Instructions to use Nohobby/MN-12B-Siskin-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nohobby/MN-12B-Siskin-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="Nohobby/MN-12B-Siskin-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nohobby/MN-12B-Siskin-v0.2")
model = AutoModelForCausalLM.from_pretrained("Nohobby/MN-12B-Siskin-v0.2")
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 Nohobby/MN-12B-Siskin-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nohobby/MN-12B-Siskin-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nohobby/MN-12B-Siskin-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nohobby/MN-12B-Siskin-v0.2

SGLang

How to use Nohobby/MN-12B-Siskin-v0.2 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 "Nohobby/MN-12B-Siskin-v0.2" \
    --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": "Nohobby/MN-12B-Siskin-v0.2",
		"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 "Nohobby/MN-12B-Siskin-v0.2" \
        --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": "Nohobby/MN-12B-Siskin-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nohobby/MN-12B-Siskin-v0.2 with Docker Model Runner:
```
docker model run hf.co/Nohobby/MN-12B-Siskin-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Siskin 0.2

The 0.1 version's writing is more "human" — check it out here

Overview

Somewhat experimental merge of some Nemo models. Consists mostly of human data, so there should be very little gptisms/claudisms. Models work with about ~28k context and could maybe be stretched further.

Note: Both v0.1 and v0.2 can write in the first person despite the initial message. Specify the narrative style somewhere in the prompt to fix this if you don't like it.

Prompt template: Mistral

<s>[INST] {input} [/INST] {output}</s>

Quants

Static

Imatrix

Merge Details

Merge Method

This model was merged using the della_linear merge method using ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1
    parameters:
      weight: [0.2, 0.3, 0.2, 0.3, 0.2]
      density: [0.45, 0.55, 0.45, 0.55, 0.45]
  - model: NeverSleep/Lumimaid-v0.2-12B
    parameters:
      weight: [0.165, 0.295, 0.295, 0.165, 0.165, 0.295, 0.295, 0.165]
      density: [0.5]
  - model: elinas/Chronos-Gold-12B-1.0
    parameters:
      weight: [0.01768, -0.01675, 0.01285, -0.01696, 0.01421]
      density: [0.6, 0.4, 0.5, 0.4, 0.6]
  - model: v000000/NM-12B-Lyris-dev-3
    parameters:
      weight: [0.208, 0.139, 0.139, 0.139, 0.208]
      density: [0.7]
  - model: nbeerbower/Lyra-Gutenberg-mistral-nemo-12B
    parameters:
      weight: [0.33]
      density: [0.45, 0.55, 0.45, 0.55, 0.45]
merge_method: della_linear
base_model: ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1
parameters:
  epsilon: 0.04
  lambda: 1.05
  int8_mask: true
  rescale: true
  normalize: false
dtype: bfloat16
tokenizer_source: base