llm-awq
FLiPStackWeekly
| llm-awq | FLiPStackWeekly | |
|---|---|---|
| 7 | 83 | |
| 1,902 | 14 | |
| 10.9% | - | |
| 8.0 | 9.9 | |
| 8 days ago | 1 day ago | |
| Python | ||
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
llm-awq
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TinyChat: Large Language Model on the Edge
TinyChat is an efficient, lightweight, Python-native serving framework for 4-bit LLMs by AWQ. It delivers 2.3x generation speed up on RTX4090.
Code: https://github.com/mit-han-lab/llm-awq/tree/main/tinychat
- FLaNK Stack Weekly 23 Oct 2023
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New base model InternLM 7B weights released, with 8k context window.
I am having trouble finding any 8bit GPTQ models at all, there don't seem to be any on HF it's almost all 4bit with the odd 3bit of the big ones. Suspect I will have to make my own for eval purposes but it's lower priority on my list then finding a 4bit that's GPU friendly but doesn't have such a performance penalty... Looking at AWQ they have 3 and 4bit versions.
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Llama33B vs Falcon40B vs MPT30B
Using the currently popular gptq the 3bit quantization hurts performance much more than 4bit, but there's also awq (https://github.com/mit-han-lab/llm-awq) and squishllm (https://github.com/SqueezeAILab/SqueezeLLM) which are able to manage 3bit without as much performance drop - I hope to see them used more commonly.
- New hardware-friendly quantization method
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Activation-Aware Weight Quantization for LLM Compression Outperforms GPTQ
Better quantization would have a direct and meaningful impact for everyone running local LLMs. The technique has already been applied to both Vicuna and the multimodal LLaMA variant LLaVA.
https://github.com/mit-han-lab/llm-awq
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New quantization method AWQ outperforms GPTQ in 4-bit and 3-bit with 1.45x speedup and works with multimodal LLMs
GitHub: https://github.com/mit-han-lab/llm-awq
FLiPStackWeekly
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SqueezeLLM - [ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization
gorilla-cli - LLMs for your CLI
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
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Voyager - An Open-Ended Embodied Agent with Large Language Models
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CML_AMP_AI_Text_Summarization_with_Amazon_Bedrock - CML_AMP_AI_Text_Summarization_with_Amazon_Bedrock
create-nifi-pulsar-flink-apps - How to create a real-time scalable streaming app using Apache NiFi, Apache Pulsar and Apache Flink SQL
kafka-streams-dashboards - showcases Grafana dashboards for Kafka Stream applications leveraging client JMX metrics.
FLiP-PulsarSummit2022Asia - FLiP-PulsarSummit2022Asia: Pulsar Summit Asia 2022