Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
trl
grpo
conversational
text-generation-inference
Instructions to use jorbix/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 jorbix/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="jorbix/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("jorbix/Qwen2.5-1.5B-Open-R1-GRPO") model = AutoModelForCausalLM.from_pretrained("jorbix/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 jorbix/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 "jorbix/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": "jorbix/Qwen2.5-1.5B-Open-R1-GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jorbix/Qwen2.5-1.5B-Open-R1-GRPO
- SGLang
How to use jorbix/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 "jorbix/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": "jorbix/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 "jorbix/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": "jorbix/Qwen2.5-1.5B-Open-R1-GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jorbix/Qwen2.5-1.5B-Open-R1-GRPO with Docker Model Runner:
docker model run hf.co/jorbix/Qwen2.5-1.5B-Open-R1-GRPO
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.8888888888888888, | |
| "eval_steps": 500, | |
| "global_step": 6, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "clip_ratio": 0.0, | |
| "completion_length": 49.59999942779541, | |
| "epoch": 0.14814814814814814, | |
| "grad_norm": 0.796875, | |
| "kl": 0.0, | |
| "learning_rate": 2e-05, | |
| "loss": 0.0275, | |
| "reward": 0.008593750128056854, | |
| "reward_std": 0.034375000512227416, | |
| "rewards/format_reward": 0.0, | |
| "rewards/reward_asserty_verify_with_z3_v2": 0.007812500116415322, | |
| "rewards/tag_count_reward": 0.0007812500116415322, | |
| "step": 1 | |
| }, | |
| { | |
| "clip_ratio": 0.0, | |
| "completion_length": 47.14687633514404, | |
| "epoch": 0.2962962962962963, | |
| "grad_norm": 2.390625, | |
| "kl": 0.0, | |
| "learning_rate": 1.8090169943749477e-05, | |
| "loss": 0.0268, | |
| "reward": 0.03593750135041773, | |
| "reward_std": 0.11172563303261995, | |
| "rewards/format_reward": 0.0, | |
| "rewards/reward_asserty_verify_with_z3_v2": 0.032812500139698386, | |
| "rewards/tag_count_reward": 0.0031250000465661287, | |
| "step": 2 | |
| }, | |
| { | |
| "clip_ratio": 0.0, | |
| "completion_length": 43.42812538146973, | |
| "epoch": 0.4444444444444444, | |
| "grad_norm": 112.5, | |
| "kl": 5.2669981345534325, | |
| "learning_rate": 1.3090169943749475e-05, | |
| "loss": 0.1044, | |
| "reward": 0.2042931616306305, | |
| "reward_std": 0.15884361974895, | |
| "rewards/format_reward": 0.0, | |
| "rewards/reward_asserty_verify_with_z3_v2": 0.1925744116306305, | |
| "rewards/tag_count_reward": 0.011718750291038305, | |
| "step": 3 | |
| }, | |
| { | |
| "clip_ratio": 0.0, | |
| "completion_length": 248.70000076293945, | |
| "epoch": 0.5925925925925926, | |
| "grad_norm": 54.75, | |
| "kl": 1.006903812289238, | |
| "learning_rate": 6.909830056250527e-06, | |
| "loss": -0.018, | |
| "reward": 0.17103422805666924, | |
| "reward_std": 0.2909920923411846, | |
| "rewards/format_reward": 0.015625000232830644, | |
| "rewards/reward_asserty_verify_with_z3_v2": 0.08119047619402409, | |
| "rewards/tag_count_reward": 0.07421874813735485, | |
| "step": 4 | |
| }, | |
| { | |
| "clip_ratio": 0.0, | |
| "completion_length": 372.0031318664551, | |
| "epoch": 0.7407407407407407, | |
| "grad_norm": 7.4375, | |
| "kl": 0.1897900253534317, | |
| "learning_rate": 1.9098300562505266e-06, | |
| "loss": -0.1511, | |
| "reward": 0.18147081695497036, | |
| "reward_std": 0.2571421265602112, | |
| "rewards/format_reward": 0.009375000139698386, | |
| "rewards/reward_asserty_verify_with_z3_v2": 0.1197520662099123, | |
| "rewards/tag_count_reward": 0.052343751303851604, | |
| "step": 5 | |
| }, | |
| { | |
| "clip_ratio": 0.0, | |
| "completion_length": 331.5047149658203, | |
| "epoch": 0.8888888888888888, | |
| "grad_norm": 2.109375, | |
| "kl": 0.14362791739404202, | |
| "learning_rate": 0.0, | |
| "loss": -0.0532, | |
| "reward": 0.1194010404869914, | |
| "reward_std": 0.2147055435925722, | |
| "rewards/format_reward": 0.0031250000465661287, | |
| "rewards/reward_asserty_verify_with_z3_v2": 0.049869792885147035, | |
| "rewards/tag_count_reward": 0.06640625, | |
| "step": 6 | |
| }, | |
| { | |
| "epoch": 0.8888888888888888, | |
| "step": 6, | |
| "total_flos": 0.0, | |
| "train_loss": -0.010596248631676039, | |
| "train_runtime": 239.2366, | |
| "train_samples_per_second": 0.556, | |
| "train_steps_per_second": 0.025 | |
| } | |
| ], | |
| "logging_steps": 1, | |
| "max_steps": 6, | |
| "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": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 0.0, | |
| "train_batch_size": 16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |