prd101-wd/phi1_5-sentiment-merged
014
<!-- 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. -->
<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.10.0.dev0
base_model: microsoft/phi-1_5
# optionally might have model_type or tokenizer_type
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
load_in_8bit: false
load_in_4bit: true
datasets:
- #path: garage-bAInd/Open-Platypus
path: /workspace/data/sentiment.jsonl
type: alpaca
dataset_prepared_path:
val_set_size: 5
output_dir: /workspace/outputs/phi-sentiment-out
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
#axolotl own suggestion
eval_sample_packing: False
adapter: qlora
#lora_model_dir:
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_torch_fused
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
#tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: True
resume_from_checkpoint:
logging_steps: 1
#flash_attention: true
flash_attention: false
warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.1
resize_token_embeddings_to_32x: true
special_tokens:
pad_token: "<|endoftext|>"</details><br>
workspace/outputs/phi-sentiment-out
This model is a fine-tuned version of microsoft/phi-1_5 on the /workspace/data/sentiment.jsonl dataset. It achieves the following results on the evaluation set:
- Loss: 0.2148
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- trainbatchsize: 4
- evalbatchsize: 4
- seed: 42
- gradientaccumulationsteps: 2
- totaltrainbatch_size: 8
- optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.95) and epsilon=1e-05 and optimizer_args=No additional optimizer arguments
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 100
- num_epochs: 1.0
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1