claude-Code-style harness, inject tool defs as TEXT in that format — relying on the native tools param yields empty output (that fix took it from 0 → solving tasks here).

⚠️ Good to know

Strengths: agentic coding — at IQ2 it still does clean multi-step tool use + writes correct code (verified, see below).
Limits: 2-bit is lossy vs the fp16; for max fidelity use a higher quant. It's a coder, English-centric.
The agentic tool-format note above matters — without it, it looks "broken" when it isn't.

📊 Evaluation methodology (so the number is reproducible)

Local agentic SWE-bench Verified probe, Claude Code + agent-bridge harness (terminal-bench/Docker), temp 0.7 / top_p 0.8, tool format openhands.

probe	score	what it is
`swe-mix` (ranking)	3/30	6 continuity anchors + 24 discriminating (15× "1-4 h" + 9× "15 min-1 h", 10 repos). Exact instances in `swe_mix.json`.
`django-6` (legacy/easy)	4/6	the original easy probe (saturated)

On swe-mix, the 3 solved are the easier "anchor" tasks; the "1–4 h" instances are out of reach for a 10 GB model in a 20-min agent budget (expected). This ranks models head-to-head on the same set — not 1:1 with the official SWE-bench leaderboard. Per-instance pass/fail and the imatrix are reproducible from the artifacts here.

🗒️ Changelog

2026-06-28 — v1: IQ2_M release + context Modelfiles + agentic eval.

📚 Credit & license

Model, weights, training: © Qwen — Qwen3-Coder-30B-A3B-Instruct. IQ2_M imatrix quant + context Modelfiles + eval: KikoCis. Apache-2.0 (same as upstream). No weights modified.

Downloads last month: 401

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

2-bit

Inference Providers NEW

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Model tree for KikoCis/Qwen3-Coder-30B-A3B-Instruct-IQ2_M-GGUF

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Qwen/Qwen3-Coder-30B-A3B-Instruct

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