See axolotl config
axolotl version: 0.5.2
adapter: lora
base_model: unsloth/mistral-7b
bf16: auto
datasets:
- data_files:
- 8219297a1f15c78f_train_data.json
ds_type: json
format: custom
path: 8219297a1f15c78f_train_data.json
preprocessing:
- shuffle: true
type:
field: null
field_input: null
field_instruction: prompt
field_output: response
field_system: null
format: null
no_input_format: null
system_format: '{system}'
system_prompt: ''
debug: null
device_map: auto
early_stopping_patience: null
eval_max_new_tokens: 16
eval_table_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 8
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: taopanda/test-mistral-7b
is_mistral_derived_model: true
learning_rate: 0.0005
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_r: 64
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 450
micro_batch_size: 4
model_type: MistralForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: ./out/taopanda_test-mistral-7b
pad_to_sequence_len: true
resume_from_checkpoint: null
save_steps: 0.15
save_total_limit: 1
seed: 42
sequence_len: 1024
special_tokens:
bos_token: <s>
eos_token: </s>
unk_token: <unk>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_flash_attn_2: true
val_set_size: 0.1
wandb_entity: fatcat87-taopanda
wandb_log_model: null
wandb_mode: online
wandb_name: taopanda_test-mistral-7b
wandb_project: subnet56-test
wandb_runid: taopanda_test-mistral-7b
wandb_watch: null
warmup_ratio: 0.06
weight_decay: 0.01
xformers_attention: null
test-mistral-7b
This model is a fine-tuned version of unsloth/mistral-7b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6985
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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 87
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.0651 | 0.0116 | 1 | 1.0560 |
0.7617 | 0.1272 | 11 | 0.7764 |
0.7784 | 0.2543 | 22 | 0.7421 |
0.6848 | 0.3815 | 33 | 0.7290 |
0.7263 | 0.5087 | 44 | 0.7161 |
0.7062 | 0.6358 | 55 | 0.7074 |
0.7281 | 0.7630 | 66 | 0.7020 |
0.7402 | 0.8902 | 77 | 0.6985 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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unsloth/mistral-7b