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Model: MegaScience/Llama3.1-8B-MegaScience Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- meta-llama/Llama-3.1-8B
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datasets:
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- MegaScience/MegaScience
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language:
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- en
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license: llama3.1
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metrics:
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- accuracy
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- science
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---
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# [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
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**Llama3.1-8B-MegaScience** is a model fine-tuned on **MegaScience**, a large-scale mixture of high-quality open-source scientific datasets totaling 1.25 million instances, as presented in the paper "MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning". The MegaScience dataset features truthful reference answers extracted from 12k university-level scientific textbooks, comprising 650k reasoning questions spanning 7 scientific disciplines. This model significantly outperforms corresponding official instruct models in average performance on scientific reasoning tasks and exhibits greater effectiveness for larger and stronger models, suggesting a scaling benefit for scientific tuning.
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For more details on the project, including the data curation pipeline and evaluation system, visit the [official GitHub repository](https://github.com/GAIR-NLP/lm-open-science-evaluation).
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## Llama3.1-8B-MegaScience
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### Training Recipe
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- **LR**: 5e-6
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- **LR Schedule**: Cosine
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- **Batch Size**: 512
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- **Max Length**: 4,096
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- **Warm Up Ratio**: 0.05
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- **Epochs**: 3
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### Evaluation Results
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<div style="display: flex; justify-content: left; gap: 20px;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/abIVZ2XB9D-o-TCyvOkDE.png" alt="Data Pipeline" style="width:80%;">
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</div>
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<div style="display: flex; justify-content: left; gap: 20px;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/xFTJ7nevc3S4UYJxUS7ue.png" alt="Data Pipeline" style="width:80%;">
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</div>
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### More about MegaScience
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<div style="display: flex; justify-content: left; gap: 20px;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/VogIpBbjfNxXFP9DfVMms.png" alt="Data Pipeline" style="width:100%;">
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</div>
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### Usage
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You can use the model with the `transformers` library:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "MegaScience/Llama3.1-8B-MegaScience"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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messages = [
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{"role": "user", "content": "Explain the concept of quantum entanglement."},
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]
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input_ids = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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eos_token_id=tokenizer.eos_token_id,
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do_sample=True,
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temperature=0.7,
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top_p=0.9
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## Citation
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Check out our [paper](https://arxiv.org/abs/2507.16812) for more details. If you use our dataset or find our work useful, please cite
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```
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@article{fan2025megascience,
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title={MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning},
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author={Fan, Run-Ze and Wang, Zengzhi and Liu, Pengfei},
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year={2025},
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journal={arXiv preprint arXiv:2507.16812},
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url={https://arxiv.org/abs/2507.16812}
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}
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```
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{{ '<|begin_of_text|>' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|start_header_id|>system<|end_header_id|>
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' + system_message + '<|eot_id|>' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|start_header_id|>user<|end_header_id|>
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' + content + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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' }}{% elif message['role'] == 'assistant' %}{{ content + '<|eot_id|>' }}{% endif %}{% endfor %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.53.1",
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"use_cache": false,
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"vocab_size": 128256
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}
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generation_config.json
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||||
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||||
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||||
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||||
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|
||||
"model.norm.weight": "model-00007-of-00007.safetensors"
|
||||
}
|
||||
}
|
||||
27
special_tokens_map.json
Normal file
27
special_tokens_map.json
Normal file
@@ -0,0 +1,27 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|eot_id|>",
|
||||
"<|eom_id|>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2069
tokenizer_config.json
Normal file
2069
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user