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

preview code

Raw

History Blame

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
	---

	<p align="center">
	<img src="./assets/thestage-edge-models-header.png" width="100%" alt="TheStageAI Edge Models — the right model at every memory budget">
	</p>

	<h1 align="center">Qwen3.5 2B — TheStageAI GGUF</h1>

	<p align="center">
	Four GGUF checkpoints for local inference with llama.cpp, from 738 MB to 2.01 GB.
	<br>
	<strong>Start with M — 1.07 GB and 100% of the BF16 instruction-strict IFEval score in our evaluation.</strong>
	</p>

	<p align="center">
	<a href="https://github.com/TheStageAI/edge-lm"><img src="./assets/cta-edge-lm.svg" height="42" alt="Explore edge-lm on GitHub"></a>

	<a href="https://docs.thestage.ai/"><img src="./assets/cta-docs.svg" height="42" alt="Read TheStageAI documentation"></a>

	<a href="https://app.thestage.ai/"><img src="./assets/cta-platform.svg" height="42" alt="Open TheStageAI Platform"></a>
	</p>

	Qwen 3.5 family: [0.8B](https://huggingface.co/TheStageAI/Qwen3.5-0.8B-GGUF) · [2B](https://huggingface.co/TheStageAI/Qwen3.5-2B-GGUF) · [4B](https://huggingface.co/TheStageAI/Qwen3.5-4B-GGUF) · [9B](https://huggingface.co/TheStageAI/Qwen3.5-9B-GGUF)

	## Start here

	\| Tier \| Size \| Use \| Download \|
	\| --- \| ---: \| --- \| --- \|
	\| XS \| 738 MB \| Smallest \| [GGUF](./Qwen3.5-2B-XS-TS-Q3_K_S.gguf) \|
	\| S \| 967 MB \| Adaptive compact \| [GGUF](./Qwen3.5-2B-S-TS-Q4_K_S.gguf) \|
	\| M \| 1.07 GB \| Recommended · Fixed Q4 reference \| [GGUF](./Qwen3.5-2B-M-TS-Q4_K_M.gguf) \|
	\| L \| 2.01 GB \| Q8 fidelity reference \| [GGUF](./Qwen3.5-2B-L-TS-Q8_0.gguf) \|

	Sizes use decimal MB/GB. Exact byte counts are recorded in [`release-manifest.json`](./release-manifest.json).

	## Run with llama.cpp

	This command selects the recommended M checkpoint by exact filename:

	```bash
	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`.

	<details>
	<summary><b>Evaluation protocol and XS reasoning note</b></summary>

	- 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.

	</details>

	## How these checkpoints were built

	This release starts from [`Qwen/Qwen3.5-2B`](https://huggingface.co/Qwen/Qwen3.5-2B) at revision [`15852e8c16360a2fea060d615a32b45270f8a8fc`](https://huggingface.co/Qwen/Qwen3.5-2B/tree/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.

	<details>
	<summary><b>File types, Hub selectors, and effective BPW</b></summary>

	\| 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`](./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.

	</details>

	## TheStageAI edge stack

	These checkpoints provide a portable llama.cpp deployment path. For native compressed models on Apple Silicon, explore [edge-lm](https://github.com/TheStageAI/edge-lm). For custom compression, compilation, and deployment workflows, use the [TheStageAI Platform](https://app.thestage.ai/) and [documentation](https://docs.thestage.ai/).

	Optimizing for a specific device, latency target, or memory budget? [Talk to the TheStageAI team →](https://app.thestage.ai/contact)

	## 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`](./release-manifest.json). Use its SHA-256 digests to verify downloaded files. Export and load gates used [llama.cpp revision `bec4772f6a2527d371557b5d2032641e5ff7619c`](https://github.com/ggml-org/llama.cpp/commit/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.