Instructions to use suayptalha/Maestro-R1-Llama-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suayptalha/Maestro-R1-Llama-8B with Transformers:

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

pipe = pipeline("text-generation", model="suayptalha/Maestro-R1-Llama-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suayptalha/Maestro-R1-Llama-8B")
model = AutoModelForCausalLM.from_pretrained("suayptalha/Maestro-R1-Llama-8B", 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 suayptalha/Maestro-R1-Llama-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suayptalha/Maestro-R1-Llama-8B"
# 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/Maestro-R1-Llama-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/suayptalha/Maestro-R1-Llama-8B

SGLang

How to use suayptalha/Maestro-R1-Llama-8B 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/Maestro-R1-Llama-8B" \
    --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/Maestro-R1-Llama-8B",
		"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/Maestro-R1-Llama-8B" \
        --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/Maestro-R1-Llama-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use suayptalha/Maestro-R1-Llama-8B with Docker Model Runner:
```
docker model run hf.co/suayptalha/Maestro-R1-Llama-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Created by suayptalha →

Model Information

Maestro-R1-Llama-8B

Maestro-R1-Llama-8B deepseek-ai/DeepSeek-R1-Distill-Llama-8B 8B Parameters

Base Model

DeepSeek-R1-Distill-Llama-8B

Maestro-R1-Llama-8B is a powerful language model fine-tuned from DeepSeek-R1-Distill-Llama-8B, a distilled model based on the Llama-3 architecture. DeepSeek-R1-Distill-Llama-8B itself is derived from the Llama-3 architecture, with a distillation process from DeepSeek-R1, utilizing a large corpus of diverse data. This distillation enables the model to retain strong reasoning capabilities while maintaining a smaller parameter count.
Maestro-R1-Llama-8B builds on this foundation, further enhancing its performance through fine-tuning on the ServiceNow-AI/R1-Distill-SFT dataset. This fine-tuning step sharpens the model's ability to handle specialized tasks and improves its reasoning, problem-solving, and code generation capabilities. The combination of the distilled base model and domain-specific fine-tuning makes Maestro-R1-Llama-8B an efficient and robust model, excelling across a wide range of language tasks.
DeepSeek-R1 Paper Link: https://arxiv.org/abs/2501.12948