AVA-Llama-3 : ایلان ریو ماسک در پرتوریا، آفریقای جنوبی به دنیا امده است : AVA-V4 : ایلان ریو ماسک در پرتوریا، آفریقای جنوبی به دنیا امده است. We will walk through the entire process of fine-tuning Alpaca LoRa on a specific dataset (detect sentiment in Bitcoin tweets), starting from the data preparation and ending with the deployment of the trained model. It is a significant upgrade compared to the earlier version. Note that the script is hardcoded to use CPU to merge the model in order to avoid CUDA out of memory errors. 25%). gguf") # downloads / loads a 4. This resource provided practical insights and a foundational understanding necessary for this implementation. 36%, closely approaching the original LLaMA-7B (63. This work used installation environment and fine-tuning instructions described in the original repo's README on a single GPU (A100, 80GB memory). 0-licensed. For example, say you start with 1000 steps, and find that at around 500 steps the model starts overfitting - the validation loss goes up (bad) while the training loss goes down significantly, meaning the model is learning the training set really well, but is unable to generalize to new datapoints. Techniques such as Quantized Aware Training (QAT) utilize such a technique and hence this is allowed. Our latest version of Llama – Llama 2 – is now accessible to individuals, creators, researchers, and businesses so they can experiment, innovate, and scale their ideas responsibly. 189 lines (157 loc) · 6. Sadly there is a bit of friction here due to licensing (I can't directly upload the checkpoints, I think). Commands for training LS-LLaMA and LS-unLLaMA on different tasks can follow the templates below: foo@bar:~ $ CUDA_VISIBLE_DEVICES=0 python file_name. Large number of extensions (built-in and user-contributed), including Coqui TTS for realistic voice outputs, Whisper STT for voice inputs, translation, multimodal Inference LLaMA models on desktops using CPU only. This project presents SQL-LLaMA, a Text-2-SQL model based on LLaMA-2 [Ref. Thus requires no videocard, but 64 (better 128 Gb) of RAM and modern processor is required. 5%): Public GitHub dataset available on Google BigQuery. 5M video caption dataset and LLaVA-CC3M image caption dataset. July 19, 2023: 🔥 LLM-Pruner now supports Llama-2-7b and Llama-2-13b (the huggingface version) July 18, 2023: 🚀 Support Baichuan, a bilingual LLM. Let's do this for 30B model. Output generated by May 5, 2023 · By inserting adapters into LLaMA's transformer, our method only introduces 1. Contribute to patrixr/llama-training development by creating an account on GitHub. cpp implementations. Better fine tuning dataset and performance. However, if you have sufficient VRAM on your GPU, you can change it to Llama-X: Open Academic Research on Improving LLaMA to SOTA LLM. 4B model achieves an average accuracy of 62. from gpt4all import GPT4All model = GPT4All ( "Meta-Llama-3-8B-Instruct. Token counts refer to pretraining data only. py , for training LS-LLaMA and LS-unLLaMA on sequence- and Mar 5, 2013 · Comprehensive toolkit for Reinforcement Learning from Human Feedback (RLHF) training, featuring instruction fine-tuning, reward model training, and support for PPO and DPO algorithms with various configurations for the Alpaca, LLaMA, and LLaMA2 models. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. Compatible with DeepSpeed 🚀, easily utilizing a variety of ZeRO optimization techniques. g. Dropdown menu for quickly switching between different models. The aim of Efficient Alpaca is to utilize LLaMA to build and enhance the LLM-based chatbots, including but not limited to reducing resource consumption (GPU memory or training time), improving inference speed, and more facilitating researchers' use (especially for fairseq users). py script will pre-train the LLaMA 7B model with FSDP in bfloat16 precision and gradient accumulation. Jun 14, 2023 · Enter the scripts/training directory of the project, and run bash run_pt. Update the adapter path in merge_peft_adapters. Nov 3, 2023 · 您好， 我想在一个LoRA training的result上进行continuous training，请问参数应该如何设置? "checkpoint_dir": "xxx" "resume_lora_training": true 请问resume_from_checkpoint也需要设置么? 非常感谢！ As the neural net architecture is identical, we can also inference the Llama 2 models released by Meta. Alpacas are herbivores and graze on grasses and other plants. Thank you to our partners of RedPajama-v1, including Ontocord. , VL branch or AL branch) in Video-LLaMA consists of two stages, Pre-training on the Webvid-2. Apache 2. 1] for instruction-based generation of SQL code from natural language queries. We train our models on trillions of tokens, and show that it is possible to train state-of . Download the model. We provide a simple training script based on Fabric if you want to venture into pre-training on RedPajama, a reproduction of the original LLaMA dataset. data import DataLoader import torch. 5. Learn more about bidirectional Unicode characters. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory. - llama/training_example. Efficiency and Affordability: The Megatron-LM techniques make LLaMA training fast and affordable. Q4_0. Grouped-Query Attention (GQA) is used for all models to improve inference efficiency. Note that, due to train on the papers, MedLLaMA_13B may generate some citation numbers (LLaMA somtimes will do this as well) and we dismiss them in the cases to show the main contents. The tutorial will cover topics such as data processing, model training, and evaluation using popular natural language processing libraries such as Transformers and Hugging Face SQL-LLaMA 2. Llama-2-Chat models outperform open-source chat models on most Benchmark. cpp with 4-bit / 5-bit quantization support! [10/11] The training data and scripts of LLaVA-1. py , llama_seq_clf. Tamil LLaMA is now bilingual, it can fluently respond in both English and Tamil. The 'llama-recipes' repository is a companion to the Meta Llama 2 and Meta Llama 3 models. Demo apps to showcase Meta Llama3 for WhatsApp As the neural net architecture is identical, we can also inference the Llama 2 models released by Meta. Supports default & custom datasets for applications such as summarization and Q&A. Contribute to gagan3012/llama-tldr development by creating an account on GitHub. For more information on the motivation behind expanding the Chinese vocabulary, please refer to the FAQ. Similar differences have been reported in this issue of lm-evaluation-harness. Llama 2 family of models. tensorboard as tensorboard from torch. Save the repetitive work of community and we work MobiLlama is a SLM design that initiates from a larger model and applies a careful parameter sharing scheme to reduce both the pre-training and the deployment cost. Definitions. LongLLaMA Code is a Code Llama model finetuned with the FoT method. Llama models use different projection sizes compared with classic transformers in the feed-forward layer, for instance, both Llama 1 and Llama 2 projection use 2. First, you need to unshard model checkpoints to a single file. Paper Abstract: We introduce LLaMA, a collection of founda- tion language models ranging from 7B to 65B parameters. ). The 'llama-recipes' repository is a companion to the Llama 2 model. LLaMA-MoE is a series of open-sourced Mixture-of-Expert (MoE) models based on LLaMA and SlimPajama. /. 📖 Document. Large language model. ⚡ Model Download Model Name Feb 1, 2024 · The Alignment Handbook aims to fill that gap by providing the community with a series of robust training recipes that span the whole pipeline. We reexamine design spaces of image tokenizers, scalability properties of image generation models, and their training data quality. We also support and verify training with RTX 3090 and RTX A6000. Users should edit the script set value of parameters. Code Llama was developed by fine-tuning Llama 2 using a higher sampling of code. For detailed information on model training, architecture and parameters, evaluations, responsible AI and safety refer to our research paper. Our models match or betters the performance of Meta's LLaMA 2 is almost all the benchmarks. The 8B model is designed for faster training Aug 11, 2023 · LLaMA models are small. Support VLM . Llama 3. Github (4. [2] [3] The latest version is Llama 3, released in April 2024. [2024/03] 🔥 We released Chatbot Arena technical report. 5 are released here, and evaluation scripts are released here! [10/10] Roboflow Deep Dive: First Impressions with LLaVA-1. cpp to make LLMs accessible and efficient for all. In our case, we use the IMDB dataset # from the `datasets` library. For running on the sample dataset: The script will save checkpoints periodically to the folder out/. [Project page] [Supplementary] [Casual GAN Papers Summary] Try out in Google Colab That's where LlamaIndex comes in. , in the passkey retrieval task, it can handle inputs of length $256k$. 