Instructions to use vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use vsan/tiny-pickle-v3-coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vsan/tiny-pickle-v3-coder-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": "vsan/tiny-pickle-v3-coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M

Ollama
How to use vsan/tiny-pickle-v3-coder-GGUF with Ollama:
```
ollama run hf.co/vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M
```

Unsloth Studio

How to use vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vsan/tiny-pickle-v3-coder-GGUF to start chatting

How to use vsan/tiny-pickle-v3-coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vsan/tiny-pickle-v3-coder-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": "vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use vsan/tiny-pickle-v3-coder-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vsan/tiny-pickle-v3-coder-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 "vsan/tiny-pickle-v3-coder-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"

Docker Model Runner
How to use vsan/tiny-pickle-v3-coder-GGUF with Docker Model Runner:
```
docker model run hf.co/vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M
```

Lemonade

How to use vsan/tiny-pickle-v3-coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.tiny-pickle-v3-coder-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-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 vsan/tiny-pickle-v3-coder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

tiny-pickle-v3-coder-GGUF / README.md

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	---
	base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
	library_name: llama.cpp
	pipeline_tag: text-generation
	license: apache-2.0
	tags:
	- gguf
	- qwen3-coder
	- coding
	- software-engineering
	- moe
	- q8_0
	- q4_k_m
	- tiny-pickle
	---

	# Tiny Pickle v3 Coder — GGUF

	Quantized GGUF releases of Tiny Pickle v3 Coder.

	Tiny Pickle v3 Coder was produced by fine-tuning
	`Qwen/Qwen3-Coder-30B-A3B-Instruct` with the LoRA adapter published at
	`vsan/tiny-pickle-v3-coder-LoRA`, then merging and converting the resulting model with
	llama.cpp.

	## Files

	\| File \| Quantization \| Approximate size \|
	\|---\|---\|---:\|
	\| `tiny-pickle-v3-coder-q8_0.gguf` \| Q8_0 \| 31G \|
	\| `tiny-pickle-v3-coder-q4_k_m.gguf` \| Q4_K_M \| 18G \|

	Q8_0 retains greater numerical fidelity but requires more storage and
	memory. Q4_K_M is smaller and more practical for local inference.

	## Run with llama.cpp

	```bash
	llama-cli \
	-m tiny-pickle-v3-coder-q4_k_m.gguf \
	-ngl 99 \
	-c 8192 \
	-p "Write a robust Python LRU cache with unit tests."
	```

	## Intended use

	- Code generation
	- Debugging
	- Code review
	- Implementation planning
	- Test generation
	- Software-engineering assistance

	## Limitations

	Tiny Pickle v3 Coder is experimental and has not yet been proven superior
	to its base model on independent benchmarks. Quantization may reduce model
	quality. Generated code can be incorrect, insecure, incomplete, or
	non-functional and must be reviewed and tested.