Instructions to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF",
	filename="fastcontext-4b-rl_base-function-calling-xlam-unsloth.q2_k.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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": "ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M

Ollama
How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF with Ollama:
```
ollama run hf.co/ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
```

Unsloth Studio

How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF to start chatting

How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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": "ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 "ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-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 ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF with Docker Model Runner:
```
docker model run hf.co/ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
```

Lemonade

How to use ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF-Q4_K_M

List all available models

lemonade list

FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF

GGUF quantizations of FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth for CPU and edge inference with llama.cpp, Ollama, LM Studio, and other GGUF runtimes.

This model is a fine-tune of FastContext-1.0-4B-RL for function calling, trained with Unsloth on Salesforce/xlam-function-calling-60k.

Full-precision model: FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth
Base model: FastContext-1.0-4B-RL
Parameters: 4B

Available Quantizations

Quant	Size	Recommended Use	File
`Q2_K`	1.67 GB	Smallest, lowest quality — quick tests / very constrained devices	`fastcontext-4b-rl_base-function-calling-xlam-unsloth.q2_k.gguf`
`Q3_K_M`	2.08 GB	Small, acceptable quality	`fastcontext-4b-rl_base-function-calling-xlam-unsloth.q3_k_m.gguf`
`Q4_K_M`	2.50 GB	Recommended — best size/quality balance	`fastcontext-4b-rl_base-function-calling-xlam-unsloth.q4_k_m.gguf`
`Q5_K_M`	2.89 GB	High quality, larger	`fastcontext-4b-rl_base-function-calling-xlam-unsloth.q5_k_m.gguf`
`Q6_K`	3.31 GB	Very high quality, near-fp16	`fastcontext-4b-rl_base-function-calling-xlam-unsloth.q6_k.gguf`
`Q8_0`	4.28 GB	Near-lossless, largest	`fastcontext-4b-rl_base-function-calling-xlam-unsloth.q8_0.gguf`

Q4_K_M is the recommended default for most users.

Usage

Download a single quant

pip install -U "huggingface_hub[cli]"
hf download ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF \
  --include "*q4_k_m*.gguf" --local-dir ./FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF

llama.cpp

# build: https://github.com/ggerganov/llama.cpp
./llama-cli -m ./FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF/fastcontext-4b-rl_base-function-calling-xlam-unsloth.q2_k.gguf \
  -p "Check if the numbers 8 and 1233 are powers of two." -n 512

Ollama

ollama run hf.co/ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M "Check if the numbers 8 and 1233 are powers of two."

Training Outcome

Metric	Value
SLURM Job ID	`45169150`
Runtime	2h 09m 11s
Final Training Loss	0.2303
Peak VRAM	14.52 GB
GPU	H100 80GB HBM3 (MIG 3g.40gb)

License

MIT — see the base model for full terms.

Acknowledgments

Unsloth for 2x faster fine-tuning
llama.cpp for GGUF quantization
Base model developers (microsoft)
Compute Canada / DRAC for HPC resources

Downloads last month: 339

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF

Base model

microsoft/FastContext-1.0-4B-RL

Adapter

ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth

Quantized

(1)

this model

ermiaazarkhalili
/

FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF

FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF

Available Quantizations

Usage

Download a single quant

llama.cpp

Ollama

Training Outcome

License

Acknowledgments

Model tree for ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF

Dataset used to train ermiaazarkhalili/FastContext-4B-RL_base-Function-Calling-xLAM-Unsloth-GGUF