Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
open-r1
trl
sft
conversational
text-generation-inference
Instructions to use flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full") model = AutoModelForCausalLM.from_pretrained("flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full") 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 flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full
- SGLang
How to use flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full 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 "flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full" \ --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": "flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full", "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 "flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full" \ --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": "flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full with Docker Model Runner:
docker model run hf.co/flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full
Model save
Browse files- README.md +58 -0
- all_results.json +8 -0
- generation_config.json +9 -0
- train_results.json +8 -0
- trainer_state.json +0 -0
README.md
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---
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base_model: flyingbugs/Qwen2.5-Math-7B-Instruct
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library_name: transformers
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model_name: Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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# Model Card for Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full
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This model is a fine-tuned version of [flyingbugs/Qwen2.5-Math-7B-Instruct](https://huggingface.co/flyingbugs/Qwen2.5-Math-7B-Instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-random-perturbation-full", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jjh233/huggingface/runs/uuzxjit7)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.16.0.dev0
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- Transformers: 4.51.3
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- Pytorch: 2.5.1
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- Datasets: 3.5.1
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- Tokenizers: 0.21.1
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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all_results.json
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{
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"total_flos": 8.761222688732611e+18,
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"train_loss": 1.0859681145773188,
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"train_runtime": 27041.1536,
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"train_samples": 93733,
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"train_samples_per_second": 0.468,
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"train_steps_per_second": 0.029
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"transformers_version": "4.51.3"
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}
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train_results.json
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{
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"total_flos": 8.761222688732611e+18,
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"train_loss": 1.0859681145773188,
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"train_runtime": 27041.1536,
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"train_samples": 93733,
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"train_samples_per_second": 0.468,
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"train_steps_per_second": 0.029
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}
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trainer_state.json
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