shisa-v2 Base Model ablation
Per the Llama 3 Community License Agreement, the official name of this model is "Llama 3 shisa-v1-llama3-70b"
This is a fine-tune Llama 3 70B Instruct with the primary shisa-v1 dataset to improve Japanese language capabilities.
This model uses a LR of 8e-6 that slightly improves performance vs the initial 2e-5 tune (based on and validating predictive power of the the
results of the Llama 3 8B LR ablations).
It also uses NEFTune, although the expected impact is neglible for this dataset.
While the 2e-5 model matched gpt-3.5-turbo performance, this 2e-6 version consistently edges it out, so I think it's fair to say that this model "beats" it.
While this is merely a test ablation on the road to shisa-v2, as of its release (mid-May 2024), it's the strongest commercially-usable open JA model benchmarked so far, so this model may be of general interest.
Performance
| 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-v1-swallowmx-13a47b | 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 |
Compute for training this model was generously provided by
Ubitus.
See axolotl config
axolotl version: 0.4.0
1base_model: meta-llama/Meta-Llama-3-70B-Instruct
2model_type: LlamaForCausalLM
3tokenizer_type: AutoTokenizer
4
5load_in_8bit: false
6load_in_4bit: false
7strict: false
8
9# doesn't work...
10# hub_model_id: shisa-ai/shisa-llama3-70b-v1
11# hub_strategy: end
12
13use_wandb: true
14wandb_project: shisa-v2
15wandb_entity: augmxnt
16wandb_name: shisa-llama3-70b-v1.8e6
17
18chat_template: llama3
19datasets:
20 - path: augmxnt/ultra-orca-boros-en-ja-v1
21 type: sharegpt
22dataset_prepared_path: last_run_prepared
23val_set_size: 0.05
24output_dir: ./outputs/basemodel-llama3-70b.8e6
25
26sequence_len: 4096
27sample_packing: true
28pad_to_sequence_len: true
29
30neftune_noise_alpha: 5
31
32gradient_accumulation_steps: 2
33micro_batch_size: 2
34num_epochs: 3
35optimizer: paged_adamw_8bit
36lr_scheduler: linear
37learning_rate: 8e-6
38
39train_on_inputs: false
40group_by_length: false
41bf16: auto
42fp16:
43tf32: true
44
45gradient_checkpointing: true
46gradient_checkpointing_kwargs:
47 use_reentrant: false
48early_stopping_patience:
49resume_from_checkpoint:
50logging_steps: 1
51xformers_attention:
52flash_attention: true
53
54warmup_ratio: 0.1
55evals_per_epoch: 2
56eval_table_size:
57saves_per_epoch: 0
58debug:
59deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
60weight_decay: 0.05
61fsdp:
62fsdp_config:
63special_tokens:
64 pad_token: <|end_of_text|>
65
outputs/basemodel-llama3-70b.8e6
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:
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-6
- 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 |
|---|
| 1.248 | 0.0033 | 1 | 0.7102 |
| 0.7497 | 0.5008 | 154 | 0.4374 |
| 0.7229 | 1.0016 | 308 | 0.3940 |
| 0.3772 | 1.4862 | 462 | 0.3962 |
| 0.3791 | 1.9870 | 616 | 0.3838 |
| 0.0943 | 2.4699 | 770 | 0.4440 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
