Llama 2 long context. 7B: 32K: NousResearch/Yarn .
Llama 2 long context nlp yarn llama alpaca 64k large-language-models llm rlhf flash-attention llama2 llama-2 alpaca-2 alpaca2 Resources. AI LLM Context Expansion project. Skip to content. You can think of transformer models like Llama-2 as a text document X characters long (the "context"). Go to HuggingFace and try out LLaMA-2-7B-32K. , GPT-4-Turbo) in long-context understanding and retrieval-augmented generation (RAG) capabilities. A transformer layer doesn’t care about the length of the show that our recipe results in 7B and 13B LLaMA-2 of strong long-context performance, substantially closing the gap to frontier models like GPT-4 128K on the Needle-in-a-Haystack test, opening future possibilities of studying long-context modeling under academic budgets. 5-turbo-16k‘s overall performance on long Llama-2 with 128k context length thanks to YaRN News twitter. Open comment sort options. After that, I will release some LLama 2 models trained with Bowen's new ntk methodology. Also, I am currently working on building a high-quality long context dataset with help from the original author of We use two long-context models: Llama-2-7B-32K-Instruct and Llama-3-8B-Instruct-Gradient-1048k. 0 license Activity. so llama. 7B: 32K: NousResearch/Yarn System Info llamafactory newest version Reproduction 需要finetune 72B模型+32k context 用llamafactory总是爆显存，大家有什么好的办法吗？ hiyouga / LLaMA-Factory Public. LLaMA-2-7B-32K is an open-source, long context language model developed by Together, fine-tuned from Meta's original Llama-2 7B model. ai) Table of contents The experimental studies show that Activation Beacon is able to extend Llama-2-7B's context length by ×100 times (from 4K to 400K), meanwhile achieving a superior result on both long-context generation and understanding tasks. 2. 30. Q&A. no-execution opened this issue Oct 24, 2024 Replicate - Llama 2 13B LlamaCPP 🦙 x 🦙 Rap Battle Llama API llamafile LLM Predictor LM Studio LocalAI Maritalk MistralRS LLM MistralAI ModelScope LLMS Stress-Testing Long Context LLMs with a Recall Task Pydantic Tree Summarize Refine Refine with Structured Answer Filtering Tree Summarize Llama-2-7B-32K-Instruct Model Description Llama-2-7B-32K-Instruct is an open-source, long-context chat model finetuned from Llama-2-7B-32K, over high-quality instruction and chat data. Together AI's model predates Llama 2 Long by a few months. For basic Llama-2, it is 4,096 "tokens". {MODEL_PATH} TOKENIZER_TYPE="hf" ;; llama-2-7b Discover the power of Llama-3. The maximum context length of open source LLMs has followed a similar trend: while the first For example, a 4K context window (like those in GPT-3. Size Context which may potentially improve long context performance on a per-parameter basis. g. Old. 1. {Long Context Transfer from Language to Vision}, author={Peiyuan Zhang and Kaichen Zhang and Bo Li and Guangtao Zeng and Jingkang Yang and For these tests, we will use the 7B parameter Llama 2 model. There are some models Context Train Link; Llama-2-7b-longlora-8k-ft: 7B: 8192: Full FT: Model: Llama-2-7b-longlora-16k-ft: 7B: 16384: Full FT: Model: Llama-2-7b-longlora {LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models}, author={Yukang Chen and Shengju Qian and Haotian Tang and Xin Lai and Zhijian Liu and Song Han and Jiaya Jia Large language models (LLMs) are typically trained with a pre-defined context size, such as 2048 tokens for LLaMA (Touvron et al. Also included are evaluation scripts and benchmark tasks that evaluate a model’s information retrieval capabilities with context expansion. cpp handles scaling Quantized versions of Llama 3 long context version by Gradient. 1-8B-Instruct and RAG workflows paired with Llama-3. , 2023). 19] We release a new version of LongAlpaca models, LongAlpaca-7B-16k, LongAlpaca-7B-16k, and LongAlpaca-7B-16k. I am training a few different instruction models. Long-context models are already crucial for document understanding, We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. This model is the Flash Attention 2 patched version of the original model: Extension of Llama 2 to 128k context windows • 17 items • 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) Topics. LLaMA-2 7B 80K: continue pretrained on 80K, tested on 128K; LLaMA-2 13B 64K: continue pretrained on 64K, tested on 128K; Evaluating the pretrained checkpoint on Needle-in-a-HayStack; Loading the preprocessed data; Processing the long-context data; Continue pretraining the model on processed long-context data LongLLaMA: Focused Transformer Training for Context Scaling TLDR | Overview | Usage | LongLLaMA performance | Authors | Citation | License | Acknowledgments. Yi-1. Namely, we consider: Long-context models are already crucial for document understanding, summarization, and retrieval augmented generation. Does this mean that in order to make full use of the default Llama-2 4K context, Extending the training of base model should use We apply CEPED to LLaMA-2-Chat and show that while preserving their instruction understanding ability, CEPED-LLaMA-2-Chat can incorporate more context and improve performance on long-text understanding tasks (Shaham et al. 5x native context, and 2. 