Instructions to use RobTeam/qwen3.5-2b-ru-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 RobTeam/qwen3.5-2b-ru-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 RobTeam/qwen3.5-2b-ru-gguf:F16
# Run inference directly in the terminal:
llama cli -hf RobTeam/qwen3.5-2b-ru-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RobTeam/qwen3.5-2b-ru-gguf:F16
# Run inference directly in the terminal:
llama cli -hf RobTeam/qwen3.5-2b-ru-gguf:F16

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 RobTeam/qwen3.5-2b-ru-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf RobTeam/qwen3.5-2b-ru-gguf:F16

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 RobTeam/qwen3.5-2b-ru-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RobTeam/qwen3.5-2b-ru-gguf:F16

Use Docker

docker model run hf.co/RobTeam/qwen3.5-2b-ru-gguf:F16

LM Studio
Jan
Ollama
How to use RobTeam/qwen3.5-2b-ru-gguf with Ollama:
```
ollama run hf.co/RobTeam/qwen3.5-2b-ru-gguf:F16
```
Unsloth Desktop

How to use RobTeam/qwen3.5-2b-ru-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RobTeam/qwen3.5-2b-ru-gguf:F16

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": "RobTeam/qwen3.5-2b-ru-gguf:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use RobTeam/qwen3.5-2b-ru-gguf with Docker Model Runner:
```
docker model run hf.co/RobTeam/qwen3.5-2b-ru-gguf:F16
```

Lemonade

How to use RobTeam/qwen3.5-2b-ru-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RobTeam/qwen3.5-2b-ru-gguf:F16

Run and chat with the model

lemonade run user.qwen3.5-2b-ru-gguf-F16

List all available models

lemonade list

Hermes Agent

How to use RobTeam/qwen3.5-2b-ru-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 RobTeam/qwen3.5-2b-ru-gguf:F16

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 RobTeam/qwen3.5-2b-ru-gguf:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RobTeam/qwen3.5-2b-ru-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RobTeam/qwen3.5-2b-ru-gguf:F16

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 "RobTeam/qwen3.5-2b-ru-gguf:F16" \
  --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-SFT-RU (GGUF) — unofficial derivative

Unofficial Russian SFT derivative of Qwen/Qwen3.5-2B (Apache 2.0). Not affiliated with Alibaba Qwen team. Base model license (Apache 2.0) applies.

What was done

SFT (LoRA r=8, MLP + attention, base frozen) on 572 Russian rows: everyday Q&A, math, 40 Python + 40 Java code tasks. 159 steps, CPU.
Adapters merged into stock weights (MTP tensors preserved).
Real GGUF quantization with llama.cpp: imatrix calibrated on 1200 mixed rows, then Q4_K_M (5.36 BPW, 1.22 GB). Also ships F16 (3.9 GB).
Engine-verified: loads in llama-server, 15*4 → 60 at ~22 tok/s CPU.

Files

Qwen3.5-2B-SFT-Q4_K_M.gguf (1.22 GB) — recommended for LM Studio / Ollama.
Qwen3.5-2B-SFT-F16.gguf (3.9 GB) — full precision merged SFT.

Use (LM Studio)

Drop the .gguf into LM Studio (or pull this repo from the Models tab), context 4096 to start. Recommended sampling (Qwen3.5 non-thinking text): temperature 1.0, top_k 20, top_p 1.0, presence_penalty 2.0.

Honesty notes

No formal benchmarks (MMLU etc.) were run. Domain checks only: math (15*4 = 60), Russian small talk, short Python answers.
Small SFT (159 steps) shifts style toward the training rows; hard reasoning stays at the base 2B level. No scripts or training code distributed.

Downloads last month: 127

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

4-bit

16-bit

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