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

Use Docker

docker model run hf.co/igorktech/Qwen3.5-2B-multiharness-RL-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use igorktech/Qwen3.5-2B-multiharness-RL-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "igorktech/Qwen3.5-2B-multiharness-RL-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": "igorktech/Qwen3.5-2B-multiharness-RL-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/igorktech/Qwen3.5-2B-multiharness-RL-GGUF:Q4_K_M

Ollama
How to use igorktech/Qwen3.5-2B-multiharness-RL-GGUF with Ollama:
```
ollama run hf.co/igorktech/Qwen3.5-2B-multiharness-RL-GGUF:Q4_K_M
```
Unsloth Desktop

How to use igorktech/Qwen3.5-2B-multiharness-RL-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use igorktech/Qwen3.5-2B-multiharness-RL-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use igorktech/Qwen3.5-2B-multiharness-RL-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 igorktech/Qwen3.5-2B-multiharness-RL-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 igorktech/Qwen3.5-2B-multiharness-RL-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use igorktech/Qwen3.5-2B-multiharness-RL-GGUF with OpenClaw:

Start the llama.cpp server

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

Qwen3.5-2B-multiharness-RL GGUF

GGUF quantizations of FineEnvs/Qwen3.5-2B-multiharness-RL.

File	Quant
`Qwen3.5-2B-multiharness-RL-Q8_0.gguf`	Q8_0
`Qwen3.5-2B-multiharness-RL-Q6_K.gguf`	Q6_K
`Qwen3.5-2B-multiharness-RL-Q5_K_M.gguf`	Q5_K_M
`Qwen3.5-2B-multiharness-RL-Q4_K_M.gguf`	Q4_K_M
`Qwen3.5-2B-multiharness-RL-Q4_K_S.gguf`	Q4_K_S
`Qwen3.5-2B-multiharness-RL-IQ4_XS.gguf`	IQ4_XS (imatrix)
`Qwen3.5-2B-multiharness-RL-Q3_K_M.gguf`	Q3_K_M (imatrix)
`Qwen3.5-2B-multiharness-RL-IQ3_M.gguf`	IQ3_M (imatrix)
`Qwen3.5-2B-multiharness-RL-Q2_K.gguf`	Q2_K (imatrix)
`mmproj-Qwen3.5-2B-multiharness-RL-f16.gguf`	vision projector (F16)

IQ quants and quants at Q3 and below use an importance matrix computed on wikitext-2-raw.

The multi-token-prediction (MTP) head declared in the source config is not present in the released weights, so these files contain the main model only.

Usage

llama-server -m Qwen3.5-2B-multiharness-RL-Q4_K_M.gguf --temp 0.8 --top-p 1.0

Image input (the source card claims no image benchmark; this release was tuned for text/tool use):

llama-mtmd-cli -m Qwen3.5-2B-multiharness-RL-Q4_K_M.gguf \
  --mmproj mmproj-Qwen3.5-2B-multiharness-RL-f16.gguf --image photo.jpg -p "Describe this image."

Sampling settings from the source card: temperature 0.8, top-p 1.0.

License and provenance

Derived from Qwen/Qwen3.5-2B under the Apache License 2.0, via the FineEnvs reinforcement-learning fine-tune. LICENSE, NOTICE and release_manifest.json from the source repository are included unchanged. Not an official Qwen or FineEnvs release.

Quantization only; the weights are not otherwise modified. The vision projector was converted using preprocessor_config.json from the base Qwen/Qwen3.5-2B repository, as the source repository does not ship one (the vision architecture is identical to the base model).

Downloads last month: 815

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for igorktech/Qwen3.5-2B-multiharness-RL-GGUF

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

FineEnvs/Qwen3.5-2B-multiharness-RL

Quantized

(1)

this model