fats-fme/3ab1c19e-540b-4a75-8aeb-a2f6afdb7544
011
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.4.1
adapter: lora
base_model: codellama/CodeLlama-7b-Instruct-hf
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4d9f07a482367b19_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 5
eval_max_new_tokens: 128
eval_sample_packing: false
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: fats-fme/3ab1c19e-540b-4a75-8aeb-a2f6afdb7544
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 256
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lora_target_modules:
- gate_proj
- up_proj
- o_proj
- down_proj
- k_proj
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 130GB
max_steps: 200
micro_batch_size: 1
mlflow_experiment_name: /tmp/4d9f07a482367b19_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 2048
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
use_scaled_dot_product_attention: false
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 59c88e83-96bc-4ad9-b0c3-3ab5195eb8cf
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 59c88e83-96bc-4ad9-b0c3-3ab5195eb8cf
warmup_steps: 100
weight_decay: 0.01
xformers_attention: null
</details><br>
3ab1c19e-540b-4a75-8aeb-a2f6afdb7544
This model is a fine-tuned version of codellama/CodeLlama-7b-Instruct-hf on the None dataset. It achieves the following results on the evaluation set:
- Loss: nan
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: 1e-05
- trainbatchsize: 1
- evalbatchsize: 1
- seed: 42
- gradientaccumulationsteps: 8
- totaltrainbatch_size: 8
- optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 100
- training_steps: 200
Training results
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1