Instructions to use inflatebot/MN-12B-Mag-Mell-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inflatebot/MN-12B-Mag-Mell-R1 with Transformers:

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

pipe = pipeline("text-generation", model="inflatebot/MN-12B-Mag-Mell-R1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inflatebot/MN-12B-Mag-Mell-R1")
model = AutoModelForCausalLM.from_pretrained("inflatebot/MN-12B-Mag-Mell-R1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inflatebot/MN-12B-Mag-Mell-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inflatebot/MN-12B-Mag-Mell-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inflatebot/MN-12B-Mag-Mell-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inflatebot/MN-12B-Mag-Mell-R1

SGLang

How to use inflatebot/MN-12B-Mag-Mell-R1 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 "inflatebot/MN-12B-Mag-Mell-R1" \
    --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": "inflatebot/MN-12B-Mag-Mell-R1",
		"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 "inflatebot/MN-12B-Mag-Mell-R1" \
        --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": "inflatebot/MN-12B-Mag-Mell-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inflatebot/MN-12B-Mag-Mell-R1 with Docker Model Runner:
```
docker model run hf.co/inflatebot/MN-12B-Mag-Mell-R1
```

really good

by JoggyMuffin - opened Nov 18, 2024

Discussion

JoggyMuffin

Nov 18, 2024

Somehow this manages to be preferable over much larger models (even 34b), and I find myself consistently surprised the model is so tiny. If you could find a way to replicate this models attention to detail, narrative pacing, and novel use of literary techniques on a larger scale, it would be huge. I find that most big models I've tried are smart, but sound very very similar one interaction to the next, with a level of detachment and formulaic use of dialogue and cliche metaphors. While this model does have the occasional tired phrase, it makes up for it by an apparent engagement with the content of the story and 'taking risks'. Characters get angry when appropriate, speak differently depending on their personality (as opposed to having maybe five different personalities that most models seem to mix and match), and strong instruction following.

TLDR A great model for its size, and if something similar can be done with a smarter model while retaining its literate novelty, I would be ecstatic.

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