Instructions to use bunnycore/Llama-3.2-3B-Mix-Skill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bunnycore/Llama-3.2-3B-Mix-Skill with Transformers:

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

pipe = pipeline("text-generation", model="bunnycore/Llama-3.2-3B-Mix-Skill")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bunnycore/Llama-3.2-3B-Mix-Skill")
model = AutoModelForCausalLM.from_pretrained("bunnycore/Llama-3.2-3B-Mix-Skill")
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 bunnycore/Llama-3.2-3B-Mix-Skill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bunnycore/Llama-3.2-3B-Mix-Skill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bunnycore/Llama-3.2-3B-Mix-Skill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bunnycore/Llama-3.2-3B-Mix-Skill

SGLang

How to use bunnycore/Llama-3.2-3B-Mix-Skill 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 "bunnycore/Llama-3.2-3B-Mix-Skill" \
    --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": "bunnycore/Llama-3.2-3B-Mix-Skill",
		"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 "bunnycore/Llama-3.2-3B-Mix-Skill" \
        --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": "bunnycore/Llama-3.2-3B-Mix-Skill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bunnycore/Llama-3.2-3B-Mix-Skill with Docker Model Runner:
```
docker model run hf.co/bunnycore/Llama-3.2-3B-Mix-Skill
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This language model is a merged version of several pre-trained models, designed to excel in roleplay, long-form question answering, and prompt following tasks. It was created using the TIES merge method with huihui-ai/Llama-3.2-3B-Instruct-abliterated as the base model.

Intended Use:

This model is suitable for a variety of applications, including:

Creative Writing: Generating stories, poems, scripts, and other forms of creative text.
Question Answering: Providing comprehensive and informative answers to a wide range of questions.
Role-Playing: Engaging in interactive role-playing scenarios with users.
Prompt Following: Completing tasks and generating text based on specific prompts or instructions.

merge

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

Merge Details

Merge Method

This model was merged using the TIES merge method using huihui-ai/Llama-3.2-3B-Instruct-abliterated 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: bunnycore/Llama-3.2-3B-Long-Think
    parameters:
      density: 0.5
      weight: 0.5
  - model: bunnycore/Llama-3.2-3B-Pure-RP
    parameters:
      density: 0.5
      weight: 0.5

merge_method: ties
base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated
parameters:
  normalize: false
  int8_mask: true
dtype: float16