ReAct
mistral-inference
| ReAct | mistral-inference | |
|---|---|---|
| 1 | 10 | |
| 1,679 | 9,029 | |
| - | 4.0% | |
| 4.8 | 7.1 | |
| 4 months ago | 6 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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ReAct
mistral-inference
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EU Approves AI Act
That wasn't part of the question, but since you ask.
https://mistral.ai/
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Mistral 7B vs. Mixtral 8x7B
A French startup, Mistral AI has released two impressive large language models (LLMs) - Mistral 7B and Mixtral 8x7B. These models push the boundaries of performance and introduce a better architectural innovation aimed at optimizing inference speed and computational efficiency.
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How to have your own ChatGPT on your machine (and make him discussed with himself)
However, some models are publicly available. It’s the case for Mistral, a fast, and efficient French model which seems to outperform GPT4 on some tasks. And it is under Apache 2.0 license 😊.
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How to Serve LLM Completions in Production
I recommend starting either with llama2 or Mistral. You need to download the pretrained weights and convert them into GGUF format before they can be used with llama.cpp.
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Stuff we figured out about AI in 2023
> Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!
actually its not just a basic version. Llama 1/2's model.py is 500 lines: https://github.com/facebookresearch/llama/blob/main/llama/mo...
Mistral (is rumored to have) forked llama and is 369 lines: https://github.com/mistralai/mistral-src/blob/main/mistral/m...
and both of these are SOTA open source models.
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How Open is Generative AI? Part 2
MistralAI, a French startup, developed a 7.3 billion parameter LLM named Mistral for various applications. Committed to open-sourcing its technology under Apache 2.0, the training dataset details for Mistral remain undisclosed. The Mistral Instruct model was fine-tuned using publicly available instruction datasets from the Hugging Face repository, though specifics about the licenses and potential constraints are not detailed. Recently, MistralAI released Mixtral 8x7B, a model based on the sparse mixture of experts (SMoE) architecture, consisting of several specialized models (likely eight, as suggested by its name) activated as needed.
- Mistral website was just updated
- Mistral AI – open-source models
- Mistral 8x7B 32k model [magnet]
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Ask HN: Why the LLaMA code base is so short
I was getting into LLM and I pick up some projects. I tried to dive into the code to see what is secret sauce.
But the code is so short to the point there is nothing to really read.
https://github.com/facebookresearch/llama
I then proceed to check https://github.com/mistralai/mistral-src and suprsingly it's same.
What is exactly those codebases? It feels like just download the models.
What are some alternatives?
ragas - Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
lida - Automatic Generation of Visualizations and Infographics using Large Language Models
EasyEdit - [ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.
LLM-Training-Puzzles - What would you do with 1000 H100s...
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
AutoCog - Automaton & Cognition
llama - Inference code for Llama models
llm-search - Querying local documents, powered by LLM
text-generation-webui-colab - A colab gradio web UI for running Large Language Models
FastLoRAChat - Instruct-tune LLaMA on consumer hardware with shareGPT data
chameleon-llm - Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".