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Per the Llama 3 Community License Agreement, the official name of this model is "LLama 3 shisa-v1-llama3-8b"

8e6 moved in as it is a slightly superior model, will do some cleanup and renaming soon...

I ran the tests for 2 runs just to try to lower variance. These are all using temp 0.2, min_p 0.1, freq penalty 0.5

Model AVG Score ELYZA100 JA MT-Bench Rakuda Tengu-Bench JA Char %
shisa-v1-llama3-8b.lr-2e4 3.97 4.60 4.54 3.33 3.42 92.42%
shisa-v1-llama3-8b.lr-5e5 5.73 6.28 6.45 5.37 4.81 90.93%
shisa-v1-llama3-8b.2e5 6.33 6.51 6.66 6.68 5.48 91.51%
shisa-v1-llama3-8b (8-e6) 6.59 6.67 6.95 7.05 5.68 91.30%
shisa-v1-llama3-8b.5e6 6.42 6.33 6.76 7.15 5.45 91.56%
shisa-v1-llama3-8b.2e6 6.31 6.26 6.88 6.73 5.38 92.00%
  • The 2e-4 and 5e-5 are definitely overtrained and perform significantly worse.
  • 2e-5 is on the edge since weightwacher shows the embed as slightly overtrained for 2e-5, but NEFTune version is not
  • 8e-6 performs the best, and 5e-6 also performed slightly better than 2e-5

For a comparison of where it sits vs other models:

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
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

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

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/lr-8e6

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: shisa-v1-llama3-8b.lr-8e6

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 8e-6

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 0
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.00
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

outputs/lr-8e6

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4983

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: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
1.3951 0.0064 1 0.8645
0.8731 0.5020 79 0.5577
0.8405 1.0040 158 0.5138
0.6888 1.4853 237 0.4982
0.6674 1.9873 316 0.4870
0.5859 2.4694 395 0.4983

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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Dataset used to train shisa-ai/shisa-v1-llama3-8b

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