base_model: mistralai/Mistral-7B-v0.1
model_type: AutoModelForCausalLM
tokenizer_config: Open-Orca/Mistral-7B-OpenOrca
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
resize_token_embeddings_to_32x: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

flash_attention: true
xformers_attention:

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
datasets:
  - path: skymizer/Sonnet3.5-SlimOrcaDedupCleaned-train
    type: chat_template
    field_messages: messages

test_datasets:
  - path: skymizer/Sonnet3.5-SlimOrcaDedupCleaned-test
    type: chat_template
    field_messages: messages
    split: train

hf_use_auth_token: true
dataset_prepared_path: pretokenized/slim-orca
output_dir: ./exp_output_artifacts

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

eval_sample_packing: false
# eval_causal_lm_metrics: ["perplexity"]

wandb_project: "axolotl_mistral_sft"
wandb_entity:
wandb_watch:
wandb_name: "mistral-7B-v0.1-sft-slim-orca-sonnet-3.5-v4"
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 16
eval_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.000003
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.95
adam_eps: 0.000001
max_grad_norm: 1.0

train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false

hub_model_id: "skymizer/mistral-7B-v0.1-sft-slim-orca-sonnet-3.5-v4"
save_strategy: "epoch"
saves_per_epoch: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1

warmup_ratio: 0.03
eval_steps: 50
eval_table_size:
eval_max_new_tokens: 2048
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
seed: 42

mistral-7B-v0.1-sft-slim-orca-sonnet-3.5-v4

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: 0.5315

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: 3e-06
train_batch_size: 16
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 11
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.2346	0.0026	1	1.3762
0.6186	0.1277	50	0.6218
0.5905	0.2554	100	0.5852
0.5684	0.3831	150	0.5648
0.5621	0.5109	200	0.5513
0.551	0.6386	250	0.5415
0.5489	0.7663	300	0.5345
0.5434	0.8940	350	0.5315

Framework versions

Transformers 4.46.3
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3