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sanchuanhehe/
end_try

peft

safetensors

mistral

generated_from_trainer

mistralai/Mistral-7B-v0.1

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See axolotl config

axolotl version: 0.4.1

yaml

base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: /workspace/data/subset_ophm/subset.json
    type: sharegpt 
    conversation: chatml
chat_template: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

eval_sample_packing: false

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project: try
wandb_entity: sanchuan
wandb_watch: "true"
wandb_name: end_try
wandb_log_model: "checkpoint"

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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: 1
xformers_attention:
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

Visualize in Weights & Biases

outputs/qlora-out

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5560

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.6721	0.0028	1	0.8014
0.5585	0.2496	89	0.5805
0.5953	0.4991	178	0.5657
0.5453	0.7487	267	0.5579
0.5728	0.9982	356	0.5560

Framework versions

PEFT 0.12.0
Transformers 4.44.0
Pytorch 2.4.0+cu124
Datasets 2.20.0
Tokenizers 0.19.1

sanchuanhehe/end_try

outputs/qlora-out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sanchuanhehe/
end_try