See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: unsloth/codellama-7b
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 2ad46d0313986df7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/2ad46d0313986df7_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
device_map:
? ''
: 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/90d5b725-9dfb-405c-86fa-326350c1c1ee
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1092
micro_batch_size: 4
mlflow_experiment_name: /tmp/2ad46d0313986df7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.03354781570171966
wandb_entity: null
wandb_mode: online
wandb_name: d47f6846-6c00-4ad8-aca7-52fa04750bcb
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d47f6846-6c00-4ad8-aca7-52fa04750bcb
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
90d5b725-9dfb-405c-86fa-326350c1c1ee
This model is a fine-tuned version of unsloth/codellama-7b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6689
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
- training_steps: 1092
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.6172 | 0.0002 | 1 | 2.5837 |
1.5458 | 0.0222 | 100 | 1.7610 |
1.6923 | 0.0444 | 200 | 1.7392 |
1.6922 | 0.0666 | 300 | 1.7222 |
1.8 | 0.0889 | 400 | 1.7104 |
1.7277 | 0.1111 | 500 | 1.6967 |
1.7427 | 0.1333 | 600 | 1.6879 |
1.8182 | 0.1555 | 700 | 1.6800 |
1.6517 | 0.1777 | 800 | 1.6742 |
1.7055 | 0.1999 | 900 | 1.6706 |
1.646 | 0.2222 | 1000 | 1.6689 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
- Downloads last month
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Inference Providers
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The model has no pipeline_tag.
Model tree for Alphatao/90d5b725-9dfb-405c-86fa-326350c1c1ee
Base model
unsloth/codellama-7b