Instructions to use North-ML1/Forge-1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use North-ML1/Forge-1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="North-ML1/Forge-1-GGUF",
	filename="forge-1-f16.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 North-ML1/Forge-1-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 North-ML1/Forge-1-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf North-ML1/Forge-1-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf North-ML1/Forge-1-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf North-ML1/Forge-1-GGUF:F16

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 North-ML1/Forge-1-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf North-ML1/Forge-1-GGUF:F16

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 North-ML1/Forge-1-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf North-ML1/Forge-1-GGUF:F16

Use Docker

docker model run hf.co/North-ML1/Forge-1-GGUF:F16

LM Studio
Jan

vLLM

How to use North-ML1/Forge-1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "North-ML1/Forge-1-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": "North-ML1/Forge-1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/North-ML1/Forge-1-GGUF:F16

Ollama
How to use North-ML1/Forge-1-GGUF with Ollama:
```
ollama run hf.co/North-ML1/Forge-1-GGUF:F16
```

Unsloth Studio

How to use North-ML1/Forge-1-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 North-ML1/Forge-1-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 North-ML1/Forge-1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for North-ML1/Forge-1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use North-ML1/Forge-1-GGUF with Docker Model Runner:
```
docker model run hf.co/North-ML1/Forge-1-GGUF:F16
```

Lemonade

How to use North-ML1/Forge-1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull North-ML1/Forge-1-GGUF:F16

Run and chat with the model

lemonade run user.Forge-1-GGUF-F16

List all available models

lemonade list

Forge-1 GGUF

GGUF exports for North-ML1/Forge-1.

Files:

forge-1-f16.gguf - F16 GGUF export.
forge-1-q4_k_m.gguf - Q4_K_M quantized GGUF for practical local inference.

Source model: North-ML1/Forge-1, moved from North-ML1/Forge-1-Base.

Conversion notes:

Converted with llama.cpp from the Hugging Face safetensors export.
The model uses a locally trained ByteLevel BPE tokenizer. Current llama.cpp did not recognize its tokenizer hash, so the converter was patched to mark this ByteLevel BPE tokenizer as gpt-2 pre-tokenization.
A smoke test with llama-cli loaded forge-1-q4_k_m.gguf and answered 2 + 2 = 4.

Example:

llama-cli -m forge-1-q4_k_m.gguf -p '<|im_start|>user\nWhat is 2 + 2?<|im_end|>\n<|im_start|>assistant\n' -n 32 --temp 0

SHA256:

56b5f16b11e742e82874f11f5db20bc9be497793626989a5ed4000108773a87c  forge-1-f16.gguf
2272f246381687462abac21a8f9b8cda1d891fa0cdba22d45c16f77c7527c51c  forge-1-q4_k_m.gguf

Prompt Format

Forge-1 is ChatML-tuned. Use a ChatML prompt, for example:

<|im_start|>user
What is 2 + 2?<|im_end|>
<|im_start|>assistant

Plain completion prompts are not reliable for this checkpoint. If your UI has a chat-template selector, choose ChatML.

Downloads last month: 327

GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

Model tree for North-ML1/Forge-1-GGUF

Base model

North-ML1/Forge-1

Quantized

(1)

this model

Collection including North-ML1/Forge-1-GGUF

Forge 1

Collection

The series of stable coding models built by North ML. Optimized for speed, clarity and efficiency. • 4 items • Updated 8 days ago