metadata
base_model: alpindale/Mistral-7B-v0.2-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: mpa-Mistral-7b-v0.2-hf-sft
results: []
See axolotl config
axolotl version: 0.4.0
base_model: alpindale/Mistral-7B-v0.2-hf
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: /data/seongyun/open-instruct-2/augmented_diverse_response/preferences_v1_responses_for_training.jsonl
type:
system_prompt: ""
system_format: "[INST] {system}\n"
field_system: system
field_instruction: instruction
field_output: output
format: "{instruction} [/INST]"
no_input_format: "{instruction} [/INST]"
# conversation: mistral
dataset_prepared_path:
hub_model_id: kaist-ai/mpa-Mistral-7b-v0.2-hf-sft
hub_strategy: checkpoint
# val_set_size: 0
output_dir: /data/suehyun/axolotl/outputs/mpa/mistral-7b-v0.2-hf
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: mpa
wandb_entity: suehyun
wandb_watch:
wandb_name: mpa_mistral-7b-v0.2-hf
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
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
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:
mpa-Mistral-7b-v0.2-hf-sft
This model is a fine-tuned version of alpindale/Mistral-7B-v0.2-hf 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.0