Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
open-r1
trl
grpo
conversational
text-generation-inference
Instructions to use ununtrium/Qwen2.5-1.5B-Open-R1-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ununtrium/Qwen2.5-1.5B-Open-R1-GRPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ununtrium/Qwen2.5-1.5B-Open-R1-GRPO") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ununtrium/Qwen2.5-1.5B-Open-R1-GRPO") model = AutoModelForCausalLM.from_pretrained("ununtrium/Qwen2.5-1.5B-Open-R1-GRPO", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ununtrium/Qwen2.5-1.5B-Open-R1-GRPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ununtrium/Qwen2.5-1.5B-Open-R1-GRPO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ununtrium/Qwen2.5-1.5B-Open-R1-GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ununtrium/Qwen2.5-1.5B-Open-R1-GRPO
- SGLang
How to use ununtrium/Qwen2.5-1.5B-Open-R1-GRPO 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 "ununtrium/Qwen2.5-1.5B-Open-R1-GRPO" \ --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": "ununtrium/Qwen2.5-1.5B-Open-R1-GRPO", "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 "ununtrium/Qwen2.5-1.5B-Open-R1-GRPO" \ --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": "ununtrium/Qwen2.5-1.5B-Open-R1-GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ununtrium/Qwen2.5-1.5B-Open-R1-GRPO with Docker Model Runner:
docker model run hf.co/ununtrium/Qwen2.5-1.5B-Open-R1-GRPO
Download trainer_state.json from ununtrium/Qwen2.5-1.5B-Open-R1-GRPO: direct link, hf CLI and curl.
- Browser
- Download file 4.53 kB
-
https://huggingface.co/ununtrium/Qwen2.5-1.5B-Open-R1-GRPO/resolve/main/trainer_state.json
- Command line
-
hf download hf://ununtrium/Qwen2.5-1.5B-Open-R1-GRPO/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/ununtrium/Qwen2.5-1.5B-Open-R1-GRPO/resolve/main/trainer_state.json
4.53 kB
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.9884169884169884, | |
| "eval_steps": 100, | |
| "global_step": 32, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "completion_length": 291.36697788238524, | |
| "epoch": 0.15444015444015444, | |
| "grad_norm": 91.9554297295678, | |
| "kl": 0.3540959358215332, | |
| "learning_rate": 1.9937122098932428e-05, | |
| "loss": 0.0142, | |
| "reward": 0.6166573823895305, | |
| "reward_std": 0.452694922266528, | |
| "rewards/accuracy_reward": 0.10803571995347738, | |
| "rewards/cosine_scaled_reward": -0.10179503666004167, | |
| "rewards/format_reward": 0.5000000233761966, | |
| "rewards/reasoning_steps_reward": 0.110416672937572, | |
| "step": 5 | |
| }, | |
| { | |
| "completion_length": 43.16875188350677, | |
| "epoch": 0.3088803088803089, | |
| "grad_norm": 3.33245795174105, | |
| "kl": 0.8302490234375, | |
| "learning_rate": 1.78183148246803e-05, | |
| "loss": 0.0332, | |
| "reward": 0.9400907799601554, | |
| "reward_std": 0.19169574869779354, | |
| "rewards/accuracy_reward": 0.024107144121080636, | |
| "rewards/cosine_scaled_reward": 0.0037812213704455644, | |
| "rewards/format_reward": 0.9107143148779869, | |
| "rewards/reasoning_steps_reward": 0.0014880953822284937, | |
| "step": 10 | |
| }, | |
| { | |
| "completion_length": 15.207143580913543, | |
| "epoch": 0.46332046332046334, | |
| "grad_norm": 0.8155119970711466, | |
| "kl": 1.6966796875, | |
| "learning_rate": 1.3302790619551673e-05, | |
| "loss": 0.0679, | |
| "reward": 0.9947075441479682, | |
| "reward_std": 0.005296425729534348, | |
| "rewards/accuracy_reward": 0.0, | |
| "rewards/cosine_scaled_reward": -0.0017210765145136975, | |
| "rewards/format_reward": 0.9964285731315613, | |
| "rewards/reasoning_steps_reward": 0.0, | |
| "step": 15 | |
| }, | |
| { | |
| "completion_length": 14.96160796880722, | |
| "epoch": 0.6177606177606177, | |
| "grad_norm": 0.5795656668267815, | |
| "kl": 1.59228515625, | |
| "learning_rate": 7.774790660436857e-06, | |
| "loss": 0.0637, | |
| "reward": 0.9939029954373837, | |
| "reward_std": 0.006596084152567983, | |
| "rewards/accuracy_reward": 0.0, | |
| "rewards/cosine_scaled_reward": -0.0016327791643561795, | |
| "rewards/format_reward": 0.9946428596973419, | |
| "rewards/reasoning_steps_reward": 0.0008928571827709675, | |
| "step": 20 | |
| }, | |
| { | |
| "completion_length": 14.393750703334808, | |
| "epoch": 0.7722007722007722, | |
| "grad_norm": 4.257970780643175, | |
| "kl": 1.46865234375, | |
| "learning_rate": 2.9289321881345257e-06, | |
| "loss": 0.0588, | |
| "reward": 0.989137577265501, | |
| "reward_std": 0.013341282614055672, | |
| "rewards/accuracy_reward": 0.0, | |
| "rewards/cosine_scaled_reward": -0.0019339090387802571, | |
| "rewards/format_reward": 0.9910714328289032, | |
| "rewards/reasoning_steps_reward": 0.0, | |
| "step": 25 | |
| }, | |
| { | |
| "completion_length": 13.3160719871521, | |
| "epoch": 0.9266409266409267, | |
| "grad_norm": 0.5366189541461951, | |
| "kl": 1.44560546875, | |
| "learning_rate": 2.507208781817638e-07, | |
| "loss": 0.0578, | |
| "reward": 0.9885662972927094, | |
| "reward_std": 0.014164349375778328, | |
| "rewards/accuracy_reward": 0.0, | |
| "rewards/cosine_scaled_reward": -0.0016123341440106743, | |
| "rewards/format_reward": 0.9901785761117935, | |
| "rewards/reasoning_steps_reward": 0.0, | |
| "step": 30 | |
| }, | |
| { | |
| "completion_length": 12.924107730388641, | |
| "epoch": 0.9884169884169884, | |
| "kl": 1.577392578125, | |
| "reward": 0.9940091818571091, | |
| "reward_std": 0.006472988553753112, | |
| "rewards/accuracy_reward": 0.0, | |
| "rewards/cosine_scaled_reward": -0.0015265825350070372, | |
| "rewards/format_reward": 0.9955357164144516, | |
| "rewards/reasoning_steps_reward": 0.0, | |
| "step": 32, | |
| "total_flos": 0.0, | |
| "train_loss": 0.049734286265447736, | |
| "train_runtime": 1467.0643, | |
| "train_samples_per_second": 2.469, | |
| "train_steps_per_second": 0.022 | |
| } | |
| ], | |
| "logging_steps": 5, | |
| "max_steps": 32, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": false, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 0.0, | |
| "train_batch_size": 2, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |