Text-Summarization-using-NLP
LLM-Finetuning-Hub
| Text-Summarization-using-NLP | LLM-Finetuning-Hub | |
|---|---|---|
| 2 | 6 | |
| 33 | 638 | |
| - | - | |
| 0.0 | 9.5 | |
| about 1 year ago | 25 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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Text-Summarization-using-NLP
LLM-Finetuning-Hub
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Zephyr-7B QLoRA Benchmark for Summarization and Classification
Hi everyone, we've been working on benchmarking different open-source LLMs. We measure, in particular, on the performance of these models once finetued (via QLoRA) on classic NLP downstream tasks like summarization and classification. We also put particular emphasis on benchmarking inference time/cost for these models once deployed.
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Show HN: Finetuning LLMs: Open-source vs. Close-source
Hello all,
I have been working on benchmarking different LLMs -- both open-source and closed-source.
Repo: https://github.com/georgian-io/LLM-Finetuning-Hub
Precisely, I am comparing their out-of-the-box capabilities (prompting) and their fine-tuned conterparts!
So far, the following models have been benchmarked:
Open-Source:
- FLaNK Stack Weekly for 12 September 2023
- [P][R] Finetune LLMs via the Finetuning Hub
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Show HN: Leverage Falcon 7B blog post
- Finetuning with QLoRA
I evaluate how Falcon does on classification tasks when compared to Bert and Distilbert.
Moreover, I talk about different ways you can deploy the model, and the associated costs!
The code for all of my experiments are available on: https://github.com/georgian-io/LLM-Finetuning-Hub
Happy reading and learning!
- Show HN: LLM Finetuning Hub
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