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metadata
license: mit
library_name: peft
tags:
  - llama-factory
  - lora
  - generated_from_trainer
base_model: cognitivecomputations/dolphin-2_6-phi-2
model-index:
  - name: dolphin-2_6-phi-2-sft-glaive-function-calling-v2-ep1-lora
    results: []

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-v2_convert dataset that I converted for llama_factory 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:

  • quant_method: QuantizationMethod.BITS_AND_BYTES
  • load_in_8bit: False
  • load_in_4bit: True
  • llm_int8_threshold: 6.0
  • llm_int8_skip_modules: None
  • llm_int8_enable_fp32_cpu_offload: False
  • llm_int8_has_fp16_weight: False
  • bnb_4bit_quant_type: nf4
  • bnb_4bit_use_double_quant: True
  • bnb_4bit_compute_dtype: float16

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • total_eval_batch_size: 2
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss
0.3453 1.0 376 0.3524

Framework versions

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