Instructions to use exoplanet/F2LLM-v2-1.7B-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 exoplanet/F2LLM-v2-1.7B-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 exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

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 exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

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 exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

Use Docker

docker model run hf.co/exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use exoplanet/F2LLM-v2-1.7B-GGUF with Ollama:
```
ollama run hf.co/exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0
```

Unsloth Studio

How to use exoplanet/F2LLM-v2-1.7B-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 exoplanet/F2LLM-v2-1.7B-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 exoplanet/F2LLM-v2-1.7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for exoplanet/F2LLM-v2-1.7B-GGUF to start chatting

How to use exoplanet/F2LLM-v2-1.7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

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": "exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use exoplanet/F2LLM-v2-1.7B-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 exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

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 exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use exoplanet/F2LLM-v2-1.7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

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 "exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0" \
  --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 exoplanet/F2LLM-v2-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0
```

Lemonade

How to use exoplanet/F2LLM-v2-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull exoplanet/F2LLM-v2-1.7B-GGUF:Q8_0

Run and chat with the model

lemonade run user.F2LLM-v2-1.7B-GGUF-Q8_0

List all available models

lemonade list

F2LLM-v2-1.7B — GGUF (Q8_0)

A GGUF conversion of codefuse-ai/F2LLM-v2-1.7B for use with llama.cpp on device. No changes to the weights other than the conversion and Q8_0 quantisation. All credit for the model belongs to the CodeFuse authors; it is redistributed here under the Apache-2.0 licence of the original.

Why this exists

The upstream repository publishes safetensors only. This is the same model in GGUF form so it can be loaded by llama.cpp on iOS.

Conversion

# llama.cpp b10192 (SHA 9ebfc3a8cf4c1c6983258c4d603274b2b3d3dd65)
python convert_hf_to_gguf.py --outtype f16 --outfile f2llm-f16.gguf <src>
llama-quantize f2llm-f16.gguf F2LLM-v2-1.7B-Q8_0.gguf Q8_0


Architecture	`qwen3` (Qwen3Model), 28 layers
Embedding dimension	2048
Pooling	last-token (`qwen3.pooling_type = 3`)
Normalisation	L2 (apply in your client)
`add_eos_token`	true
Size	1749 MiB
sha256	`4ba9abad93b1d46d162650da80ca4f1ebc29022fed51cae59aa9e2d5aa024ad3`

Usage notes

Query and document sides are asymmetric. Prefix queries with the instruction form and encode documents raw:

Instruct: Given a question, retrieve passages that can help answer the question.
Query: <your query>

Verified on device (iPhone 16e, Metal) against a hard multilingual retrieval set covering de/fr/ja/ko/zh — correct ranking in all five, with the widest gold-vs-distractor margins in ja/ko/zh.