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---
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: []
---
<!-- 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](https://huggingface.co/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 |