Instructions to use cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M

Use Docker

docker model run hf.co/cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use cloudsurf-software/CloudSurf-4B-FC-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cloudsurf-software/CloudSurf-4B-FC-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": "cloudsurf-software/CloudSurf-4B-FC-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M

Ollama
How to use cloudsurf-software/CloudSurf-4B-FC-GGUF with Ollama:
```
ollama run hf.co/cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M
```

Unsloth Studio

How to use cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cloudsurf-software/CloudSurf-4B-FC-GGUF to start chatting

How to use cloudsurf-software/CloudSurf-4B-FC-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cloudsurf-software/CloudSurf-4B-FC-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": "cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cloudsurf-software/CloudSurf-4B-FC-GGUF with Docker Model Runner:
```
docker model run hf.co/cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M
```

Lemonade

How to use cloudsurf-software/CloudSurf-4B-FC-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CloudSurf-4B-FC-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-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 cloudsurf-software/CloudSurf-4B-FC-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cloudsurf-software/CloudSurf-4B-FC-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cloudsurf-software/CloudSurf-4B-FC-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 "cloudsurf-software/CloudSurf-4B-FC-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"

CloudSurf-4B-FC-GGUF / README.md

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metadata

license: apache-2.0
language:
  - en
base_model: cloudsurf-software/CloudSurf-4B-FC
base_model_relation: quantized
pipeline_tag: text-generation
library_name: llama.cpp
model_name: CloudSurf-4B-FC
tags:
  - gguf
  - llama.cpp
  - function-calling
  - tool-use
  - bfcl
  - cloudsurf
  - gemma-4
  - conversational
inference: false

CloudSurf-4B-FC — official GGUF quants

Official GGUF quantizations of CloudSurf-4B-FC, a Gemma-4 E4B (effective-4B active; ~8.0B total params) function-calling / tool-use model by CloudSurf Software, quantized by us from the BF16 merged champion checkpoint. All benchmark scores, training details, and honesty disclosures live in the base model card — the scores there were measured on the BF16 weights, not on these quants. A quantized-model eval is pending and will be added here when it lands; until then, assume some quality loss below Q8_0, growing as the bit-width shrinks.

Every model file in this repo passed a 6-case tool-call smoke suite (simple call, multi-argument, tool selection, role:tool round-trip, irrelevance refusal, parallel calls) against llama-server --jinja before upload. A file that fails any case does not ship.

Files

File	Size	Use case	Tool-call smoke
CloudSurf-4B-FC.Q4_K_M.gguf	5.3 GB	Recommended default — fits 8 GB RAM/VRAM	6/6 PASS (2026-08-19)
CloudSurf-4B-FC.Q5_K_M.gguf	5.8 GB	Balanced quality/size	6/6 PASS (2026-08-19)
CloudSurf-4B-FC.Q6_K.gguf	6.2 GB	Quality pick for 8GB VRAM	6/6 PASS (2026-08-19)
CloudSurf-4B-FC.Q8_0.gguf	8.0 GB	Near-lossless	6/6 PASS (2026-08-19)
CloudSurf-4B-FC.Q3_K_M.gguf	4.9 GB	Low-RAM entry point	6/6 PASS (2026-08-19)
CloudSurf-4B-FC.BF16.gguf	15.1 GB	Reference / requantize source	6/6 PASS (2026-08-19)

Vision (mmproj)

Pair any quant with a projector file for image input: mmproj-CloudSurf-4B-FC.F16.gguf (1.0 GB) or mmproj-CloudSurf-4B-FC.Q8_0.gguf (0.6 GB):

llama-server -m CloudSurf-4B-FC.Q4_K_M.gguf --mmproj mmproj-CloudSurf-4B-FC.F16.gguf --jinja -ngl 99

Function calling is this model's primary capability; vision rides along from the Gemma-4 base.

For imatrix (weighted) quants below Q3, see the community repo mradermacher/CloudSurf-4B-FC-i1-GGUF — an independent quantization of the same weights, credit to mradermacher for the fast pickup.

SHA-256 checksums for every file are in quant-manifest.json.

Running

llama.cpp (server, OpenAI-compatible tool calls)

--jinja is required — it enables the embedded chat template's tool-call rendering. Without it the server falls back to a generic template and tool calls will not parse.

llama-server -m CloudSurf-4B-FC.Q4_K_M.gguf --jinja -ngl 99 -c 16384 \
  --host 127.0.0.1 --port 8080 --alias cloudsurf-4b-fc
# then point any OpenAI client at http://127.0.0.1:8080/v1
# (chat/completions with tools in, tool_calls out)

llama.cpp (CLI)

llama-cli -m CloudSurf-4B-FC.Q4_K_M.gguf --jinja -ngl 99 -c 16384 \
  -p "You are a helpful assistant." -cnv

LM Studio

Search for CloudSurf-4B-FC-GGUF in the in-app model browser and download a quant. Enable the model's own prompt template (default) and use the OpenAI-compatible local server for tool calls.

Ollama

cat > Modelfile <<'EOF'
FROM ./CloudSurf-4B-FC.Q4_K_M.gguf
EOF
ollama create cloudsurf-4b-fc -f Modelfile
ollama run cloudsurf-4b-fc

The GGUF's embedded chat template carries the tool-call format; recent Ollama versions read it directly.

Notes and known quirks

Scores: the base card's BFCL V4 numbers (3-seed mean 55.73 as-registered / 54.91 matched-variant — both frames disclosed there) are BF16 measurements. No number in that card applies to these quants until the pending quantized-model eval is published here.
Params: "4B" is effective active parameters (the base's E4B convention); total is ~8.0B, which is what sizes these files.
</s> metadata quirk: GGUF conversions of this model can carry a stray </s> end-token metadata entry; llama.cpp detects and auto-corrects it at load (benign — observed and verified 2026-08-18).
Context: trained/evaluated at 131K-class context; -c 16384 above is a practical default, raise as your memory allows.

License

Apache-2.0, same as the base model. Gemma-4 base weights are subject to Google's Gemma terms as described in the base model card.