Instructions to use cs-552-2026-baseline/math_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-552-2026-baseline/math_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cs-552-2026-baseline/math_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cs-552-2026-baseline/math_model") model = AutoModelForCausalLM.from_pretrained("cs-552-2026-baseline/math_model") 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 cs-552-2026-baseline/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-baseline/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-baseline/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cs-552-2026-baseline/math_model
- SGLang
How to use cs-552-2026-baseline/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-baseline/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-baseline/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-baseline/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-baseline/math_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use cs-552-2026-baseline/math_model with Docker Model Runner:
docker model run hf.co/cs-552-2026-baseline/math_model
Update Automated MNLP evaluation report (2026-05-03)
Browse files- EVAL_REPORT.md +1 -12
EVAL_REPORT.md
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- **Model repo:** [`cs-552-2026-taadmin/math_model`](https://huggingface.co/cs-552-2026-taadmin/math_model)
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- **Owner(s):** group **taadmin**
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- **Generated at:** 2026-05-03T02:
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- **Pipeline:** [mnlp-project-ci](https://github.com/eric11eca/mnlp-project-ci)
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_This PR is opened automatically by the course CI. It is **non-blocking** — you do not need to merge it. The next nightly run will refresh this file._
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| Multilingual | `pass@1` | — | — | not run |
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## Per-source breakdown
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### Math
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| `HuggingFaceH4/MATH-500` | `pass@8` | 0.6000 | 5 |
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| `MathArena/aime_2025` | `pass@8` | 0.0000 | 2 |
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| `MathArena/aime_2026` | `pass@8` | 0.0000 | 1 |
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| `MathArena/hmmt_feb_2026` | `pass@8` | 0.0000 | 2 |
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## Sample completions
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### Math
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- **Model repo:** [`cs-552-2026-taadmin/math_model`](https://huggingface.co/cs-552-2026-taadmin/math_model)
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- **Owner(s):** group **taadmin**
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- **Generated at:** 2026-05-03T02:20:59+00:00 (UTC)
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- **Pipeline:** [mnlp-project-ci](https://github.com/eric11eca/mnlp-project-ci)
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_This PR is opened automatically by the course CI. It is **non-blocking** — you do not need to merge it. The next nightly run will refresh this file._
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| Multilingual | `pass@1` | — | — | not run |
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| Safety | `pass@1` | — | — | not run |
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## Sample completions
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### Math
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