Instructions to use anthracite-org/magnum-v2-72b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anthracite-org/magnum-v2-72b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="anthracite-org/magnum-v2-72b-gguf",
	filename="magnum-v2-72b-IQ3_M.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 anthracite-org/magnum-v2-72b-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 anthracite-org/magnum-v2-72b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf anthracite-org/magnum-v2-72b-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 anthracite-org/magnum-v2-72b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf anthracite-org/magnum-v2-72b-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 anthracite-org/magnum-v2-72b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf anthracite-org/magnum-v2-72b-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 anthracite-org/magnum-v2-72b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf anthracite-org/magnum-v2-72b-gguf:Q4_K_M

Use Docker

docker model run hf.co/anthracite-org/magnum-v2-72b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use anthracite-org/magnum-v2-72b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anthracite-org/magnum-v2-72b-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": "anthracite-org/magnum-v2-72b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anthracite-org/magnum-v2-72b-gguf:Q4_K_M

Ollama
How to use anthracite-org/magnum-v2-72b-gguf with Ollama:
```
ollama run hf.co/anthracite-org/magnum-v2-72b-gguf:Q4_K_M
```

Unsloth Studio

How to use anthracite-org/magnum-v2-72b-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 anthracite-org/magnum-v2-72b-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 anthracite-org/magnum-v2-72b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for anthracite-org/magnum-v2-72b-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use anthracite-org/magnum-v2-72b-gguf with Docker Model Runner:
```
docker model run hf.co/anthracite-org/magnum-v2-72b-gguf:Q4_K_M
```

Lemonade

How to use anthracite-org/magnum-v2-72b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull anthracite-org/magnum-v2-72b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.magnum-v2-72b-gguf-Q4_K_M

List all available models

lemonade list

How to use from the

Use from the

llama-cpp-python library

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="anthracite-org/magnum-v2-72b-gguf",
	filename="",
)

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

This repo contains GGUF quants of the model. If you need the original weights, please find them here.

Q8_0 had a corrupted file upload, if you need q8 check out: mradermacher/magnum-v2-72b-GGUF

This is the seventh (Lucky!) in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of Qwen-2 72B Instruct.

Prompting

Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:

"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""

Credits

This model has been a team effort, and the credits goes to all members of Anthracite.

Training

The training was done for 2 epochs. We used 8x AMD Instinct™ MI300X Accelerators for the full-parameter fine-tuning of the model.

We also trained with a weight decay of 0.01 to help further stabilize the loss trajectory and mitigate catastrophic forgetting, and utilize a peak learning rate of 4e-6 to prevent the 2nd epoch loss from dropping too significantly (as it is a strong indicator of overfitting).