30. import argparse import torch import os from tqdm import tqdm from torch import optim from torch. Training. If you find our LLaMA-Adapter code and paper useful, please kindly cite: @article{zhang2023llamaadapter, title = {LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention}, author={Zhang, Renrui and Han, Jiaming and Zhou, Aojun and Hu, Xiangfei and Yan, Shilin and Lu, Pan and Li, Hongsheng and Gao, Peng and Qiao, Yu Multiple model backends: Transformers, llama. LLaMA is a large language model trained by Meta AI that surpasses GPT-3 in terms of accuracy and efficiency while being 10 times smaller. Suppose that we train our own LLaMA-13b model on four 8xA100-80GB devices. Jul 12, 2024 · finetune_llama_v2. 🚀 LLaMA2-Accessory is an open-source toolkit for pretraining, finetuning and deployment of Large Language Models (LLMs) and multimodal LLMs. The research paper "Pre-training Small Base LMs with Fewer Tokens", which utilizes LitGPT, develops smaller base language models by inheriting a few transformer blocks from larger models and training on a tiny fraction of the data used by the larger models. py --input_dir D:\Downloads\LLaMA --model_size 30B. We observe up to ~20 points improvement over training-free methods when quantizing weight, activations and kv cache to 4-bit, 8-bit and 4-bit, respectively. eos_token # Below is an example function to build the dataset. We release LLaVA Bench for benchmarking open-ended visual chat with results from Bard and Bing-Chat. However, one can use the outputs to further train the Llama family of models. To review, open the file in an editor that reveals hidden Unicode characters. For llama-7b and baichuan2-7b，it is better to use parts: [5, 4, 4, 4, 4, 4, 4, 5]. Note: the following instructions are for using LLaMA-Train on a computer with a GPU. Once we have those checkpoints, we have to convert them into Apr 6, 2023 · LLaMA-GPT-4 performs similarly to the original GPT-4 in all three criteria, suggesting a promising direction for developing state-of-the-art instruction-following LLMs. The Meta LLaMA GitHub repository has been an essential resource for understanding the intricacies of the LLaMA 2 model and its implementation. However, this doesn't explain why llama1 and llama2 with batchsize=1 can work, which also has huge outliners in hidden_size. The training speed is up to the slowest gpu, so we should let each gpu have equal or similar computation burden. sh to start pre-training (use a single GPU by default). 7x hidden size rather than the standard 4x hidden size. We are appreciative to the work done by the growing open-source AI community that made this project possible. pip install gpt4all. installation pip install -e . Meta developed and released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. It demonstrates that these smaller Mar 22, 2023 · Our initial release contains the data generation procedure, dataset, and training recipe. utils. py , unllama_token_clf. cpp启动，提示维度不一致 问题8：Chinese-Alpaca-Plus效果很差 问题9：模型在NLU类任务（文本分类等）上效果不好 问题10：为什么叫33B，不应该是30B吗？ Saved searches Use saved searches to filter your results more quickly Llama (acronym for Large Language Model Meta AI, and formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023. This repo is mainly inherited from LLaMA-Adapter with more advanced MLE-LLaMA. English CommonCrawl (67%): Removed non-English text and duplicated content. - raghavc/LLM-RLHF-Tuning-with-PPO-and-DPO We are grateful to the great team at EleutherAI for paving the path on open training datasets with The Pile and for open-sourcing code we use in training some of the RedPajama models. Jul 19, 2023 · 中文LLaMA-2 & Alpaca-2大模型二期项目 + 64K超长上下文模型 (Chinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models) - ymcui/Chinese-LLaMA-Alpaca-2 Easy-to-use LLM fine-tuning framework (LLaMA, BLOOM, Mistral, Baichuan, Qwen, ChatGLM) - TingchenFu/LlamaFactory Model Architecture Llama 2 is an auto-regressive language optimized transformer. In this repository I release model weights, the dataset and the code used for finetuning the LLaMA-2 