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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	---
	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](https://huggingface.co/cloudsurf-software/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](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC)
	— 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](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/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](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/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](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/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](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/CloudSurf-4B-FC.Q8_0.gguf) \| 8.0 GB \| Near-lossless \| 6/6 PASS (2026-08-19) \|
	\| [CloudSurf-4B-FC.Q3_K_M.gguf](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/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](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/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](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/mmproj-CloudSurf-4B-FC.F16.gguf)
	(1.0 GB) or
	[mmproj-CloudSurf-4B-FC.Q8_0.gguf](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/mmproj-CloudSurf-4B-FC.Q8_0.gguf)
	(0.6 GB):

	```bash
	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](https://huggingface.co/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`](https://huggingface.co/cloudsurf-software/CloudSurf-4B-FC-GGUF/resolve/main/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.

	```bash
	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)

	```bash
	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

	```bash
	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.