初始化项目，由ModelHub XC社区提供模型
Model: TheFinAI/Fino1-8B Source: Original Platform
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README.md
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README.md
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---
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license: llama3.1
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datasets:
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- TheFinAI/Fino1_Reasoning_Path_FinQA
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language:
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- en
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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pipeline_tag: text-generation
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---
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# 🦙 Fino1-8B
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**Fino1-8B** is a fine-tuned version of **Llama 3.1 8B Instruct**, designed to improve performance on **[financial reasoning tasks]**. This model has been trained using **SFT** and **RF** on **TheFinAI/Fino1_Reasoning_Path_FinQA**, enhancing its capabilities in **financial reasoning tasks**.
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Check our paper arxiv.org/abs/2502.08127 for more details.
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## 📌 Model Details
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- **Model Name**: `Fino1-8B`
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- **Base Model**: `Meta Llama 3.1 8B Instruct`
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- **Fine-Tuned On**: `TheFinAI/Fino1_Reasoning_Path_FinQA` Derived from FinQA dataset.
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- **Training Method**: SFT and RF
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- **Objective**: `[Enhance performance on specific tasks such as financial mathemtical reasoning]`
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- **Tokenizer**: Inherited from `Llama 3.1 8B Instruct`
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## 📊 Training Configuration
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- **Training Hardware**: `GPU: [e.g., 4xH100]`
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- **Batch Size**: `[e.g., 16]`
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- **Learning Rate**: `[e.g., 2e-5]`
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- **Epochs**: `[e.g., 3]`
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- **Optimizer**: `[e.g., AdamW, LAMB]`
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## 🔧 Usage
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To use `Fino1-8B` with Hugging Face's `transformers` library:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "TheFinAI/Fino1-8B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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input_text = "What is the results of 3-5?"
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inputs = tokenizer(input_text, return_tensors="pt")
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output = model.generate(**inputs, max_new_tokens=200)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## 💡 Citation
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If you use this model in your research, please cite:
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```python
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@article{qian2025fino1,
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title={Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance},
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author={Qian, Lingfei and Zhou, Weipeng and Wang, Yan and Peng, Xueqing and Huang, Jimin and Xie, Qianqian},
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journal={arXiv preprint arXiv:2502.08127},
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year={2025}
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}
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config.json
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"_name_or_path": "/home/jupyter/20000360102458359xu/LingfeiQian/finetune/HuatuoGPT-o1-main/ckpts/sft_stage1/checkpoint-2-2061/tfmr",
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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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1
response_1739385512394.json
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1
response_1739385512394.json
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||||
"The brain is a highly complex organ that serves as the central control unit of the nervous system in humans and other animals. It is responsible for processing sensory information, regulating bodily functions, and facilitating cognitive processes such as thinking, memory, and emotion. The brain's structure is hierarchical and modular, with different regions specialized for various functions, yet interconnected to allow for integrated processing and response.\n\nRecent advances in neuroscience have significantly enhanced our understanding of the brain's structure and function. For instance, fluorescence imaging techniques have enabled researchers to map neuronal structures across different length scales, from the nanometer-scale organization of synaptic proteins to the macroscopic wiring of neuronal circuits [0]. This multiscale approach is crucial for understanding how brain function emerges from its hierarchical structure.\n\nIn the context of brain injuries, such as those resulting in disorders of consciousness, the relationship between structural and functional connectivity becomes particularly important. Studies using MRI-DWI and FDG-PET have shown that severe brain injuries can lead to profound declines in regional metabolism and white matter integrity, affecting the function-structure connectivity within the brain [3]. This understanding is further supported by mathematical models that link structural and functional connectomes, suggesting that recovery from brain injuries may involve functional rerouting via alternate structural pathways [7].\n\nMoreover, the brain's response to stress and its role in mental health conditions like depression have been explored through whole-brain mapping techniques. For example, research using the learned helplessness model of depression in mice has revealed distinct patterns of neuronal activation associated with stress responses, highlighting the brain's role in adaptive and maladaptive behaviors [5].\n\nOverall, the brain's complexity is reflected in its intricate network of connections and its ability to adapt to various challenges, whether through structural changes following injury or through functional adaptations in response to environmental stressors. Understanding these processes is essential for developing interventions and treatments for neurological and psychiatric conditions.\n\nReferences:\n[0] Advancing multiscale structural mapping of the brain through fluorescence imaging and analysis across length scales.\n[3] Function-structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET.\n[5] Whole-Brain Mapping of Neuronal Activity in the Learned Helplessness Model of Depression.\n[7] The application of a mathematical model linking structural and functional connectomes in severe brain injury."
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"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": "<|end_of_text|>"
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
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tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
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Load Diff
Reference in New Issue
Block a user