Vfrz nielsr HF Staff commited on
Commit
98c8fd0
·
verified ·
1 Parent(s): 1563983

Improve model card: Add library_name, code link, and usage example (#1)

Browse files

- Improve model card: Add library_name, code link, and usage example (6ac229bc16a091103a502f680034b9ae87efbbba)


Co-authored-by: Niels Rogge <[email protected]>

Files changed (1) hide show
  1. README.md +34 -5
README.md CHANGED
@@ -1,19 +1,48 @@
1
  ---
2
- license: apache-2.0
 
3
  datasets:
4
  - MegaScience/MegaScience
5
  language:
6
  - en
 
7
  metrics:
8
  - accuracy
9
- base_model:
10
- - Qwen/Qwen2.5-3B
11
  pipeline_tag: text-generation
 
12
  ---
13
- # [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
 
 
 
 
 
14
 
15
  ## Qwen2.5-3B-MegaScience
16
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17
  ### Training Recipe
18
 
19
  - **LR**: 5e-6
@@ -51,4 +80,4 @@ Check out our [paper](https://arxiv.org/abs/2507.16812) for more details. If you
51
  journal={arXiv preprint arXiv:2507.16812},
52
  url={https://arxiv.org/abs/2507.16812}
53
  }
54
- ```
 
1
  ---
2
+ base_model:
3
+ - Qwen/Qwen2.5-3B
4
  datasets:
5
  - MegaScience/MegaScience
6
  language:
7
  - en
8
+ license: apache-2.0
9
  metrics:
10
  - accuracy
 
 
11
  pipeline_tag: text-generation
12
+ library_name: transformers
13
  ---
14
+
15
+ # [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://huggingface.co/papers/2507.16812)
16
+
17
+ This repository contains the `Qwen2.5-3B-MegaScience` model, one of the models trained as part of the MegaScience project.
18
+
19
+ For the official code, data processing pipeline, and evaluation system, please refer to the [MegaScience GitHub repository](https://github.com/GAIR-NLP/lm-open-science-evaluation).
20
 
21
  ## Qwen2.5-3B-MegaScience
22
 
23
+ ### Usage
24
+
25
+ You can use this model with the Hugging Face `transformers` library:
26
+
27
+ ```python
28
+ from transformers import AutoModelForCausalLM, AutoTokenizer
29
+
30
+ model_name = "MegaScience/Qwen2.5-3B-MegaScience"
31
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
32
+ model = AutoModelForCausalLM.from_pretrained(model_name)
33
+
34
+ # Example text generation
35
+ prompt = "The capital of France is"
36
+ messages = [
37
+ {"role": "user", "content": prompt}
38
+ ]
39
+ text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
40
+ model_inputs = tokenizer([text], return_tensors="pt")
41
+
42
+ generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=20)
43
+ print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
44
+ ```
45
+
46
  ### Training Recipe
47
 
48
  - **LR**: 5e-6
 
80
  journal={arXiv preprint arXiv:2507.16812},
81
  url={https://arxiv.org/abs/2507.16812}
82
  }
83
+ ```