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library_name: transformers
tags:
- trl
- sft
datasets:
- qwedsacf/grade-school-math-instructions
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
base_model:
- Qwen/Qwen2.5-3B
---
<img src="https://huggingface.co/entfane/math-professor-3B/resolve/main/math-professor-image.png" width="300" height="300"/>
# Math Professor 3B
This model is a math instruction fine-tuned version of Qwen2.5-3B model.
### Fine-tuning dataset
Model was fine-tuned on [qwedsacf/grade-school-math-instructions](https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions) instruction dataset.
### Inference
```python
!pip install transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "entfane/math-professor-3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
messages = [
{"role": "user", "content": "What's the derivative of 2x^2?"}
]
input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
encoded_input = tokenizer(input, return_tensors = "pt").to(model.device)
output = model.generate(**encoded_input, max_new_tokens=1024)
print(tokenizer.decode(output[0], skip_special_tokens=False))
``` |