Instructions to use samuelfaj/distill2-0.6B-4bit-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samuelfaj/distill2-0.6B-4bit-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="samuelfaj/distill2-0.6B-4bit-GGUF",
	filename="Users/samuelfajreldines/dev/distill-lora/models/distill2-0.6B-fp16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use samuelfaj/distill2-0.6B-4bit-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf samuelfaj/distill2-0.6B-4bit-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 samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf samuelfaj/distill2-0.6B-4bit-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 samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M

Use Docker

docker model run hf.co/samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use samuelfaj/distill2-0.6B-4bit-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "samuelfaj/distill2-0.6B-4bit-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": "samuelfaj/distill2-0.6B-4bit-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M

Ollama
How to use samuelfaj/distill2-0.6B-4bit-GGUF with Ollama:
```
ollama run hf.co/samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M
```

Unsloth Studio new

How to use samuelfaj/distill2-0.6B-4bit-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 samuelfaj/distill2-0.6B-4bit-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 samuelfaj/distill2-0.6B-4bit-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for samuelfaj/distill2-0.6B-4bit-GGUF to start chatting

Pi new

How to use samuelfaj/distill2-0.6B-4bit-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf samuelfaj/distill2-0.6B-4bit-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": "samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use samuelfaj/distill2-0.6B-4bit-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf samuelfaj/distill2-0.6B-4bit-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 samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use samuelfaj/distill2-0.6B-4bit-GGUF with Docker Model Runner:
```
docker model run hf.co/samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M
```

Lemonade

How to use samuelfaj/distill2-0.6B-4bit-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull samuelfaj/distill2-0.6B-4bit-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.distill2-0.6B-4bit-GGUF-Q4_K_M

List all available models

lemonade list

distill2-0.6B — Expert Language Model for CLI Output (GGUF)

distill2-0.6B is the second-generation domain-specific Expert Language Model for CLI output compression and classification — in GGUF format for cross-platform use with llama.cpp.

See distill2-0.6B-4bit-MLX for the MLX (Apple Silicon) version.

What is distill?

distill compresses arbitrary command-line output to structured summaries.

Input:  500 lines of npm install logs
Output: PASS — 24 packages installed, 0 vulnerabilities

distill2-0.6B achieves 98.4% accuracy at 0.6B parameters — outperforming its 1.7B predecessor.

Files

File	Format	Size	Use case
`distill2-0.6B-Q4_K_M.gguf`	Q4_K_M (4-bit)	378 MB	Production, low memory
`distill2-0.6B-fp16.gguf`	fp16	1.2 GB	Maximum quality

Performance

Metric	Value
Overall accuracy	98.4%
Tasks at 100%	5 of 8
Tasks ≥95%	7 of 8
Base model	Qwen3-0.6B
Training	QLoRA 4-bit + GGUF conversion

8 Specialized Tasks

Task	Accuracy	Description
`pass_fail`	100%	Command success/failure
`safe_review`	100%	Terraform plan safety
`json_extraction`	100%	JSON from noisy logs
`test_result`	100%	Test suite pass/fail
`typescript_check`	100%	TS compiler errors
`terraform_plan`	98.4%	Resource change counts
`security_audit`	96.6%	Vulnerability counts
`generic`	93.1%	Free-form CLI summaries

Usage (llama.cpp)

# Download
huggingface-cli download samuelfaj/distill2-0.6B-4bit-GGUF distill2-0.6B-Q4_K_M.gguf --local-dir .

# Run with llama-cli
llama-cli -m distill2-0.6B-Q4_K_M.gguf -p "Command output: npm test\n4 passed, 0 failed"

# Or as server
llama-server -m distill2-0.6B-Q4_K_M.gguf --port 8080