metadata
license: mit
base_model: microsoft/Phi-3-medium-128k-instruct
tags:
- generated_from_trainer
model-index:
- name: outputs/phi3-medium-128k-14b.8e6
results: []
Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| Quant | Model Size | lm_head |
|---|---|---|
See axolotl config
axolotl version: 0.4.0
base_model: microsoft/Phi-3-medium-128k-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: shisa-llama3-70b-v1.8e6
chat_template: chatml
datasets:
- path: augmxnt/ultra-orca-boros-en-ja-v1
type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/phi3-medium-128k-14b.8e6
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
neftune_noise_alpha: 5
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_8bit
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: linear
learning_rate: 0.000008
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: True
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true
special_tokens:
pad_token: "<|endoftext|>"
outputs/phi3-medium-128k-14b.8e6
This model is a fine-tuned version of microsoft/Phi-3-medium-128k-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3339
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: 8e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.8309 | 0.0021 | 1 | 2.3406 |
| 0.7688 | 0.2513 | 121 | 0.4958 |
| 0.6435 | 0.5026 | 242 | 0.3830 |
| 0.5286 | 0.7539 | 363 | 0.3626 |
| 0.5559 | 1.0052 | 484 | 0.3549 |
| 0.4651 | 1.2425 | 605 | 0.3486 |
| 0.5294 | 1.4938 | 726 | 0.3432 |
| 0.5453 | 1.7451 | 847 | 0.3392 |
| 0.5258 | 1.9964 | 968 | 0.3376 |
| 0.4805 | 2.2331 | 1089 | 0.3357 |
| 0.4552 | 2.4844 | 1210 | 0.3352 |
| 0.5358 | 2.7357 | 1331 | 0.3339 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1