7B and 13B language model. The goal of this repository is to provide examples to quickly get started with fine-tuning for domain adaptation and how to run inference for the fine-tuned models. sh are as follows: Execute the following command to start pre-training (some variables need to be specified by the suer): ########parameters Instruction: Tell me about alpacas. Apr 18, 2024 · Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. We demonstrate PMC_LLaMA_13B's responses with out of domain queries. [9/20] We summarize our empirical study of training 33B and 65B LLaVA models in a note. why? because during training we only learn to predict tokens using past tokens. It should be noted that during the fine-tuning stage, Alpaca has one more pad token than LLaMA, so the Chinese Alpaca vocabulary size is 49,954. In our experiments, the pruning duration was set to 3200ba, and the continued pre-training duration was set to 48000ba. For now, we have chosen to host a live demo to help readers better understand the capabilities and limits of Alpaca, as well as a way to help us better User-friendly LLaMA: Train or Run the model using PyTorch. train. ai, MILA Québec AI Institute, ETH DS3Lab, Université de Montréal, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION. [2023/08] We released Vicuna v1. 2 models are out. 5 based on Llama 2 with 4K and 16K context lengths. py. According to Meta, the release of Llama 3 features pretrained and instruction fine-tuned language models with 8B and 70B parameter counts that can support a broad range of use cases including summarization, classification, information extraction, and content grounded question and answering. This is the repo for the Llama-X, which aims to: Progressively improve the performance of LLaMA to SOTA LLM with open-source community. May 20, 2023: 🎉 Code and Preprint Paper released! It's correct that the license restricts using any part of the Llama models, including the response outputs to train another AI model (LLM or otherwise). Once we have those checkpoints, we have to convert them into Each decoder layer (or transformer block) is constructed from one self-attention layer and one feed-forward multi-layer perceptron. The training of each cross-modal branch (i. We need to set it to eos_token. Merge the adapter back to the pretrained model. The following table depicts the training cost and TFLOPS of DeepSpeed implentation Automatically dispatch high-performance operators such as FlashAttention and Triton kernels to increase training throughput. as a result, during inference we set the future tokens to zero. As with Llama 2, we applied considerable safety mitigations to the fine-tuned versions of the model. For ease of use, the examples use Hugging Face converted versions of the models. Please note that you would have to request and been granted access from Meta to use the Llama-2 base model. Follow this guide to start pre-training on the RedPajama dataset: Pretrain on RedPajama Chinese large language model base generated through incremental pre-training on Chinese datasets - OpenLMLab/OpenChineseLLaMA The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. 66GB LLM with model This project is the JAX implementation of Llama 2. 7 times faster training speed with a better Rouge score on the advertising text generation task. We build LLaMA-MoE with the following two steps: Partition LLaMA's FFNs into sparse experts and insert top-K gate for each layer of experts. 0 to enjoy this feature. Read the report. او در 18 سالگی به کانادا رفت : AVA-V2 The 'llama-recipes' repository is a companion to the Meta Llama 3 models. Aug 23, 2012 · [23/08/18] Now we support resuming training, upgrade transformers to 4. You signed out in another tab or window. , 1ep). 🤗 HF Repo • 👋 join our WeChat • 🚀 Demo. We intend to release the model weights if we are given permission to do so by the creators of LLaMA. mlp forward after the post_layer_norm, and this inf may comes from huge value in hidden_size. 🔬 Pre-training Small Base LMs with Fewer Tokens. during the training process of llama3, the future token qk scores are masked. LlamaIndex is a "data framework" to help you build LLM apps. py can be one of unllama_seq_clf. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3. Running the pre-training script requires at least 4 GPUs with 40GB+ each (A100). After removing duplicate tokens, the final Chinese LLaMA vocabulary size is 49,953. completion of periodic structures. The pre-training data used in LLaMA are. One should customize this function to train the model on # its own dataset Crucially, LongLLaMA is able to extrapolate much beyond the context length seen in training: $8k$. Alpaca-LoRA: Alpacas are members of the camelid family and are native to the Andes Mountains of South America. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs. py , and llama_token_clf. You switched accounts on another tab or window. Aug 18, 2023 · [23/07/19] 现在我们支持了 LLaMA-2 模型的训练。请尝试使用 --model_name_or_path meta-llama/Llama-2-7b-hf 参数。使用 LLaMA-2-chat 模型时请添加 --template llama2 参数。 [23/07/18] 我们开发了支持训练和测试的浏览器一体化界面。请尝试使用 train_web. It provides the following tools: Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc. cpp (through llama-cpp-python), ExLlamaV2, AutoGPTQ, AutoAWQ, TensorRT-LLM. Efficiency and Affordability : The Megatron-LM techniques make LLaMA training fast and affordable. A WebUI for Efficient Fine-Tuning of 100+ LLMs (ACL 2024) - Pretraining · hiyouga/LLaMA-Factory Wiki LLama Training code DL-NLP. if getattr (tokenizer, "pad_token", None) is None: tokenizer. Flexible. cuda. Jul 25, 2023 · I dive into it and find that the nan occurs in layer. E. Megatron-LLaMA makes large-scale training of LLaMA models fast, affordable and scalable. , Llama, without inductive biases on visual signals can achieve state-of-the-art image generation performance if scaling properly. LLaMA2-Accessory: An Open-source Toolkit for LLM Development 🚀. Conversion scripts for our optimized streaming PackedDataset are included. Llama 3 uses a tokenizer with a vocabulary of 128K tokens, and was trained on on sequences of 8,192 tokens. May 20, 2023 · The fine-tuned LLaMA-5. py 在您的浏览器中微调模型。 LaMa generalizes surprisingly well to much higher resolutions (~2k ️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e. pad_token = tokenizer. Try --rope_scaling linear argument in training and --rope_scaling dynamic argument at inference to extrapolate the position embeddings. gpt4all gives you access to LLMs with our Python client around llama. ai, MILA Québec AI Institute, ETH DS3Lab, Université de Montréal, Stanford Center for Research on Umar Jamil's guide on Training LLama2 from scratch. You signed in with another tab or window. The same filters were applied. Llama 2: open source, free for research and commercial use. [10/12] LLaVA is now supported in llama. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. The goal of this repository is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Meta Llama and other Here are some key training parameters to take note of: max_duration: This parameter defines the maximum training duration and can be specified in either the number of steps (e. Fine-tuning with the data We follow the same reciple to fine-tune LLaMA as Alpaca using standard Hugging Face training code. Jun 11, 2024 · It is an affirmative answer to whether vanilla autoregressive models, e. The contents of run_pt. Nomic contributes to open source software like llama. This repository is intended as a minimal, hackable and readable example to load LLaMA ( arXiv) models and run inference by using only CPU. That includes: Participants in building the RedPajama dataset including Ontocord. pth file in the root folder of this repo. So Step 1, get the Llama 2 checkpoints by following the Meta instructions. The objectives of this project are threefold: Implement the Llama 2 model using JAX to enable efficient training and inference on Google Cloud TPU; Develop a high-quality codebase that serves as an exemplary implementation of the Transformer model using JAX; Facilitate the identification of 问题5：回复内容很短 问题6：Windows下，模型无法理解中文、生成速度很慢等问题 问题7：Chinese-LLaMA 13B模型没法用llama. Tamil LLaMA v0. 31. This repository used base model of quantized Llama-2-70b-hf. [23/08/12] Now we support RoPE scaling to extend the context length of the LLaMA models. