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

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metadata

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.

from transformers import AutoModelForCausalLM, AutoTokenizer

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