Instructions to use suayptalha/Komodo-Llama-3.2-3B-v2-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suayptalha/Komodo-Llama-3.2-3B-v2-fp16 with Transformers:

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

pipe = pipeline("text-generation", model="suayptalha/Komodo-Llama-3.2-3B-v2-fp16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("suayptalha/Komodo-Llama-3.2-3B-v2-fp16")
model = AutoModelForMultimodalLM.from_pretrained("suayptalha/Komodo-Llama-3.2-3B-v2-fp16")
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 Settings

vLLM

How to use suayptalha/Komodo-Llama-3.2-3B-v2-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suayptalha/Komodo-Llama-3.2-3B-v2-fp16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suayptalha/Komodo-Llama-3.2-3B-v2-fp16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/suayptalha/Komodo-Llama-3.2-3B-v2-fp16

SGLang

How to use suayptalha/Komodo-Llama-3.2-3B-v2-fp16 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 "suayptalha/Komodo-Llama-3.2-3B-v2-fp16" \
    --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": "suayptalha/Komodo-Llama-3.2-3B-v2-fp16",
		"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 "suayptalha/Komodo-Llama-3.2-3B-v2-fp16" \
        --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": "suayptalha/Komodo-Llama-3.2-3B-v2-fp16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use suayptalha/Komodo-Llama-3.2-3B-v2-fp16 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for suayptalha/Komodo-Llama-3.2-3B-v2-fp16 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for suayptalha/Komodo-Llama-3.2-3B-v2-fp16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for suayptalha/Komodo-Llama-3.2-3B-v2-fp16 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="suayptalha/Komodo-Llama-3.2-3B-v2-fp16",
    max_seq_length=2048,
)

Docker Model Runner
How to use suayptalha/Komodo-Llama-3.2-3B-v2-fp16 with Docker Model Runner:
```
docker model run hf.co/suayptalha/Komodo-Llama-3.2-3B-v2-fp16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This version of Komodo is a Llama-3.2-3B-Instruct finetuned model on jeggers/competition_math dataset to increase math performance of the base model.

This model is fp16. You should import it using torch_dtype="float16".

Finetune system prompt:

You are a highly intelligent and accurate mathematical assistant.
You will solve mathematical problems step by step, explain your reasoning clearly, and provide concise, correct answers.
When the solution requires multiple steps, detail each step systematically.

You can use ChatML format!