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. The train_redpajama. # only for this model. In this example, D:\Downloads\LLaMA is a root folder of downloaded torrent with weights. Support various LLMs (InternLM, Mixtral-8x7B, Llama 2, ChatGLM, Qwen, Baichuan, ). Mar 23, 2023 · | English | 中文 | NOTE&FAQ(Please take a look before using) This is the repo for the Chinese-Vicuna project, which aims to build and share instruction-following Chinese LLaMA model tuning methods which can be trained on a single Nvidia RTX-2080TI, multi-round chatbot which can be trained on a single Nvidia RTX-3090 with the context len 2048. The goal of this repository is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Meta Jun 1, 2023 · @article {li2023llavamed, title = {Llava-med: Training a large language-and-vision assistant for biomedicine in one day}, author = {Li, Chunyuan and Wong, Cliff and Zhang, Sheng and Usuyama, Naoto and Liu, Haotian and Yang, Jianwei and Naumann, Tristan and Poon, Hoifung and Gao, Jianfeng}, journal = {arXiv preprint arXiv:2306. This will create merged. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Supporting a number of candid inference solutions such as HF TGI, VLLM for local or cloud deployment. py and run the script to merge peft adapters back to pretrained model. C4 (15%): A cleaned version of CommonCrawl. They are known for their soft, luxurious fleece, which is used to make clothing, blankets, and other items. For stablizing training at early stages, we propose a novel Zero-init Attention with zero gating mechanism to adaptively incorporate the instructional signals. [2023/09] We released LMSYS-Chat-1M, a large-scale real-world LLM conversation dataset. The model uses MERT + LLaMA as the backbone and employs an adapter to encoperate music context information Pre-training. 2M learnable parameters, and turns a LLaMA into an instruction-following model within 1 hour. We should not only consider the memory but also computation layout among different gpus. input_layer_norm, which is caused by inf in layers. To use the Google Colab notebook supplied under notebooks, which provides the same functionality, see the acompanying document. Make sure you have enough swap space (128Gb should be ok :). 00890}, year = {2023}} Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT methods to cover single/multi-node GPUs. News 🗞️ April 12, 2024 : We release Zephyr 141B (A35B), in collaboration with Argilla and Kaist AI, along with the recipe to fine-tune Mixtral 8x22B with ORPO 🪁 We experiment with LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. Better base model. By inserting adapters into LLaMA's transformer, our method only introduces 1. Mar 9, 2023 · We implement LLaMA training on the TencentPretrain framework, the tutorial is as follows: Clone the TencentPretrain project and install dependencies: PyTorch, DeepSpeed, SentencePiece git clone htt Apr 13, 2023 · Implementation of the LLaMA language model based on nanoGPT. py dataset_name model_size file_name. Nothing else. This project will be constantly updated and maintained. History. Conduct Llama-X as an open academic research which is long-term, systematic and rigorous. Better tokenizer. e. Only includes pages used as references in Wikipedia. Reload to refresh your session. By exposing the model to vast amounts of Chinese text data, we enable it to capture intricate linguistic patterns and nuances, resulting in improved language generation. We're unlocking the power of these large language models. Code. This is a project under development, which aims to fine-tune the llama (7-70B) model based on the 🤗transformers and 🚀deepspeed, and provide simple and convenient training scripts. Our model is also designed with the purpose of captioning music files to generate Text-to-Music Generation datasets. - How to use training LLaMA with train. py at master · ypeleg/llama Continued pre-training: To further enhance Llama-2's Chinese language understanding, we continue its pre-training on large-scale Chinese corpora. python merge-weights. 61 KB. , 3200ba) or epochs (e. amp import GradScaler from llama import LlamaTokenizer, LlamaForCausalLM from utils The MU-LLaMA model is Music Understanding Language Model designed with the purpose of answering questions based on music. py, what format of training data needs to be provided? · Issue #134 · Lightning-AI/lit-llama [2023/07/19] 🔥 We release a major upgrade, including support for LLaMA-2, LoRA training, 4-/8-bit inference, higher resolution (336x336), and a lot more.
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