Comments (4)
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Our model can largely be plugged and played in repos that support llama 2 (including BitsandBytes and SFT repos like FastChat. For your case, you need to find a training script that supports databricks-dolly-15k.jsonl dataset format and change the model name to our released checkpoint. Just make sure you have the latest version of HF transformers to support MQA. We are working on fine-tuning our model as well. Will be releasing something, probably next week.
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@TapendraBaduwal you should probably wait till the models fully trained and then ask for this, but there was a mention sft in one of the closed issues
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@jzhang38 For fine-tune i am using Parameter-Efficient Fine-Tuning (PEFT) . PEFT supports the QLoRa method to fine-tune a small fraction of the LLM parameters with 4-bit quantization. By merge the adapter weights. It is the right way to fine-tune this tiny model ?
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Related Issues (20)
- Should this line use args.seed instead of seed=42?
- Pretraining failing on IndexError: list index out of range in file packed_dataset.py HOT 1
- A question on learning rate decay schedule HOT 1
- Why FSDP not DPP?
- On the visualization of Wandb in fine-tuning
- Is there any simple demo of fine-tuning TinyLlama HOT 4
- More intermediate checkpoints in < 240k steps
- The results under the FastChat framework are quite bizarre?
- 模型和代码欢迎发布到wisemodel.cn开源社区
- Encountered an issue while loading the model using transformers HOT 1
- A potential bug in multi-GPU training HOT 1
- Where is the pretraing example of llama-1.1b-chat
- Would it be possible to provide help with evaluation?
- On which will it run better
- Training Run - New Tokenizer HOT 1
- Llama 3 HOT 2
- model结构 HOT 1
- model.py HOT 1
- Clarify Chinese support or not on README HOT 1
- 有没有chat模型的其余benchmark的报道,例如mt-bench?
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