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

Libraries

How to use matchaaaaa/MN-Tiramisu-12B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("matchaaaaa/MN-Tiramisu-12B")
model = AutoModelForCausalLM.from_pretrained("matchaaaaa/MN-Tiramisu-12B")
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

vLLM

How to use matchaaaaa/MN-Tiramisu-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matchaaaaa/MN-Tiramisu-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": "matchaaaaa/MN-Tiramisu-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/matchaaaaa/MN-Tiramisu-12B

SGLang

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

Docker Model Runner
How to use matchaaaaa/MN-Tiramisu-12B with Docker Model Runner:
```
docker model run hf.co/matchaaaaa/MN-Tiramisu-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MN-Tiramisu-12B

This is a really yappity-yappy yapping model that's good for long-form RP. Tried to rein it in with Mahou and give it some more character understanding with Pantheon. Feedback is always welcome.

Native Context Length: 16K/16384 (can be extended using RoPE, YMMV)

Prompt Template: ChatML

<|im_start|>system
{system prompt}<|im_end|>
<|im_start|>user
{message}<|im_end|>
<|im_start|>assistant
{response}

Recommended Settings:

Here are some settings ranges that tend to work for me. They aren't strict values, and there's a bit of leeway in them. Feel free to experiment a bit!

Temperature: 1.0 (maybe less, a little bit goes a long way with Nemo)
Min-P: 0.1 to 0.2
(all other samplers disabled)

Merge Details

This is a merge of pre-trained language models created using mergekit.

Merge Method

This model was merged using the linear DARE merge method using flammenai/Mahou-1.3-mistral-nemo-12B as a base.

Models Merged

The following models were included in the merge:

nbeerbower/mistral-nemo-gutenberg-12B-v4
Sao10K/MN-12B-Lyra-v1
Gryphe/Pantheon-RP-1.5-12b-Nemo
flammenai/Mahou-1.3-mistral-nemo-12B

Configuration

The following YAML configuration was used to produce this model:

base_model: flammenai/Mahou-1.3-mistral-nemo-12B
dtype: bfloat16
merge_method: dare_linear
slices:
- sources:
  - layer_range: [0, 40]
    model: Gryphe/Pantheon-RP-1.5-12b-Nemo
    parameters:
      weight: [0.45, 0.35, 0.35, 0.2, 0.2]
  - layer_range: [0, 40]
    model: Sao10K/MN-12B-Lyra-v1
    parameters:
      weight: [0.25, 0.3, 0.35, 0.3, 0.2]
  - layer_range: [0, 40]
    model: nbeerbower/mistral-nemo-gutenberg-12B-v4
    parameters:
      weight:
      - filter: mlp
        value: [0.1, 0.2, 0.1, 0.4, 0.5]
      - value: [0.1, 0.2, 0.1, 0.2, 0.2]
  - layer_range: [0, 40]
    model: flammenai/Mahou-1.3-mistral-nemo-12B
    parameters:
      weight:
      - filter: mlp
        value: [0.2, 0.15, 0.2, 0.1, 0.1]
      - value: [0.2, 0.15, 0.2, 0.3, 0.4]
tokenizer_source: union

Benchmarks (or Benchmark because I tried only one)

I ran EQ bench from EleutherAI's lm-evaluation-harness (thank you @FallenMerick).

| Tasks  |Version|Filter|n-shot|     Metric      |   | Value  |   |Stderr|
|--------|------:|------|-----:|-----------------|---|-------:|---|-----:|
|eq_bench|    2.1|none  |     0|eqbench          |↑  | 79.3617|±  | 1.637|
|        |       |none  |     0|percent_parseable|↑  |100.0000|±  | 0.000|

And as always, have a great day!