Comments (7)
Yes! We will release a LLaMA v2 as soon as we can get our hands on some compute!
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Don't we have all the training data to just do that on our own? The fine-tuning shouldn't be that hard to get training.
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ugh maybe not #46 . I haven't read a self-instruct paper. Isn't it just doing inference to generate more training data? Maybe jsonformer is involved. idk
edit: Okay so no jsonformer. GPT-4 was used for self-instruct:
Instruction Generation Guided by the self-instruct paradigm [42], we employed GPT-4 to generate
synthetic instruction data. We provided three in-context examples, along with a reference API
documentation, and tasked the model with generating real-world use cases that call upon the API.
We specifically instructed the model to refrain from using any API names or hints when creating
instructions. We constructed six examples (Instruction-API pairs) for each of the three model hubs.
These 18 points, were the only hand-generated or modified data. For each of our 1,645 API datapoints,
we sample 3 of 6 corresponding instruction examples to generate a total of 10 instruction-api pairs as
demonstrated in Figure 3. We would like to highlight that we only need to employ GPT-4 to generate
the instructions and this can be swapped with open-source alternatives such as LLaMA, Alpaca, etc.
Maybe this code will be shared? It should be relatively trivial (Thanks to the nuances described in the paper) with some tinkering anyway.
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Hi @ShishirPatil is the training code being released as well? Thanks!
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Hey @TomExMachina all the training data is at https://github.com/ShishirPatil/gorilla/tree/main/data/apibench All files with the _train.json
suffix!
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Is there any How-To guide to fine-tune/training for those unfamiliar with the topic but would like to contribute?
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@tonxxd There is a community contributed PR in the works here #59 Thanks for your interest @yordis ! If you are interested in contributing APIs, we have a README https://github.com/ShishirPatil/gorilla/tree/main/data#how-to-contribute Let me know if you have any follow up questions!
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Related Issues (20)
- [RAFT] Publish Pypi package with raft, eval and format scripts
- [Apibench] Resume interrupted LLM generations from last generation
- [BFCL] Get rid of legacy naming convention for LLM generated files
- [BFCL] Sanity check should be optional and by default off HOT 2
- [bug] OpenFunctions-v2: how to continue conversation? HOT 1
- [BFCL] Inconsistency in leaderboard scores HOT 2
- Question about AST evaluation for Java HOT 3
- Java/Javascript Scores HOT 1
- LeaderBoard data generation HOT 1
- Set Model Temperature to 0 for Consistent Leaderboard Results HOT 5
- BFCL setup instruction is very difficult to follow
- Clarify Documentation About Running The Benchmark HOT 1
- Single Source of Truth
- Questions about the evaluation criteria. HOT 3
- [Apibench] No module named 'tree_sitter_java' HOT 2
- Evaluation using vLLM and other tools HOT 1
- Test data error in executable parallel multiple function HOT 2
- distutils.errors.CompileError: command '/usr/bin/cc' failed with exit code 1
- [bug] Hosted Gorilla: <Issue> HOT 2
- LangChain Integration of Gorilla OpenFunctions-v2 HOT 5
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