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BiomedParse: A Foundation Model for Joint Segmentation, Detection, and Recognition of Biomedical Objects Across Nine Modalities
A central, open resource for data and tools related to chain-of-thought reasoning in large language models. Developed @ Samwald research group: https://samwald.info/
A reading list on LLM based Synthetic Data Generation 🔥
A framework for prompt tuning using Intent-based Prompt Calibration
Evaluate your LLM's response with Prometheus and GPT4 💯
Open-source evaluation toolkit of large vision-language models (LVLMs), support 160+ VLMs, 50+ benchmarks
Code for "DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation" (ACL 2024)
Code and documents of LongLoRA and LongAlpaca (ICLR 2024 Oral)
Landmark Attention: Random-Access Infinite Context Length for Transformers
A simple pip-installable Python tool to generate your own HTML citation world map from your Google Scholar ID.
Synthetic Patient Population Simulator
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training Pipeline. 训练医疗大模型，实现了包括增量预训练(PT)、有监督微调(SFT)、RLHF、DPO、ORPO。
A code repository that cointains all the code for finetuning some of the popular LLMs on medical data
Tests for long context window evaluation
LlamaIndex is the leading framework for building LLM-powered agents over your data.
LOFT: A 1 Million+ Token Long-Context Benchmark
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
A framework for few-shot evaluation of language models.
For Med-Gemini, we relabeled the MedQA benchmark; this repo includes the annotations and analysis code.
YaRN: Efficient Context Window Extension of Large Language Models
Scalable toolkit for efficient model alignment
Library for clinical NLP with spaCy.
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
A high-throughput and memory-efficient inference and serving engine for LLMs
BeaverTails is a collection of datasets designed to facilitate research on safety alignment in large language models (LLMs).
The calflops is designed to calculate FLOPs、MACs and Parameters in all various neural networks, such as Linear、 CNN、 RNN、 GCN、Transformer(Bert、LlaMA etc Large Language Model)