MatthieuO/Mistroll-v0.1

See axolotl config

axolotl version: 0.4.0

base_model: mistral-community/Mistral-7B-v0.2 
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: dataset/medical_meadow_large.json
    type: alpaca
    ds_type: json
chat_template: chatml
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out

sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

use_wandb: true
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name: Mistroll-v0.1 
wandb_log_model:

adapter: qlora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true

low_cpu_mem_usage: true
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.0002

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:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 4
save_total_limit: 2
debug:
deepspeed:
weight_decay: 0.0
special_tokens:

out

This model is a fine-tuned version of mistral-community/Mistral-7B-v0.2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1296

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: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• total_eval_batch_size: 2
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.7713	0.0	1	1.6075
1.4023	0.25	1246	1.2095
1.2564	0.5	2492	1.1769
1.6691	0.75	3738	1.1296

Framework versions

• PEFT 0.10.0
• Transformers 4.40.0.dev0
• Pytorch 2.2.2+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0