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Sure, here are two: 1. Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks https://huggingface.co/papers/2305.14201 https://huggingface.co/papers/230

Sure, here are two: 1. Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks https://huggingface.co/papers/2305.14201 https://huggingface.co/papers/2305.14201 2. Gorilla: Large Language Model Connected with Massive APIs https://arxiv.org/abs/2305.15334 https://arxiv.org/abs/2305.15334 Consider also these 2 papers supporting the feasibility of fine-tuning: 3. LIMA: Less Is More for Alignment [showing that a very small number of high quality examples is sufficient to align a base model] https://arxiv.org/abs/2305.11206 https://arxiv.org/abs/2305.11206 4. QLoRA: Efficient Finetuning of Quantized LLMs [showing that LLMs can now be fine-tuned quickly on consumer-grade GPUs] https://arxiv.org/abs/2305.14314 https://arxiv.org/abs/2305.14314 —- Adding up these developments (all of which occurred during the span of one week), I don’t see how huge, slow, general-purpose models maintain their relevance in the long term, when a lean, domain-focused model is right there within reach of every application developer.

fareesh 3y ago
Sorry I should have been more specific - I was limiting my question to the bigger models. The smaller (~7B) models are feasible with these approaches.
- anon373839 3y ago
  Ah. Perhaps the larger models will find use in in-house deployments where companies want their employees to have access to ChatGPT-like general purpose assistants, but want to prevent data from leaving their premises. LIMA shows the LLaMA 65B hitting a quality level somewhere between DaVinci-003 and GPT-4 with minimal alignment, so the base models are probably powerful enough already for this to work. Just speculating.
- MacsHeadroom 3y ago
  One benefit of finetuning larger models, like 65B, is to free up limited context space vs few-shot prompting. If you want a specific kind of interaction with the model then you could take up 1/3rd of the 2048 token context window with few-shot or you could simply finetune it with QLoRA for a few hours on a consumer GPU and then get to use the full 2048 context with the finetuned model.
sgu999 3y ago
Wow, thanks a lot! :)

fareesh 3y ago

Sorry I should have been more specific - I was limiting my question to the bigger models. The smaller (~7B) models are feasible with these approaches.

anon373839 3y ago
Ah. Perhaps the larger models will find use in in-house deployments where companies want their employees to have access to ChatGPT-like general purpose assistants, but want to prevent data from leaving their premises. LIMA shows the LLaMA 65B hitting a quality level somewhere between DaVinci-003 and GPT-4 with minimal alignment, so the base models are probably powerful enough already for this to work. Just speculating.
MacsHeadroom 3y ago
One benefit of finetuning larger models, like 65B, is to free up limited context space vs few-shot prompting. If you want a specific kind of interaction with the model then you could take up 1/3rd of the 2048 token context window with few-shot or you could simply finetune it with QLoRA for a few hours on a consumer GPU and then get to use the full 2048 context with the finetuned model.

sgu999 3y ago

Wow, thanks a lot! :)