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

See axolotl config

axolotl version: 0.4.0

yaml

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

shisa-ai/shisa-v1-phi3-14b

Performance

outputs/phi3-medium-128k-14b.8e6

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

shisa-ai/
shisa-v1-phi3-14b