ChatGLM-6B
basaran
| ChatGLM-6B | basaran | |
|---|---|---|
| 17 | 22 | |
| 39,341 | 1,281 | |
| 1.6% | - | |
| 8.4 | 10.0 | |
| 2 months ago | 3 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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ChatGLM-6B
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What are the current fastest multi-gpu inference frameworks?
ChatGLM seems to be pretty popular but I've never used this before.
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A CEO is spending more than $2,000 a month on ChatGPT Plus accounts for all of his employees, and he says it's saving 'hours' of time
There are also locally hosted options that approach the effectiveness of ChatGPT. This GLM for example was specifically trained to be able to be processed on a single consumer grade GPU
- Open Source Chinese LLMs
- ChatGLM-6B: run locally on consumer graphics card (6GB of GPU memory required)
- Ask HN: Open source LLM for commercial use?
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Coding LLaMa Modell?
A link to for y'all. Definitely gonna try to mess around with this!
- 关于GPT，AI和未来的一些社会经济问题，向诸位请教
- FLiPN-FLaNK Stack Weekly for 20 March 2023
- ChatGLM-6B - an open source 6.2 billion parameter English/Chinese bilingual LLM trained on 1T tokens, supplemented by supervised fine-tuning, feedback bootstrap, and Reinforcement Learning from Human Feedback. Runs on consumer grade GPUs
- ChatGLM: Open bilingual language model based on General Language Model framework
basaran
- OpenLLM
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Langchain and self hosted LLaMA hosted API
What are the current best "no reinventing the wheel" approaches to have Langchain use an LLM through a locally hosted REST API, the likes of Oobabooga or hyperonym/basaran with streaming support for 4-bit GPTQ?
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Run and create custom ChatGPT-like bots with OpenChat
Disclaimer: I am curating LLM-tools on github [1]
A few thoughts:
* allow for custom endpoint URLs, this way people can use open source LLMs with a fake openAI API backend like basaran[2] or llama-api-server[3]
* look into better embedding methods for info-retrieval like InstructorEmbeddings or Document Summary Index
* Don't use a single embedding per content item, use multiple to increase retrieval quality
1 https://github.com/underlines/awesome-marketing-datascience/...
2 https://github.com/hyperonym/basaran
3 https://github.com/iaalm/llama-api-server
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1-Jun-2023
open-source alternative to the OpenAI text completion API (https://github.com/hyperonym/basaran)
- Introducing Basaran: self-hosted open-source alternative to the OpenAI text completion API
- Basaran is an open-source alternative to the OpenAI text completion API
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Ask HN: What's the best self hosted/local alternative to GPT-4?
Guanaco-65B[0] using Basaran[1] for your OpenAI compatible API. You can use any ChatGPT front-end which lets you change the OpenAI endpoint URL.
[0] An fp4 finetune of LLaMA-30B by Tim Dettmers
[1] https://github.com/hyperonym/basaran
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Are all the finetunes stupid?
For lm-eval, I think you'd either need to take GPTQ's inference script and shim it into a model: https://github.com/EleutherAI/lm-evaluation-harness/tree/master/lm_eval/models or you might be able to use a project like https://github.com/hyperonym/basaran and then you could use the gpt3 model...
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Using the API in Node
There are also: - Basaran repo: "Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models". "...Compatibility with OpenAI API and client libraries..."; - llama-cpp-python repo: "Simple Python bindings for @ggerganov's llama.cpp library...". "...OpenAI-like API...".
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Researcher looking for help with how to prepare a finetuning dataset for models like Bloomz and Cerebras-GPT
I want to start with a totally freely available model, so again, that excludes things like LLaMA where the weights are only available through a wait list. The two models that most get my attention and (I think, and hope) fit my criteria of open availability are Cerebras-GPT (13b) and Bloomz (7b). The tools to process and fine-tune that seem most feasible to me, from my limit knowledge, are xturing and basaran.
What are some alternatives?
llama.cpp - LLM inference in C/C++
text-generation-inference - Large Language Model Text Generation Inference
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
openai-chatgpt-opentranslator - Python command that uses openai to perform text translations
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.
AutoGPTQ - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
datagen - Generate authentic looking mock data based on a SQL, JSON or Avro schema and produce to Kafka in JSON or Avro format.
llm-foundry - LLM training code for Databricks foundation models
accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support