This test ablation probably shouldn't be used. It actually underperforms the original Phi 3 Medium Instruct model as it was trained w/ ChatML (but still performs better with the original Phi 3 instruct template). If you were to do a training run w/ this dataset, I'd recommend modifying the training to use the Phi 3 Instruct chat format.
Performance
Measured using a fork of Lightblue's Shaberi benchmark framework:
| Model | Average | ELYZA-tasks-100 | MT-Bench | Rakuda | Tengu-Bench |
|---|---|---|---|---|---|
| gpt-4-turbo-2024-04-09 | 8.75 | 8.78 | 8.74 | 9.18 | 8.31 |
| gpt-4o-2024-05-13 | 8.72 | 8.88 | 8.69 | 9.15 | 8.16 |
| gemini-1.5-pro | 8.58 | 8.58 | 8.93 | 9.20 | 7.61 |
| claude-3-opus-20240229 | 8.55 | 8.64 | 8.58 | 8.75 | 8.23 |
| CohereForAI/c4ai-command-r-plus | 7.69 | 7.50 | 7.43 | 9.05 | 6.79 |
| shisa-ai/shisa-v1-llama3-70b | 7.30 | 7.34 | 7.67 | 8.15 | 6.04 |
| gpt-3.5-turbo-0125 | 7.17 | 7.24 | 6.98 | 7.64 | 6.82 |
| shisa-ai/shisa-v1-llama3-70b.2e5 | 7.17 | 7.16 | 7.45 | 7.98 | 6.09 |
| karakuri-ai/karakuri-lm-8x7b-chat-v0.1 | 7.00 | 7.18 | 6.30 | 7.98 | 6.55 |
| karakuri-ai/karakuri-lm-70b-chat-v0.1 | 6.84 | 6.86 | 6.43 | 7.85 | 6.23 |
| lightblue/ao-karasu-72B | 6.81 | 7.19 | 6.54 | 7.25 | 6.27 |
| shisa-ai/shisa-v1-llama3-8b | 6.59 | 6.67 | 6.95 | 7.05 | 5.68 |
| microsoft/Phi-3-medium-128k-instruct | 6.48 | 7.10 | 5.92 | 6.84 | 6.04 |
| shisa-ai/shisa-swallowmx-13a47b-v1 | 6.17 | 6.48 | 6.07 | 7.11 | 5.03 |
| lightblue/suzume-llama-3-8B-japanese | 5.96 | 6.68 | 4.96 | 6.68 | 5.53 |
| augmxnt/shisa-gamma-7b-v1 | 5.82 | 5.96 | 5.02 | 6.85 | 5.47 |
| shisa-ai/shisa-v1-phi3-14b | 5.77 | 6.28 | 5.26 | 6.55 | 5.01 |
| shisa-ai/shisa-v1-gemma-8b | 5.64 | 6.50 | 5.42 | 5.10 | 5.55 |
| Rakuten/RakutenAI-7B-chat | 5.58 | 5.92 | 4.60 | 6.58 | 5.24 |
| lightblue/qarasu-14B-chat-plus-unleashed | 5.20 | 5.58 | 4.74 | 5.46 | 5.01 |
| shisa-ai/shisa-v1-mistral0.3-7b | 5.11 | 5.64 | 6.10 | 3.83 | 4.86 |
| cyberagent/calm2-7b-chat | 4.76 | 4.90 | 3.58 | 5.75 | 4.81 |
| mistralai/Mistral-7B-Instruct-v0.2 | 4.69 | 5.78 | 4.65 | 3.80 | 4.53 |
| shisa-ai/shisa-v1-yi1.5-9b | 4.63 | 5.98 | 4.28 | 3.26 | 5.00 |
| augmxnt/shisa-7b-v1 | 4.50 | 4.63 | 3.95 | 4.89 | 4.53 |
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