Comments (8)
Not yet, but we will start tracking this as a feature request! Thanks for the suggestion.
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either of these would work. but being able to retrieve the model post finetune is critical to use this.
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@FarziBuilder can you sketch out the experience you're thinking about about in more detail? Is it something like FineTune.publish(my_finetune_id, "huggingface")
? Or would you be ok with an API to download a zip file that you yourself upload to HF?
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Ok after some discussion internally, we'll go for the latter (downloading an artifact that you can push to HF yourself).
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@yixu34 by when can it be added?
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@FarziBuilder ETA is ~2 weeks
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Just following up on this, we have things merged, but need to do some additional testing. There are some other items that have come up for us, should hopefully merge this in the next week or so.
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After #237, a fine-tuned model artifact can be downloaded successfully.
tested out this feature and it works. Please refer to the Model.download() documentation. Then, you can push to HF. Hope this helps!
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Related Issues (20)
- [Feature Request] support InternLM HOT 1
- max token length for finetune and completion endpoints on Lllama-2? HOT 2
- Bug: API calls don't work on Windows due to os.path.join HOT 1
- Fine-tuning API should return immediately with a clear error message if the input is invalid
- Can we fine tune with data stored in local csv file? HOT 9
- ⚠️ LLM Engine fine-tuning maintenance ⚠️ HOT 4
- GQA for Llama 2 7B and 13B models
- Investigate CUDA graphs
- RetNet adaptation
- Speculative decoding
- PEFT adapters with continuous batching
- Further reduction of pod cold start time
- Test out spot instances HOT 1
- Error: Internal Server Error: <class 'AttributeError'>: 'CreateFineTuneResponse' object has no attribute 'artifact_id' HOT 4
- Control frequency - completion HOT 3
- self host on runnpod HOT 1
- Add support for Mistral-7B in the Completions API HOT 2
- Add support for Mistral in the FineTune API
- Cannot pass in PEFT configs when creating a finetuning job HOT 4
- Integrate TensorRT-LLM
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