Comments (6)
We already have Instruct v0.2 in: https://github.com/Lightning-AI/litgpt/blob/main/litgpt/config.py#L1393
When the pretrained model is added, if the config is exactly the same you can use the model_name
argument https://github.com/Lightning-AI/litgpt/blob/main/tutorials/download_model_weights.md#finetunes-and-other-model-variants
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Ah yes, I somehow overlooked this. I really need to wear glasses haha. Let's stay tuned for when the model gets added to the hub. Just checked here and it doesn't seem to have landed yet.
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There seems to be a GGUF version at https://huggingface.co/bartowski/Mistral-7B-v0.2-hf-GGUF, but I suggest we wait for the official one under https://huggingface.co/mistralai/
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People at MistralAI are so badass, that they provide a direct link to download the weights.
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Haha thanks!
The only caveat is it doesn't come with any prior usage or license information and you don't know what you are downloading. I think we should probably wait for the Hub version.
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and you don't know what you are downloading.
I would say that this is the biggest issue.
Given the size of the archive it can contain anything, starting from a collection of memes to some interesting, relatively short, videos.
So, let's definitely weight.
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Related Issues (20)
- Something introduced a LoRA merging bug HOT 7
- Defaults fail on small block size for some models HOT 1
- LoRA model tokenizer configuration fails to load HOT 8
- TypeError: unsupported operand type(s) for -: 'float' and 'NoneType' HOT 7
- Gradients in GPT module of the finetuning/lora.py script are always zero HOT 6
- Explain how to pretrain on a custom dataset
- Add `--warmup_fraction` to pretraining script HOT 2
- Batch Inference (batch size > 1) HOT 1
- LongLora fine-tuning support HOT 3
- False positive warning about mixed precision in `merge_lora.py`
- Categorize SFT and Pretraining data HOT 3
- Meaningful error if no validation split fraction is provided in custom JSON data module HOT 1
- Decide what to do about 16bit weights trained with mixed precision
- Determine the default precision and quantization in chat and generate HOT 1
- Question about using custom dataset for pretraining HOT 3
- Deployment example HOT 2
- Automatically infer and download the tokenizer in pretrain?
- 1.8B H2O model HOT 3
- Is it possible to run Llama 2 70B with 80Gb? HOT 3
- Feature Request: A generation API that does not load the model weights every time HOT 3
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