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2 months ago | 
theserverside.com | Cameron McKenzie |AutoModel ForCausalLM
There are numerous ways to run large language models such as DeepSeek, Claude or Meta's Llama locally on your laptop, including Ollama and Modular's Max platform. But if you want to fully control the large language model experience, the best way is to integrate Python and Hugging Face APIs together.
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Dec 2, 2024 | 
hackster.io | AutoModel ForCausalLM
Things used in this project IntroductionIn this hands-on lab, we will continuously explore AI applications at the Edge, going from the basic setup of the Florence-2, Microsoft’s state-of-the-art vision foundation model, to advanced implementations on devices like the Raspberry Pi.Why Florence-2 at the Edge?
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Nov 20, 2024 | 
huggingface.co | AutoModel ForCausalLM
ariG23498 Aritra Roy Gosthipaty melhoushi Mostafa Elhoushi facebook pcuenq Pedro Cuenca reach-vb Vaibhav Srivastav Self-speculative decoding, proposed in LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding is a novel approach to text generation. It combines the strengths of speculative decoding with early exiting from a large language model (LLM). This method allows for efficient generation by using the same model's early layers for drafting tokens, and later layers for...
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Nov 19, 2024 | 
dev.to | AutoModel ForCausalLM
Today, I want to introduce an open-source framework I’ve been working on: AnyModal. During my work on machine learning projects, I struggled to find flexible solutions for training multimodal LLMs. While there are plenty of great tools for specific tasks—like image classification or audio processing—there was no straightforward way to combine these modalities with large language models (LLMs).
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Sep 27, 2024 | 
huggingface.co | AutoModel ForCausalLM
Zamba2-7B is a hybrid model composed of state-space (Mamba) and transformer blocks. It broadly follows the Zamba architecture which consists of a Mamba backbone alternating with shared transformer blocks (see diagram in Model Details). Zamba2-7B possesses four major improvements over Zamba1:1.) Mamba1 blocks have been replaced with Mamba2 blocks.
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Sep 20, 2024 | 
analyticsvidhya.com | AutoModel ForCausalLM
Q1. What is a foundation model? A. A foundation model is a large machine learning model trained on huge amounts of diverse data, enabling it to generalize across a wide range of tasks. LLMs are a type of foundation models trained on vast amounts of textual data. Q2. What is AI hallucination? A. AI hallucination refers to the phenomenon where an AI model generates information that seems accurate but is incorrect or fabricated.
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Aug 21, 2024 | 
blog.paperspace.com | James Skelton |AutoModel ForCausalLM
FLUX has been taking the internet by storm this past month, and for good reason. Their claims of superiority to models like DALLE 3, Ideogram, and Stable Diffusion 3 have proven well founded. With capability to use the models being added to more and more popular Image Generation tools like Stable Diffusion Web UI Forge and ComyUI, this expansion into the Stable Diffusion space will only continue. Since the model's release, we have also seen a number of important advancements to the user workflow.
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Jul 31, 2024 | 
huggingface.co | AutoModel ForCausalLM |Import Os
Gemma 2 model card Model Page: GemmaResources and Technical Documentation:Terms of Use: TermsAuthors: Google Model Information Summary description and brief definition of inputs and outputs. Description Gemma is a family of lightweight, state-of-the-art open models from Google,built from the same research and technology used to create the Gemini models.
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Jul 4, 2024 | 
unite.ai | Aayush Mittal |AutoModel ForCausalLM
Gemma 2 builds upon its predecessor, offering enhanced performance and efficiency, along with a suite of innovative features that make it particularly appealing for both research and practical applications. What sets Gemma 2 apart is its ability to deliver performance comparable to much larger proprietary models, but in a package that's designed for broader accessibility and use on more modest hardware setups.
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Jun 24, 2024 | 
blog.csdn.net | AutoModel ForCausalLM
Qwen2的web搭建(streamlit) 千问2前段时间发布了，个人觉得千问系列是我用过最好的中文开源大模型，所以这里基于streamlit进行一个千问2的web搭建，来进行模型的测试 该文档中使用的千问模型为7B-Instruct，需要5g以上的显存，如果是轻薄本不建议进行本地测试（下图为测试时的实际显存占用） 对于环境的基本要求 transformers torch streamlit sentencepiece accelerate transformers_stream_generator 上述是基础的环境准备，可以用conda创建一个新的环境来进行配置。在下载库时可以使用清华大学的镜像进行加速，如下所示 pip install transformers -i https://pypi.tuna.tsinghua.edu.cn/simple 这里推荐使用huggingface镜像网站进行下载，因为在下载中断后，再次请求时会从上次中断的地方继续，而不是重新下载。 https://hf-mirror.com 以千问为例，在终端的下载请求为 huggingface-cli...
