Instructions to use TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use TheStageAI/Qwen3.5-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheStageAI/Qwen3.5-2B-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": "TheStageAI/Qwen3.5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Ollama
How to use TheStageAI/Qwen3.5-2B-GGUF with Ollama:
```
ollama run hf.co/TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TheStageAI/Qwen3.5-2B-GGUF to start chatting

How to use TheStageAI/Qwen3.5-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheStageAI/Qwen3.5-2B-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": "TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use TheStageAI/Qwen3.5-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheStageAI/Qwen3.5-2B-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 "TheStageAI/Qwen3.5-2B-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"

Docker Model Runner
How to use TheStageAI/Qwen3.5-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
```

Lemonade

How to use TheStageAI/Qwen3.5-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.5-2B-GGUF / README.md

michellemoorre

Update private release-card draft v3

4f6c343 verified 19 days ago

6.62 kB

license: apache-2.0
base_model:
  - Qwen/Qwen3.5-2B
base_model_relation: quantized
library_name: llama.cpp
pipeline_tag: text-generation
thumbnail: >-
  https://huggingface.co/TheStageAI/Qwen3.5-2B-GGUF/resolve/main/assets/thestage-edge-models-header.png
tags:
  - gguf
  - llama.cpp
  - quantized
  - mixed-precision
  - local-inference
  - qwen3.5

TheStageAI Edge Models — the right model at every memory budget

Qwen3.5 2B — TheStageAI GGUF

Four GGUF checkpoints for local inference with llama.cpp, from 738 MB to 2.01 GB.
Start with M — 1.07 GB and 100% of the BF16 instruction-strict IFEval score in our evaluation.

Qwen 3.5 family: 0.8B · 2B · 4B · 9B

Start here

Tier	Size	Use	Download
XS	738 MB	Smallest	GGUF
S	967 MB	Adaptive compact	GGUF
M	1.07 GB	Recommended · Fixed Q4 reference	GGUF
L	2.01 GB	Q8 fidelity reference	GGUF

Sizes use decimal MB/GB. Exact byte counts are recorded in release-manifest.json.

Run with llama.cpp

This command selects the recommended M checkpoint by exact filename:

llama-cli \
  --hf-repo TheStageAI/Qwen3.5-2B-GGUF \
  --hf-file Qwen3.5-2B-M-TS-Q4_K_M.gguf

Quality

Variant	IFEval prompt / instruction strict (%)	MMLU-Pro (%)
BF16 reference	65.43 / 74.70	—
XS	52.68 / 64.15	—
S	63.22 / 73.38	—
M	66.54 / 75.54	—
L	65.80 / 74.94	—

Only complete 12,032-question MMLU-Pro runs are reported. — means that no complete release score is available; partial-subject results are not promoted to headline metrics.

The benchmarks intentionally exercise different modes: IFEval measures non-thinking instruction and format adherence, while MMLU-Pro includes long sampled reasoning.

Reasoning: use S, M, or L for long-form reasoning. XS prioritizes minimum size; run Qwen XS with --reasoning off.

Evaluation protocol and XS reasoning note

IFEval: 541 prompts, native chat template, enable_thinking=false, temperature 0.
MMLU-Pro: 12,032 questions, native chat template, enable_thinking=true, temperature 1, top-p 0.95, 32,768-token output limit.
The headline BF16-retention percentage uses instruction-strict IFEval and is capped at 100%; the uncapped raw scores are shown in the table.

XS prioritizes minimum footprint and ordinary chat or instruction following. For long-form reasoning, start with S, M, or L. Run Qwen XS with --reasoning off. In our long-thinking evaluation, XS generated substantially longer trajectories and reached the 32,768-token output limit more often than S. This release therefore presents the model as IFEval-only; incomplete subject runs are not converted into headline MMLU-Pro scores.

How these checkpoints were built

This release starts from Qwen/Qwen3.5-2B at revision 15852e8c16360a2fea060d615a32b45270f8a8fc.

XS and S use model-specific mixed-precision schedules selected for explicit size targets.
M keeps fixed Q4_K precision across quantized decoder tensors.
L keeps fixed Q8_0 precision as the high-fidelity reference.

All four checkpoints receive scale tuning without changing their stored quantized codes or tensor layout. The recommended tier is selected independently for each base model from end-to-end evaluation.

File types, Hub selectors, and effective BPW

Variant	Hub selector	GGUF file type	Whole-file BPW
XS	`Q3_K_S`	`MOSTLY_Q2_K`	3.140
S	`Q4_K_S`	`MOSTLY_Q2_K`	4.109
M	`Q4_K_M`	`MOSTLY_Q4_K_M`	4.562
L	`Q8_0`	`MOSTLY_Q8_0`	8.558

XS and S are TheStageAI mixed-precision schedules. Their public Q-type suffixes provide approximate Hub size classes for discoverability; they do not describe every tensor in the payload. M and L use fixed Q4_K and Q8_0 assignments for quantized decoder tensors.

The exact tensor-type inventory and SHA-256 digest for every file are recorded in release-manifest.json. File size does not include KV cache or runtime buffers, which grow with context length. GGUF runtimes treat multimodal projectors as separate artifacts; this release contains the language-model files listed above.

TheStageAI edge stack

These checkpoints provide a portable llama.cpp deployment path. For native compressed models on Apple Silicon, explore edge-lm. For custom compression, compilation, and deployment workflows, use the TheStageAI Platform and documentation.

Optimizing for a specific device, latency target, or memory budget? Talk to the TheStageAI team →

Reproducibility

The exact base revision, byte sizes, GGUF file types, whole-file BPW, tensor inventories, SHA-256 digests, held-out KL values, and evaluation IDs are recorded in release-manifest.json. Use its SHA-256 digests to verify downloaded files. Export and load gates used llama.cpp revision bec4772f6a2527d371557b5d2032641e5ff7619c.

License

The model weights are released under the upstream model's apache-2.0 license. llama.cpp and any surrounding runtime code retain their own licenses.