metadata
license: llama3
datasets:
- augmxnt/ultra-orca-boros-en-ja-v1
language:
- ja
- en
base_model: meta-llama/Meta-Llama-3-70B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: shisa-llama3-70b-v1
results: []
shisa-v2 Base Model ablation
The 8e-6 version is better and you should probably use that one.
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 |
| CohereForAI/c4ai-command-r-plus | 7.69 | 7.50 | 7.43 | 9.05 | 6.79 |
| gpt-3.5-turbo-0125 | 7.17 | 7.24 | 6.98 | 7.64 | 6.82 |
| shisa-ai/shisa-v1-llama3-70b | 7.17 | 7.16 | 7.45 | 7.98 | 6.09 |
| 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^ | 6.29 | 6.62 | 6.41 | 7.05 | 5.07 |
| shisa-ai/shisa-swallowmx-13a47b-v1 | 6.17 | 6.48 | 6.07 | 7.11 | 5.03 |
| shisa-ai/shisa-v1-llama3-8b | 6.10 | 6.52 | 6.20 | 6.37 | 5.33 |
| Rakuten/RakutenAI-7B-chat | 5.58 | 5.92 | 4.60 | 6.58 | 5.24 |
| shisa-ai/shisa-v1-gemma-8b | 5.64 | 6.50 | 5.42 | 5.10 | 5.55 |
| augmxnt/shisa-gamma-7b-v1 | 5.56 | 5.84 | 4.00 | 6.73 | 5.68 |
| lightblue/qarasu-14B-chat-plus-unleashed | 5.20 | 5.58 | 4.74 | 5.46 | 5.01 |
| 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 |
See axolotl config
axolotl version: 0.4.0
base_model: meta-llama/Meta-Llama-3-70B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
hub_model_id: shisa-ai/shisa-llama3-70b-v1
hub_strategy: end
use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: shisa-llama3-70b-v1
chat_template: llama3
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/basemodel-llama3-70b
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 0
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
shisa-llama3-70b-v1
This model is a fine-tuned version of meta-llama/Meta-Llama-3-70B-Instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4425
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 87
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2478 | 0.0033 | 1 | 0.7102 |
| 0.7516 | 0.5008 | 154 | 0.4325 |
| 0.7185 | 1.0016 | 308 | 0.3966 |
| 0.3708 | 1.4862 | 462 | 0.3976 |
| 0.3758 | 1.9870 | 616 | 0.3840 |
| 0.0928 | 2.4699 | 770 | 0.4425 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1