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---
license: apache-2.0
base_model: augmxnt/shisa-base-7b-v1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: shisa-7b-v1-sharegpt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: augmxnt/shisa-base-7b-v1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
hub_model_id: yentinglin/shisa-7b-v1-sharegpt
hub_strategy: end
datasets:
- path: NTQAI/sharegpt-clean-ja
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./output/ja/sft/shisa-7b-v1/sharegpt/
sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project: JA-LLM
wandb_entity:
wandb_watch:
wandb_name: sft-fft-sharegpt-clean-ja
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1 # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
learning_rate: 1e-5
adam_beta1: 0.9
adam_beta2: 0.95
adam_eps: 0.00001
max_grad_norm: 1.0
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 5
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_ratio: 0.02 # cannot use with warmup_steps
evals_per_epoch: 1
eval_table_size:
save_per_epoch: 1
save_total_limit: 1
debug:
deepspeed: deepspeed_configs/zero1.json # multi-gpu only
weight_decay: 0.001
fsdp:
fsdp_config:
special_tokens:
ddp_timeout: 180000
special_tokens:
eos_token: "<|im_end|>"
tokens:
- "<|im_start|>"
```
</details><br>
# shisa-7b-v1-sharegpt
This model is a fine-tuned version of [augmxnt/shisa-base-7b-v1](https://huggingface.co/augmxnt/shisa-base-7b-v1) on the None dataset.
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0
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