Instructions to use rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean with Transformers:

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

pipe = pipeline("text-generation", model="rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean")
model = AutoModelForCausalLM.from_pretrained("rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean")
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 rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean

SGLang

How to use rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean 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 "rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean" \
    --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": "rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean",
		"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 "rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean" \
        --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": "rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean with Docker Model Runner:
```
docker model run hf.co/rkumar1999/Llama-3.1-8B-Instruct-Open-R1-Distill-Lean
```

Llama-3.1-8B-Instruct-Open-R1-Distill-Lean

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{
  "best_metric": null,
  "best_model_checkpoint": null,
  "epoch": 0,
  "eval_steps": 500,
  "global_step": 0,
  "is_hyper_param_search": false,
  "is_local_process_zero": true,
  "is_world_process_zero": true,
  "log_history": [
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      "train_samples_per_second": 2.287,
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  "logging_steps": 500,
  "max_steps": 20,
  "num_input_tokens_seen": 0,
  "num_train_epochs": 20,
  "save_steps": 500,
  "stateful_callbacks": {
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        "should_epoch_stop": false,
        "should_evaluate": false,
        "should_log": false,
        "should_save": false,
        "should_training_stop": false
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  "train_batch_size": 2,
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}