Mistral-7B-v0.2-base 7B Mistral LF 32K 32K LLaMA-2-7B-LongLora 7B LLaMA-2 Shifted Short Attention 100K 100K Yi-6B-200K 6B Yi Position Interpolation +LF 200K 200K InternLM2-7B-base 7B InternLM Dynamic NTK 32K 200K Long-LLaMA-code-7B 7B LLaMA-2 Focused Transformer 8K 256K RWKV-5-Wor...
近期文章推荐 浅谈LLM的长度外推 浅谈训练LLM的一些小技巧 再谈长度外推 LLM推理加速-Medusa LLM监督微调-数据筛选 浅谈长文摘要 LLM长context微调技巧-LongLora 从开源LLM中学模型架构优化-Mistral 7B 从开源LLM中学模型架构-基础篇 大模型LLM微调的碎碎念 发布于 2023-10-11 18:27・IP 属地湖北 ...
mistral = [ # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json dict( org="mistralai", name="Mistral-7B-{}v0.1", padded_vocab_size=32000, block_size=4096, # should be 32768 but sliding window attention is not implemented n_layer=32, n_query_groups=8, rotary_...
In addition, we also publish 8K context window versions of Llama 2 7B fine-tuned with NTK-aware and YaRN (Table 1 in the conference paper). Mistral With the release of v2 of our paper we are also publishing 64K and 128K variants of Mistral 7B v0.1. SizeContextLink 7B 64K NousResearch...
Unrestricted context window for Mistral Large and Llama Models for watsonx.ai Software/ on-prem See this idea on ideas.ibm.com One of the differentiators of Mistral large is its large context window. In watsonx however, we restrict this context window for Mistral and Llama models because of ...
we readjust LongRoPE on 8k length to recover the short context window performance. Extensive experiments on LLaMA2 and Mistral across various tasks demonstrate the effectiveness of our method. Models extended via LongRoPE retain the original architecture with minor modifications to the ...
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🏀Mistral7B32K🏀 PC-37.01 🏐Longchat13B16K🏐 Vanilla-35.87 🏀Longchat13B16K🏀 PC-35.61 🏀Zephyr7B32K🏀 PC-30.23 🎾🍿Longchat13B16K🎾 RAG🍿 OpenAI29.95 Online Evaluation Welcome to Marathon Race, online evaluation is now available athttps://openbenchmark.online/marathon. ...
DatasetBaichuan2-7B-ChatMistral-7B-Instruct-v0.2Qwen-7B-ChatInternLM2-Chat-7BChatGLM3-6BBaichuan2-13B-ChatMixtral-8x7B-Instruct-v0.1Qwen-14B-ChatInternLM2-Chat-20B MMLU 50.1 59.2 57.1 63.7 58.0 56.6 70.3 66.7 66.5 CMMLU 53.4 42.0 57.9 63.0 57.8 54.8 50.6 68.1 65.1 AGIEval 35.3 34.5 39.7...
我们用三种流行的llm (Llama-2, Mistral和SOLAR)在三种任务类型上来评估Self-Extend。我们提出的Self-Extend方法大大提高了长上下文理解能力,在某些任务上甚至优于基于微调的方法。这些结果强调了Self-Extend是上下文窗口扩展的有效解决方案。SelfExtend的卓越性能还展示了大型语言模型在有效处理长上下文方面的潜力。 2 ...