See axolotl config

axolotl version: 0.4.1

adapter: lora
auto_find_batch_size: true
base_model: defog/llama-3-sqlcoder-8b
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 3b9b4289b748f826_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/3b9b4289b748f826_train_data.json
  type:
    field_instruction: item_title
    field_output: comment
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
early_stopping_threshold: 0.001
eval_max_new_tokens: 128
eval_steps: 40
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: mrferr3t/6f414681-a86d-4a9f-ae4e-7a017f890fbd
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 100
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 32
mlflow_experiment_name: /tmp/3b9b4289b748f826_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 50
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
s2_attention: null
sample_packing: false
save_steps: 40
saves_per_epoch: 0
sequence_len: 512
special_tokens:
  pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fb91bb99-180c-4ff4-aa46-6d9918134443
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: fb91bb99-180c-4ff4-aa46-6d9918134443
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null

6f414681-a86d-4a9f-ae4e-7a017f890fbd

This model is a fine-tuned version of defog/llama-3-sqlcoder-8b on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.8630

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: 0.0003
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use adamw_bnb_8bit 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: 920
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0003	1	3.6124
No log	0.0136	40	3.5240
No log	0.0272	80	3.2220
3.49	0.0407	120	3.0959
3.49	0.0543	160	3.0310
3.1314	0.0679	200	2.9985
3.1314	0.0815	240	2.9683
3.1314	0.0951	280	2.9548
2.9953	0.1087	320	2.9402
2.9953	0.1222	360	2.9261
2.9277	0.1358	400	2.9201
2.9277	0.1494	440	2.9053
2.9277	0.1630	480	2.9040
2.9573	0.1766	520	2.8934
2.9573	0.1902	560	2.8847
2.9408	0.2037	600	2.8869
2.9408	0.2173	640	2.8727
2.9408	0.2309	680	2.8758
2.9108	0.2445	720	2.8719
2.9108	0.2581	760	2.8697
2.8622	0.2716	800	2.8661
2.8622	0.2852	840	2.8600
2.8622	0.2988	880	2.8756
2.8705	0.3124	920	2.8648
2.8705	0.3260	960	2.8630

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.3.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1