2 . The llama-cpp-python server has a mode just for it to replicate OpenAI's API. We are actively working on continuing to improve the long-context capabilities. cpp itself is not great with long context. The original 34B they did had worse results than Llama 1 33B on benchmarks like commonsense reasoning and math, but this new one reverses that trend with better scores across everything. Abstract We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. New. 5. [2] [3] The latest version is Llama 3. Llama 1 would go up to 2000 tokens easy but all of the llama 2 models I've tried will do a little more than half that, even though the native context is now 4k. We release a smaller 3B base variant (not instruction They confidently released Code Llama 34B just a month ago, so I wonder if this means we'll finally get a better 34B model to use in the form of Llama 2 Long 34B. 2 1B and 3B models! We evaluate their performance, safety, long-context capabilities, and more. See how small models can deliver big results! CAPE leverages a small encoder to process a long input chunk by chunk and enables the frozen decoder to cross-attend to the additional contexts. LongLoRA extends models' context while retaining their original architectures, and is compatible with most existing techniques, like FlashAttention-2. A few weeks ago, we released a series of Llama-3 long context models. LongLLaMA Code is built upon the foundation of Code Llama. For these tests, we will use the 7B parameter Llama 2 model. ProLong outperforms Llama-3. Code and theory how to fine-tune for long-context LLM, like LLama-2 100K. Model Description Nous-Yarn-Llama-2-13b-64k is a state-of-the-art language model for long context, further Its complicated, but generally for most models, you set RoPE Alpha to 1. We built Llama-2-7B-32K-Instruct with less than 200 lines of Python script using Together API, and we also make the recipe fully available. 75 for 1. But yes there in fact a model specifically for what you want. That's why you usually see these sort of very long context tuning/training on small models. In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. We conduct a systemic study of long-context in-context learning. from Meta AI in 2023 presents Position Interpolation (PI) that extends the context window sizes of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal fine-tuning (within 1000 steps), This repository contains the research preview of LongLLaMA, a large language model capable of handling long contexts of 256k tokens or even more. [4]Llama models are trained at different parameter sizes, ranging between 1B and 405B. To make training our Llama-3 long context models possible, we developed a synthetic data generation pipeline that composes coherent training data for contexts up to 1 million tokens. They are available under the Llama 2 license on 🤗 Hugging Face. In order to get access to the model, you will need to request access from Meta using the same email as your HuggingFace account, which will allow you to download the models from HuggingFace. and the resulting functionality that could arise from such long context windows is really The native context length for Llama 1 and 2 are 2,024 and 4,096 tokens. We configure DuoAttention with a 25% retrieval head ratio for Llama-2-7B-32K-Instruct and a 50% ratio for Llama-3-8B-Instruct-Gradient-1048k. these seem to be settings for 16k. Controversial. 5 pro boasts a context length of 2 million tokens. LongLoRA adopts LLaMA2 7B from 4k context to 100k, or LLaMA2 70B to 32k on a single 8x A100 machine. It’s frustrating as the current models with better context would push them over the line into being properly useful. The pretrained models come with significant improvements over the Llama 1 models, Llama 2 Long boasts an improved context length and outperforms OpenAI’s GPT 3. Size Context Link; 10. We publish variants of Llama 2 fine-tuned with YaRN at 32K, 64K and 128K context window length. Contribute to Leooyii/LCEG development by creating an account on GitHub. Long Context Models vs Retrieval-Augmented Generation (RAG) To explore one part of this, we designed simple experiments to probe at the strengths and weaknesses of the long context window of Llama-3. (2024)’s long-context finetuned Llama-2-7b model, using a context of up to 80K tokens. 5 or Llama 2) is equivalent to about six pages of text, while a 32K context length could encompass up to 49 pages. You can fill whatever percent of X you want to with chat history, and whatever is left over is the space the model can respond with. 1-8B-Instruct. Replicate - Llama 2 13B LlamaCPP 🦙 x 🦙 Rap Battle Llama API llamafile LLM Predictor LM Studio LocalAI Maritalk MistralRS LLM MistralAI ModelScope LLMS Stress-Testing Long Context LLMs with a Recall Task Pydantic Tree Summarize Refine Refine with Structured Answer Filtering Tree Summarize Original model card: NousResearch's Yarn Llama 2 13B 64K Model Card: Nous-Yarn-Llama-2-13b-64k Preprint (arXiv) GitHub. Meta has upgraded its flagship open-source Llama 2 large language model to improve its ability to handle lengthier inputs. This model represents our efforts to contribute to the rapid progress of the open-source Use OpenChatKit to fine-tune a 32K model over LLaMA-2-7B-32K for your own long context applications. Apache-2. Background Frontier language models feature extremely long context This blog post clarifies our mission as a data framework along with our view of what long-context LLM architectures will look like. Hugging Face. TLDR This repository contains the research preview of Last month, we released Llama-2-7B-32K, which extended the context length of Llama-2 for the first time from 4K to 32K — giving developers the ability to use open-source AI for long-context tasks such as document The community can try to implement the method outlined in the paper, but we obviously don’t have the ability to pick up from checkpoints they mention, or access to the long context dataset they developed. Each setting is described in more detail as it is introduced. 5 at long tasks. We demonstrate that by applying DCA to Llama-2/3 70B, the model exhibits surprising extrapolation capabilities (100k context length) and a very strong understanding of practical long-context tasks. Our models outperform open-source chat models on most benchmarks we tested, and based on our Across all evaluations, our models achieve consistent improvements on most regular-context tasks and significant improvements on long-context tasks over Llama 2. In future work, we plan to evaluate the recently released Llama-3. In actual fine-tuning, we can use any framework that implements the above features properly to perform the training, such as HF Trainer, Axolotl, and Llama Factory, which theoretically should all meet the requirements. Our model Abstract. Not sure why, but I'd be thrilled if it could be fixed. This model is the Flash Attention 2 patched version of the original model: Extension of Llama A long-context sample necessarily brings longer inference time compared to multiple short-context samples. Overall, I hope you enjoyed Llama 3. Results are on Fu et al. We also include key experimental results and instructions for reproducing and building on them. 8sec/token Replicate - Llama 2 13B LlamaCPP 🦙 x 🦙 Rap Battle Llama API llamafile LLM Predictor LM Studio LocalAI Maritalk MistralRS LLM MistralAI ModelScope LLMS Chunk + Document Hybrid Retrieval with Long-Context Embeddings (Together. These models are fine-tuned on a subset LongAlpaca-12k dataset with LongLoRA in SFT, LongAlpaca-16k-length. LongLLaMA is built upon the foundation of OpenLLaMA and fine-tuned using the Focused Transformer (FoT) method. Copy link Ricardokevins commented Sep 22, 2023. This paper by Chen et al. 17] LongLoRA has been accepted by ICLR 2024 as an Oral presentation. ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference - bytedance/ShadowKV. Thanks for pointing out that the paper is missing. Long Context Windows Advantages. Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023. chunking), there will need to be evolved RAG architectures to handle the new use cases that long-context LLMs bring along. Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. Long input context . 128k Context Llama 2 Finetunes Using YaRN Interpolation (successor to NTK-aware interpolation) and Flash Attention 2 All of our metrics point to these models being the new SoTA for long context models (see Experiments section of amant555 changed the title LLama 2 finetuning on long context length with multi-GPU LLama 2 finetuning on multi-GPU with long context length Sep 21, 2023. Top. One of our most interesting learnings was around scaling positional encodings, which is why we’ll be taking a deep dive on how we managed to scale the context length up to 4M. You need high quality long context datasets Models like Llama 2 are trained on 4K tokens. My idea was to use the exact position values for "Continual pretraining from short context models can easily save around 40% FLOPs while imposing almost no loss on performance. Since llama 2 has double the context, and runs normally without rope Similarly, Claude 2 has a context length of 200k tokens and Gemini 1. , 2023a) and 4096 tokens for Llama2 (Touvron et al. It contains more than 3k long context question-answer pairs. 5 Pro comes with a 2-million-token context window. I am trying to train llama2 13 B model over 8 mega context with grammar is tricky on 12GB. Unlike the LLaMa-2-7B, the PI method shows a more significant improvement in the LLaMa-2-13B, as depicted in Figure 9. Training a 70B is much more expensive. Interesting, thanks for the resources! Using a tuned model helped, I tried TheBloke/Nous-Hermes-Llama2-GPTQ and it solved my problem. performance on shorter sequences. ’s long-context finetuned Llama-2-7b model, using a context of up to 80K tokens. That aside, Mistral is SOTA for 8k context windows. Notifications You must be signed in to change notification Need Help About Long Context #5815. 0x native context. From financial forecasting to customer behavior analysis, Llama 2's prowess in long context sets it apart as a game-changer in data-driven decision making. In addition, the three model variants had additional long-context fine-tuning, allowing them to manage a context window of up to 100,000 tokens. In the past two weeks, we’ve released a series of Llama-3 long context models by increasing RoPE theta and adding in full context length supervised fine-tuning (SFT). 11. Llama 3 long context. Reply reply We present LongLoRA, an efficient fine-tuning approach that extends the context sizes of pre-trained large language models (LLMs), with limited computation cost. LLaMa had a context length of 2048, then Llama-2 had 4096, now Llama-3 has 8192. metalman123 airo-llongma-2-13B-16k-GPTQ - 16K long context llama - works in 24GB VRAM Nous-Yarn-Llama-2-13b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 600 steps. llama2 long is a very systematic work for an ultra-long context, carried out the model structure, training data, training method perspective for experimentation and analysis, and the output of a very much effective point of view. Beta Was this translation helpful? Give feedback. Historically, large language models (LLMs) were significantly limited by the amount of text (or tokens) that could be passed to Effective Long-Context Scaling of Foundation Models. The maximum context length of open source LLMs has followed a similar trend: while the first Nous-Yarn-Llama-2-13b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 600 steps. We are excited to share this work with the open-source community and make sustained progress towards better, longer-context models. Experiment This blog post clarifies our mission as a data framework along with our view of what long-context LLM architectures will look like. You need big GPUs to train and inference long context. How is context length implemented? The context length is simply the maximum length of the input sequence. We compare DuoAttention with H2O, TOVA, and StreamingLLM using the same KV cache budget. Open 1 task done. . The final data mixture used for model CEPE employs a small encoder to process long inputs chunk by chunk, enabling the frozen decoder to utilize additional contexts via cross-attention. But once X fills up, you need to start deleting stuff. Llama 1 released 7, 13, 33 and 65 billion parameters while Llama 2 has7, 13 and 70 billion parameters; Llama 2 was trained on 40% more data; Llama2 has double the context length; Llama2 was fine tuned for helpfulness and safety; Please review the research paper and model cards (llama 2 model card, llama 1 model card) for more differences. LLaMA 2 Long is a series of long-context LLMs built through continual pretraining from LLAMA 2 with longer training sequences that support effective context windows of up to LongLoRA demonstrates strong empirical results on various tasks on LLaMA2 models from 7B/13B to 70B. Llama 3. In this work, we introduce ChatQA 2, a Llama3-based model designed to bridge the gap between open-access LLMs and leading proprietary models (e. To enable low latency responses for great user experiences, while also providing I was testing llama-2 70b (q3_K_S) at 32k context, with the following arguments: -c 32384 --rope-freq-base 80000 --rope-freq-scale 0. Long-context Summarization and Long-context QA. ai) Chunk + Document Hybrid Retrieval with Long-Context Embeddings (Together. We publish 32K and 64K variants. updated May 16 Llama 2 Long is an extension of Llama 2, On researchbenchmarks, our models achieve consistent improvements on most regular tasksand significant improvements on long-context tasks over LLAMA 2. The larger the window, the more comprehensive tasks like summarization can be. 1 8B at different context windows Will Long Context LLMs Subsume RAG? LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models []Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, Jiaya Jia. Sort by: Best. Our model series are built through continual pretraining from LLAMA 2 We worked directly with u/kaiokendev, to extend the context length of the Llama-2 13b and 7b models through fine-tuning. Fine tuning with RoPE scaling is a lot cheaper and less effective than training a model from scratch with long context length. Prometheus 2. , 2023b). We follow the recipe of Llama-2-7B-32K, and train our model with the BookSum dataset and Multi-document Question Answering. [2023. Considering how minor the adjustments in the Llama 2 Long paper were, it's surprising that no one has replicated it yet. 7B: 32K: NousResearch/Yarn We present Position Interpolation (PI) that extends the context window sizes of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal fine-tuning (within 1000 steps), while demonstrating strong empirical results on various tasks that require long context, including passkey retrieval, language modeling, and long document summarization System Info llamafactory newest version Reproduction 需要finetune 72B模型+32k context 用llamafactory总是爆显存 hiyouga / LLaMA-Factory Public. Broader Application Range: The model has identical performance to LLaMA 2 under 4k context length, performance scales directly to 8k, and works out-of-the-box with the new version of transformers (4. But this RoPE scaling makes the model "dumber” especially at 2x and beyond. The Llama 2 release introduces a family of pretrained and fine-tuned LLMs, ranging in scale from 7B to 70B parameters (7B, 13B, 70B). 18B-Instruct on the majority of long-context tasks despite having seen only 5% as many tokens during long-context training. 3, released in December 2024. We then proceed to train Llama-2-7B on 8 A100 by gradually increasing its rope base frequency to 1B. Question | Help How would you interact with a large input context, for example, source code of a long blog post? I read many posts so far, not quite asked this way It allows to run Llama 2 70B on 8 x Raspberry Pi 4B 4. Readme License. This repository contains code and tooling for the Abacus. That seems to be a typo since they mention 32k context model is trained on Llama 2 both on their website and in their paper. CAPE is efficient, generalizable, and versatile: trained with 8K-token documents, CAPE extends the context window of LLaMA-2 to 128K tokens, offering 10× of the For example, at every context length the model answered the question “Who was the first person to reach the South Pole?” as Robert Falcon Scott which is incorrect, the correct answer was Roald Amundsen. Navigation Menu Toggle navigation. Long sequence LLM are important for a long scientific article with more than 32K or Code Llama was trained on a 16k context window. Also you're living the dream with that much local compute. " "Through early experiments at the 7B scale, we identified a key limitation of LLAMA 2’s positional encoding (PE) that prevents the attention module from aggregating information of distant tokens. [2024. For example, training on the context length of 8192 needs 16x We publish variants of Llama 2 fine-tuned with YaRN at 32K, 64K and 128K context window length. Here we describe the shared setup between our ICL and finetuning experiments. It doesn't apply the same base frequency adjustment. Share Add a Comment. 5 (2024/05) Llama 3 long context. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. CEPE is efficient, generalizable, and versatile: trained with 8K-token documents, it extends the context window of LLAMA-2 to 128K tokens, offering 10x the throughput with only 1/6 of the memory. com Open. In order to get access to the model, and the resulting functionality that could arise from such long context windows is really exciting. LLaMA-2 has a context length of 4K tokens. Figure 1: The performance increases with more demonstrations far beyond the context window of the base Llama-2. Increasing Llama 2’s 4k The paper introduces three new evaluation tasks and proposes that these are a better measure of long context performance of LLMs than next-token perplexity. from Meta AI in 2023 presents Position Interpolation (PI) that extends the context window sizes of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal fine-tuning (within 1000 steps), while demonstrating strong empirical results on various tasks that require long context, including passkey retrieval, language modeling, and Llama 3. Look up Functionary on Huggingface. CodeLlama is 16k tokens. Unfortunately llama. Similarly, Claude 2 has a context length of 200k tokens and Gemini 1. We evaluate the LongAlpaca-7B-16k When u/kaiokendev first posted about linearly interpolating RoPE for longer sequences, I (and a few others) had wondered if it was possible to pick the correct scale parameter dynamically based on the sequence length rather than having to settle for the fixed tradeoff of maximum sequence length vs. Notably, our model is only trained with 512K sequence length while generalizing to nearly 1M context. Due to the high cost of continual pretraining on longer sequences, previously released long-context models are typically limited to scales of 7B/13B. They had a more clear prompt format that was used in training there (since it was actually included in LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models []Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, Jiaya Jia. no-execution opened this issue Oct Previous models have been limited in their understanding of long context by the positional encoder, which in the past was not able to extend beyond I think 2048 tokens for RoPE without changing the scaling factor. Extending LLaMA-2 to 32K context. 25 for 2. Llama 2's long context capabilities enable it to analyze large datasets, identify patterns over extended periods of time, and make more accurate predictions. 31) or with `trust_remote_code` for <= 4. Best. 2 Experimental setup. 5 Flash comes standard with a 1-million-token context window, and Gemini 1. In contrast, on the MiniMA-2-3B, PI passes only a few needle tests at longer lengths, This paper provides the first thorough understanding of RoPE extensions for long-context LLMs from an attention perspective, I will be releasing a series of Open-Llama models trained with NTK-aware scaling on Monday. Long Context Extension and Generalization in LLMs. Models; Datasets; Spaces; Posts; Docs; Solutions Pricing Log In Sign Up vsevolodl 's Collections. LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models []Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, Jiaya Jia. To enable low latency responses for great user experiences, while also providing high throughput for cost-efficient serving of these models, the NVIDIA platform is optimized at every layer of the technology stack. [5] Originally, Llama was only available as a Gemini 1. Moreover, with a cost-effective instruction tuning procedure that is free of expensive annotation, the presented models can already surpass gpt-3. However, the pre-defined size limits LLMs in many applications, like summarizing long documents or answering long questions. - abacusai/Long-Context You're absolutely right about llama 2 70b refusing to write long stories. The models pass all our evaluations and maintain perplexity at 16k extrapolation surpassing the performance of other Last month, we released Llama-2-7B-32K, which extended the context length of Llama-2 for the first time from 4K to 32K — giving developers the ability to use open-source AI for long-context tasks such as document The model has identical performance to LLaMA 2 under 4k context length, performance scales directly to 8k, and works out-of-the-box with the new version of transformers (4. 2 VLMs support long context lengths of up to 128K text tokens as well as a single image input at a resolution of 1120 x 1120 pixels. Our view is that while long-context LLMs will simplify certain parts of the RAG pipeline (e. You should NOT use a different context length unless the model is fine-tuned for an extended context length. There aren’t many 32k or 100k context datasets - especially in a chat/instruction format that can be used for supervised fine tuning or reinforcement learning. In addition, to make LongLoRA practical, we collect a dataset, LongQA, for supervised fine-tuning. Our final model, ProLong-8B, which is initialized from Llama-3 and trained on 40B tokens, demonstrates state-of-the-art long-context performance among similarly sized models at a length of 128K. jvrrsppftzhabuzkxwinijgtutskocntrgoeshgsoxtrauquadyuofbd
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