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
```

Awesome

by Freezer91 - opened Nov 25, 2024

Discussion

Freezer91

Nov 25, 2024

This is actually my fav 12B model, it perfectly keeps the characters true to themselves and has even surprised me with good introspection about how the characters feel in certain situations which is a really important trait. I also noticed that it resorts to repetitive patterns far less frequently, which has always plagued my experience with other models until now. Moreover, so far, it hasn’t displayed that excessive positivity that often ends up bordering on naivety. Top 12B here, very impressive work!

pragueyemosvoto

Nov 30, 2024

i think everyone can confidently say that they cannot wait for another inflatebot model

inflatebot

Owner Dec 1, 2024

you guys are really too kind, wow
I've been working on learning finetunes recently, and collaborating with my friends at Allura (and my subgroup, Alfitaria), and as such (combined with some personal-life stress and health nonsense) I've been a bit preoccupied.
My current concept is basically to do Veo Lu but with a larger, more stable base (and a version of the Fujin dataset that I have meticulously cleaned of symbol/link spam.)

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment