Text Classification
Transformers
Safetensors
English
qwen2
feature-extraction
prm
process-reward-model
reward model
math
hallucination-detection
custom_code
text-embeddings-inference
Instructions to use ZaandaTeika/Qwen2.5-Math-1.5B-SHARP-Step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZaandaTeika/Qwen2.5-Math-1.5B-SHARP-Step with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZaandaTeika/Qwen2.5-Math-1.5B-SHARP-Step", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ZaandaTeika/Qwen2.5-Math-1.5B-SHARP-Step", trust_remote_code=True) model = AutoModel.from_pretrained("ZaandaTeika/Qwen2.5-Math-1.5B-SHARP-Step", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- reward model
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- math
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- hallucination-detection
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base_model:
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- Qwen/Qwen2.5-Math-1.5B-Instruct
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datasets:
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step_reward = make_step_rewards(outputs[0], token_masks)
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print(step_reward) # one score per step
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```
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- reward model
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- math
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- hallucination-detection
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-Math-1.5B-Instruct
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datasets:
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step_reward = make_step_rewards(outputs[0], token_masks)
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print(step_reward) # one score per step
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```
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## Source Model and Attribution
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This checkpoint is a fine-tuned derivative of
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[Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct),
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released under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license.
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The weights were modified: the language modelling head was replaced with a two-way
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process reward head, and the model was further trained on span-derived step-level
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supervision.
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Training data comes from the [SHARP](https://huggingface.co/datasets/ZaandaTeika/SHARP)
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corpus, whose reasoning traces and hallucination annotations are licensed under
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CC BY 4.0. SHARP builds on GSM8K and MATH (both MIT); see the dataset card for the
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full source attribution.
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## Additional Information
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### Licensing Information
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This checkpoint is released under the
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[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license, following its
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base model.
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