Edit model card

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

Built with Axolotl

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
Downloads last month
1
Safetensors
Model size
70.6B params
Tensor type
BF16
·

Finetuned from

Dataset used to train shisa-ai/shisa-v1-llama3-70b.2e5