-
Firecrawl is highly suitable for custom web Retrieval-Augmented Generation (RAG) pipelines due to its advanced features and flexibility. Here are the key highlights:
1. **Smart LLM Scraping**: Conv…
-
Is it possible to run the AWQ models using the `run_vila.py` script?
I ran the following command:
```
python -W ignore llava/eval/run_vila.py \
--model-path Efficient-Large-Model/VILA1.5-3…
-
-
### Describe the feature
A recent paper titled "GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection" (https://arxiv.org/pdf/2403.03507.pdf) demonstrates a remarkable memory-effici…
-
**Is your feature request related to a problem? Please describe.**
LLM training is expensive, allowing sample packing is a more efficient way of training.
**Describe the use case**
I am trying to…
fire updated
11 months ago
-
Feature: Create queries from natural language. This will help analysts to create more efficient queries faster.
Description:
- When asking an investigation question an LLM solution will recommend…
-
周末看到这么个API还挺有意思的,不知道能不能整合进来?
[Tavily](https://tavily.com/) [Search API](https://app.tavily.com/playground) is a search engine optimized for LLMs and RAG, aimed at efficient, quick and persistent sear…
-
See the proposal: https://github.com/deepset-ai/haystack/pull/5540 and see [feature request](https://github.com/deepset-ai/haystack/issues/4926) for Haystack v1
---
LLMs clients output strings, bu…
-
We should consider adding support for AMD GPUs, which have been tested to be efficient for ML workloads.
References:
https://www.amd.com/en/technologies/deep-machine-learning
https://www.lamini.…
-
- [ ] async
- [x] less wasteful LLM calls
I'm cooking on the Database stuff right now, and it's clear that there's a few things we can do to make the daily run much more efficient.
The searches…