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Train 400x faster Static Embedding Models with Sentence Transformers
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Tim Dolan
macadeliccc
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macadeliccc/SOLAR-10.7B-Instruct-v1.0-laser:Adding `safetensors` variant of this model
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Train 400x faster Static Embedding Models with Sentence Transformers
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Adding `safetensors` variant of this model
#1 opened 5 days ago
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bartowski/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF
Text Generation
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nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
Text Generation
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rhymes-ai/Aria
Image-Text-to-Text
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naver/efficient-splade-VI-BT-large-query
Fill-Mask
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vidore/colqwen2-v0.1
Visual Document Retrieval
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Choosing the Category for a Supreme Court Case
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My tool calling playgrounds repo has been updated again to include the use of flux1-schnell or dev image generation. This functionality is similar to using Dall-E 3 via the
I have also included 2 notebooks that cover different ways to access Flux for your specific use case. The first method covers how to access flux via LitServe from Lightning AI. LitServe is a bare-bones inference engine with a focus on modularity rather than raw performance. LitServe supports text generation models as well as image generation, which is great for some use cases, but does not provide the caching mechanisms from a dedicated image generation solution.
Since dedicated caching mechanisms are so crucial to performance, I also included an example for how to integrate SwarmUI/ComfyUI to utilize a more dedicated infrastructure that may already be running as part of your tech stack. Resulting in a Llama-3.1 capable of utilizing specific ComfyUI JSON configs, and many different settings.
Lastly, I tested the response times for each over a small batch request to simulate a speed test.
It becomes clear quickly how efficient caching mechanisms can greatly reduce the generation time, even in a scenario where another model is called. An average 4.5 second response time is not bad at all when you consider that an 8B model is calling a 12B parameter model for a secondary generation.
Repo: https://github.com/tdolan21/tool-calling-playground
LitServe: https://github.com/Lightning-AI/LitServe
SwarmUI: https://github.com/mcmonkeyprojects/SwarmUI
@ decorator in ChatGPT. Once the function is selected, the model will either extract or improve your prompt (depending on how you ask).I have also included 2 notebooks that cover different ways to access Flux for your specific use case. The first method covers how to access flux via LitServe from Lightning AI. LitServe is a bare-bones inference engine with a focus on modularity rather than raw performance. LitServe supports text generation models as well as image generation, which is great for some use cases, but does not provide the caching mechanisms from a dedicated image generation solution.
Since dedicated caching mechanisms are so crucial to performance, I also included an example for how to integrate SwarmUI/ComfyUI to utilize a more dedicated infrastructure that may already be running as part of your tech stack. Resulting in a Llama-3.1 capable of utilizing specific ComfyUI JSON configs, and many different settings.
Lastly, I tested the response times for each over a small batch request to simulate a speed test.
It becomes clear quickly how efficient caching mechanisms can greatly reduce the generation time, even in a scenario where another model is called. An average 4.5 second response time is not bad at all when you consider that an 8B model is calling a 12B parameter model for a secondary generation.
Repo: https://github.com/tdolan21/tool-calling-playground
LitServe: https://github.com/Lightning-AI/LitServe
SwarmUI: https://github.com/mcmonkeyprojects/SwarmUI
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