See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: codellama/CodeLlama-7b-Instruct-hf
bf16: true
chat_template: llama3
datasets:
- data_files:
- e9a0541f2e31ff4a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e9a0541f2e31ff4a_train_data.json
type:
field_instruction: question
field_output: answer
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso01/d1870079-da8a-4cfa-90b0-f6fef09a2f4a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
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
lr_scheduler: cosine
max_memory:
0: 80GiB
max_steps: 100
micro_batch_size: 8
mlflow_experiment_name: /tmp/e9a0541f2e31ff4a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 1024
special_tokens:
pad_token: </s>
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: d1870079-da8a-4cfa-90b0-f6fef09a2f4a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: d1870079-da8a-4cfa-90b0-f6fef09a2f4a
warmup_steps: 10
weight_decay: 0.01
xformers_attention: false
d1870079-da8a-4cfa-90b0-f6fef09a2f4a
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_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: 10
- training_steps: 100
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
0.0 | 0.0066 | 1 | nan |
0.0 | 0.0592 | 9 | nan |
0.0 | 0.1184 | 18 | nan |
0.0 | 0.1776 | 27 | nan |
0.0 | 0.2368 | 36 | nan |
0.0 | 0.2961 | 45 | nan |
0.0 | 0.3553 | 54 | nan |
0.0 | 0.4145 | 63 | nan |
0.0 | 0.4737 | 72 | nan |
0.0 | 0.5329 | 81 | nan |
0.0 | 0.5921 | 90 | nan |
0.0 | 0.6513 | 99 | nan |
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
- 32
Model tree for lesso01/d1870079-da8a-4cfa-90b0-f6fef09a2f4a
Base model
codellama/CodeLlama-7b-Instruct-hf