Instructions to use GRRNMAKE/Magnus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GRRNMAKE/Magnus with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GRRNMAKE/Magnus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GRRNMAKE/Magnus")
model = AutoModelForCausalLM.from_pretrained("GRRNMAKE/Magnus", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GRRNMAKE/Magnus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GRRNMAKE/Magnus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GRRNMAKE/Magnus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GRRNMAKE/Magnus

SGLang

How to use GRRNMAKE/Magnus with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GRRNMAKE/Magnus" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GRRNMAKE/Magnus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GRRNMAKE/Magnus" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GRRNMAKE/Magnus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GRRNMAKE/Magnus with Docker Model Runner:
```
docker model run hf.co/GRRNMAKE/Magnus
```

Magnus / README.md

GRRNMAKER

Update model card with evaluation metrics and training details

e2bd3ea verified 2 months ago

preview code

Raw

History Blame Contribute Delete

1.44 kB


	---
	base_model: mistralai/Mistral-7B-Instruct-v0.3
	library_name: transformers
	tags:
	- tool-calling
	- custom-finetune
	- axim-alignment
	datasets:
	- GRRNMAKER/axim-alignment-data
	---

	# Model Card for GRRNMAKE/Magnus

	Magnus is a fine-tuned iteration of the Mistral-7B-Instruct-v0.3 model, specifically optimized for rigorous tool calling, precise formatting adherence, and extreme conciseness.

	## Training Details
	- Base Model: `mistralai/Mistral-7B-Instruct-v0.3`
	- Dataset: `GRRNMAKER/axim-alignment-data` (Axim Corrective Benchmark)
	- Training Method: 4-bit QLoRA natively merged into the base architecture.
	- Hardware: Lambda Instance (NVIDIA GPU)

	## Evaluation Results
	This model was evaluated against the rigorous Axim Corrective Benchmark targeting tool-calling performance and formatting fidelity.

	\| Metric \| Score \| Note \|
	\|---\|---\|---\|
	\| Concise Response Accuracy \| 98.200 \| Axim Alignment Dataset \|
	\| Formatting Adherence (ROUGE-L) \| Verified \| Axim Alignment Dataset \|
	\| Instruction Adherence \| High \| \|
	\| Hallucination Rate \| Low \| \|
	\| Conciseness Score \| 94.00 \| Local Verification Subset \|

	## Usage
	The model natively supports the standard Mistral v0.3 tool-calling chat template.

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("GRRNMAKE/Magnus")
	tokenizer = AutoTokenizer.from_pretrained("GRRNMAKE/Magnus")
	```