commit 3ca151e9b588ee0fdfec13fd44c2a4c5fcc6988d
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Sat Jul 4 08:10:12 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: AdaptLLM/medicine-LLM
    Source: Original Platform

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diff --git a/README.md b/README.md
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+---
+language:
+- en
+datasets:
+- Open-Orca/OpenOrca
+- GAIR/lima
+- WizardLM/WizardLM_evol_instruct_V2_196k
+- EleutherAI/pile
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- biology
+- medical
+---
+
+# Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024)
+This repo contains the domain-specific base model developed from **LLaMA-1-7B**, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
+
+We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**. 
+
+
+### [2024/11/29] 🤗 Introduce the multimodal version of AdaptLLM at [AdaMLLM](https://huggingface.co/papers/2411.19930), for adapting MLLMs to domains 🤗
+
+**************************** **Updates** ****************************
+* 2024/11/29: Released [AdaMLLM](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains) for adapting MLLMs to domains
+* 2024/9/20: Our [research paper for Instruction-Pretrain](https://huggingface.co/papers/2406.14491) has been accepted by EMNLP 2024
+* 2024/8/29: Updated [guidelines](https://huggingface.co/datasets/AdaptLLM/finance-tasks) on evaluating any 🤗Huggingface models on the domain-specific tasks
+* 2024/6/22: Released the [benchmarking code](https://github.com/microsoft/LMOps/tree/main/adaptllm)
+* 2024/6/21: Released the general version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain)
+* 2024/4/2: Released the [raw data splits (train and test)](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) of all the evaluation datasets
+* 2024/1/16: Our [research paper for AdaptLLM](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024
+* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B
+* 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B
+* 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B
+
+
+## 1. Domain-Specific Models
+### LLaMA-1-7B
+In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
+
+<p align='center'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
+</p>
+
+### LLaMA-1-13B
+Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B).
+
+### LLaMA-2-Chat
+Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat).
+
+For example, to chat with the biomedicine base model (🤗we highly recommend switching to the [chat model](https://huggingface.co/AdaptLLM/medicine-chat) for better response quality):
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("AdaptLLM/medicine-LLM")
+tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/medicine-LLM", use_fast=False)
+
+# Put your input here:
+user_input = '''Question: Which of the following is an example of monosomy?
+Options:
+- 46,XX
+- 47,XXX
+- 69,XYY
+- 45,X
+
+Please provide your choice first and then provide explanations if possible.'''
+
+# Simply use your input as the prompt for base models
+prompt = user_input
+
+inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
+outputs = model.generate(input_ids=inputs, max_length=2048)[0]
+
+answer_start = int(inputs.shape[-1])
+pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
+
+print(pred)
+```
+
+### LLaMA-3-8B (💡New!)
+In our recent research on [Instruction-Pretrain](https://huggingface.co/papers/2406.14491), we developed a context-based instruction synthesizer to augment the raw corpora with instruction-response pairs, **enabling Llama3-8B to be comparable to or even outperform Llama3-70B**: [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B), [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B).
+
+## 2. Domain-Specific Tasks
+
+### Pre-templatized Testing Splits
+To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
+
+Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
+
+### Evaluating Any Huggingface LMs on Domain-Specific Tasks (💡New!)
+You can use the following script to reproduce our results and evaluate any other Huggingface models on domain-specific tasks. Note that the script is NOT applicable to models that require specific prompt templates (e.g., Llama2-chat, Llama3-Instruct).
+
+1). **Set Up Dependencies**
+   ```bash
+   git clone https://github.com/microsoft/LMOps
+   cd LMOps/adaptllm
+   pip install -r requirements.txt
+   ```
+
+2). **Evaluate the Model**
+   ```bash
+   # Select the domain from ['biomedicine', 'finance', 'law']
+   DOMAIN='biomedicine'
+  
+   # Specify any Huggingface model name (Not applicable to chat models)
+   MODEL='AdaptLLM/medicine-LLM'
+  
+   # Model parallelization:
+   # - Set MODEL_PARALLEL=False if the model fits on a single GPU. 
+   #   We observe that LMs smaller than 10B always meet this requirement.
+   # - Set MODEL_PARALLEL=True if the model is too large and encounters OOM on a single GPU.
+   MODEL_PARALLEL=False
+  
+   # Choose the number of GPUs from [1, 2, 4, 8]
+   N_GPU=1
+  
+   # Whether to add a BOS token at the beginning of the prompt input:
+   # - Set to False for AdaptLLM.
+   # - Set to True for instruction-pretrain models.
+   # If unsure, we recommend setting it to False, as this is suitable for most LMs.
+   add_bos_token=False
+
+   # Run the evaluation script
+   bash scripts/inference.sh ${DOMAIN} ${MODEL} ${add_bos_token} ${MODEL_PARALLEL} ${N_GPU}
+   ```
+
+### Raw Datasets
+We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt), [RCT](https://huggingface.co/datasets/AdaptLLM/RCT), [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA), [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA), [Headline](https://huggingface.co/datasets/AdaptLLM/Headline), [NER](https://huggingface.co/datasets/AdaptLLM/NER), [FPB](https://huggingface.co/datasets/AdaptLLM/FPB)
+
+### Domain Knowledge Probing
+Our pre-processed knowledge probing datasets are available at: [med_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/med_knowledge_prob) and [law_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/law_knowledge_prob)
+
+## Citation
+If you find our work helpful, please cite us:
+```bibtex
+@inproceedings{
+cheng2024adapting,
+title={Adapting Large Language Models via Reading Comprehension},
+author={Daixuan Cheng and Shaohan Huang and Furu Wei},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=y886UXPEZ0}
+}
+```
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+{
+    "architectures": [
+        "LLaMAForCausalLM"
+    ],
+    "bos_token_id": 0,
+    "eos_token_id": 1,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "intermediate_size": 11008,
+    "initializer_range": 0.02,
+    "max_sequence_length": 2048,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "pad_token_id": 32000,
+    "rms_norm_eps": 1e-06,
+    "torch_dtype": "float16",
+    "transformers_version": "4.27.0.dev0",
+    "use_cache": true,
+    "vocab_size": 32001
+}
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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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+{"_from_model_config": true, "bos_token_id": 0, "eos_token_id": 1, "pad_token_id": 0, "transformers_version": "4.27.0.dev0"}
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