metadata

license: other
license_name: microsoft-research-license
license_link: >-
  https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2/blob/main/LICENSE
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. It achieves the following results on the evaluation set:

Loss: 0.3524

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