Yhyu13/dolphin-2_6-phi-2-sft-glaive-function-calling-v2-ep1-lora

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

dolphin-2_6-phi-2-sft-glaive-function-calling-v2-ep1-lora

This model is a fine-tuned version of cognitivecomputations/dolphin-2_6-phi-2 on the simple-function-calling-v2convert dataset that I converted for llamafactory https://huggingface.co/datasets/Yhyu13/glaive-function-calling-v2-llama-factory-convert, but with a subset of only the first 1000 data entries. It achieves the following results on the evaluation set:

• —Loss: 0.3524

Training script is availbale at ./scripts/local_ft_phi2_fn.sh)

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

The following bitsandbytes quantization config was used during training:

• —quantmethod: QuantizationMethod.BITSAND_BYTES
• —loadin8bit: False
• —loadin4bit: True
• —llmint8threshold: 6.0
• —llmint8skip_modules: None
• —llmint8enablefp32cpu_offload: False
• —llmint8hasfp16weight: False
• —bnb4bitquant_type: nf4
• —bnb4bitusedoublequant: True
• —bnb4bitcompute_dtype: float16

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-05
• —trainbatchsize: 1
• —evalbatchsize: 1
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 2
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 8
• —totalevalbatch_size: 2
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —num_epochs: 1.0

Training results

Framework versions

• —PEFT 0.7.0
• —Transformers 4.36.2
• —Pytorch 2.1.1+cu121
• —Datasets 2.14.7
• —Tokenizers 0.15.0