Instructions to use FallenMerick/MN-Violet-Lotus-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FallenMerick/MN-Violet-Lotus-12B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FallenMerick/MN-Violet-Lotus-12B")
model = AutoModelForCausalLM.from_pretrained("FallenMerick/MN-Violet-Lotus-12B", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FallenMerick/MN-Violet-Lotus-12B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FallenMerick/MN-Violet-Lotus-12B

SGLang

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

Docker Model Runner
How to use FallenMerick/MN-Violet-Lotus-12B with Docker Model Runner:
```
docker model run hf.co/FallenMerick/MN-Violet-Lotus-12B
```

Independent benchmark: MN-Violet-Lotus-12B — 51.0% across 13 benchmarks

by mergekit-AI - opened 8 days ago

Discussion

mergekit-AI

8 days ago

Hi FallenMerick,

We recently evaluated FallenMerick/MN-Violet-Lotus-12B on the MergeKit Cloud benchmark suite.

The model completed all 13 benchmarks, covering 2,560 sampled questions, with a 90% confidence interval.

Overall score: 51.0%

Some of the strongest results were:

Global-MMLU-Lite — 70.0%
PubMedQA — 66.1%
Shopping MMLU — 65.3%
MATH-500 — 61.1%
MedQA — 57.0%
IFEval — 56.8%

Other results:

INCLUDE-lite-44 — 50.0%
CodeXGLUE — 50.0%
FinQA — 48.6%
MMLU-Pro — 48.5%
LegalBench (CUAD) — 46.3%
GPQA Diamond — 31.9%
BFCL V3 — 11.5%

We thought the capability distribution was particularly interesting, so we wanted to share the results with you and the community.

The evaluation was run through MergeKit Cloud's 13-benchmark evaluation system.

We're happy to provide the full evaluation details/results if useful.

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