metadata
library_name: transformers
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
tags:
- axolotl
- generated_from_trainer
datasets:
- argilla/databricks-dolly-15k-curated-en
model-index:
- name: tiny-random-LlamaForCausalLM
results: []
See axolotl config
axolotl version: 0.6.0
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
batch_size: 32
bf16: true
chat_template: tokenizer_default_fallback_alpaca
datasets:
- format: custom
path: argilla/databricks-dolly-15k-curated-en
type:
field_input: original-instruction
field_instruction: original-instruction
field_output: original-response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
device_map: auto
eval_sample_packing: false
eval_steps: 200
flash_attention: true
gpu_memory_limit: 80GiB
group_by_length: true
hub_model_id: SystemAdmin123/tiny-random-LlamaForCausalLM
hub_strategy: checkpoint
learning_rate: 0.0002
logging_steps: 10
lr_scheduler: cosine
max_steps: 2500
micro_batch_size: 4
model_type: AutoModelForCausalLM
num_epochs: 100
optimizer: adamw_bnb_8bit
output_dir: /root/.sn56/axolotl/outputs/tiny-random-LlamaForCausalLM
pad_to_sequence_len: true
resize_token_embeddings_to_32x: false
sample_packing: false
save_steps: 400
save_total_limit: 1
sequence_len: 2048
tokenizer_type: LlamaTokenizerFast
torch_dtype: bf16
trust_remote_code: true
val_set_size: 0.1
wandb_entity: ''
wandb_mode: online
wandb_name: trl-internal-testing/tiny-random-LlamaForCausalLM-argilla/databricks-dolly-15k-curated-en
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: default
warmup_ratio: 0.05
tiny-random-LlamaForCausalLM
This model is a fine-tuned version of trl-internal-testing/tiny-random-LlamaForCausalLM on the argilla/databricks-dolly-15k-curated-en dataset. It achieves the following results on the evaluation set:
- Loss: 8.6989
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
- 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: 125
- training_steps: 2500
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0003 | 1 | 10.3763 |
| 9.7054 | 0.0592 | 200 | 9.6862 |
| 8.9091 | 0.1184 | 400 | 8.9612 |
| 8.7257 | 0.1776 | 600 | 8.7627 |
| 8.7416 | 0.2368 | 800 | 8.7109 |
| 8.5944 | 0.2959 | 1000 | 8.6982 |
| 8.673 | 0.3551 | 1200 | 8.6963 |
| 8.7511 | 0.4143 | 1400 | 8.6972 |
| 8.729 | 0.4735 | 1600 | 8.6961 |
| 8.6325 | 0.5327 | 1800 | 8.6948 |
| 8.6338 | 0.5919 | 2000 | 8.6946 |
| 8.7376 | 0.6511 | 2200 | 8.6954 |
| 8.573 | 0.7103 | 2400 | 8.6989 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.4.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0