TechQA.

Question

Is it possible to fine tune or use RAG on the CoreML version of Llama2?

score 208 · Answer 1 · 2023-10-13 16:11:26

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Answer

208

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Is it possible to fine tune or use RAG on the CoreML version of Llama2?

208 views Asked by Mike Ike At 13 October 2023 at 16:11

score 1723 · Answer 2 · 2023-10-17 14:37:12

Compare two strings by meaning using LLMs

1.7k views Asked by root At 17 October 2023 at 14:37

score 313 · Answer 3 · 2023-10-14 11:16:29

Implementation (and working) differences between AutoModelForCausalLMWithValueHead vs AutoModelForCausalLM?

313 views Asked by Deshwal At 14 October 2023 at 11:16

score 109 · Answer 4 · 2023-10-18 07:09:33

How do I know the right data format for different LLMs finetuning？

109 views Asked by John At 18 October 2023 at 07:09

score 37 · Answer 5 · 2023-10-16 07:55:20

I am trying to make a product which will reformat the answer using the question and Sql_answer as data

37 views Asked by S_K At 16 October 2023 at 07:55

score 281 · Answer 6 · 2023-10-10 06:39:16

CUDA OutOfMemoryError but free memory is always half of required memory in error message

281 views Asked by olivarb At 10 October 2023 at 06:39

score 773 · Answer 7 · 2023-10-17 07:04:56

Query with my own data using langchain and pinecone

773 views Asked by javascript-wtf At 17 October 2023 at 07:04

score 369 · Answer 8 · 2023-10-18 12:28:45

Could not find a version that satisfies the requirement python-magic-bin

369 views Asked by Debrup Paul At 18 October 2023 at 12:28

score 549 · Answer 9 · 2023-10-14 21:28:49

Any possibility to increase performance of querying chromadb persisted locally

549 views Asked by mlee_jordan At 14 October 2023 at 21:28

score 44 · Answer 10 · 2023-10-12 11:23:02

Grid based decision making with Llama 2

44 views Asked by skvp At 12 October 2023 at 11:23

TechQA.

List Question

Is it possible to fine tune or use RAG on the CoreML version of Llama2?

Compare two strings by meaning using LLMs

Implementation (and working) differences between AutoModelForCausalLMWithValueHead vs AutoModelForCausalLM?

How do I know the right data format for different LLMs finetuning？

I am trying to make a product which will reformat the answer using the question and Sql_answer as data

CUDA OutOfMemoryError but free memory is always half of required memory in error message

Query with my own data using langchain and pinecone

Could not find a version that satisfies the requirement python-magic-bin

Any possibility to increase performance of querying chromadb persisted locally

Grid based decision making with Llama 2

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