Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
sft
trl
conversational
text-generation-inference
Instructions to use cs-552-2026-flab/math_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-flab/math_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-flab/math_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cs-552-2026-flab/math_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-flab/math_model", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cs-552-2026-flab/math_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cs-552-2026-flab/math_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cs-552-2026-flab/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-flab/math_model
- SGLang
How to use cs-552-2026-flab/math_model 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 "cs-552-2026-flab/math_model" \ --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": "cs-552-2026-flab/math_model", "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 "cs-552-2026-flab/math_model" \ --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": "cs-552-2026-flab/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-flab/math_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-flab/math_model
Commit History
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g2rpo-guided-ds4-8g-ckpt200-tuned-sampling-T0.6-topp1.0-topk-1
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g2rpo-guided-ds4-8g-ckpt100-beats-ckpt50-orig-sampling
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grpo-dsr-sft-8g-ckpt50-CI-validated-restore-orig-sampling
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g2rpo-ds4-ckpt120-tuned-sampling-T0.6-topp1.0-topk-1
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grpo-dsr-sft-8g-ckpt50-budgetRL
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093919-SFT-Lean+math-best-allround-eval-revert-from-exp6filt
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flab math/sft 20260606_143442
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push_best_a025_step250
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add_reasoning_boxed_template
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push_guided_a025_ckpt180
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flab math/sft 20260529_151029
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Remove training junk (checkpoint-*, hydra, training_args.bin); keep final model only
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flab math/sft 20260529_091547
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flab math model (sft 20260514_093821)
Co-authored-by: mraznichenko <mraznichenko@users.noreply.huggingface.co> Co-authored-by: amir-ammar <amir-ammar@users.noreply.huggingface.co>60856e7