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---
library_name: peft
license: llama3.1
base_model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
tags:
- generated_from_trainer
datasets:
- ugaoo/subset_each5k_multimedqa
model-index:
- name: out/subset_each5k_multimedqa
results: []
---
<!-- 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"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.8.0.dev0`
```yaml
base_model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: ugaoo/subset_each5k_multimedqa
type: alpaca
val_set_size: 0
output_dir: ./out/subset_each5k_multimedqa
sequence_len: 4000
sample_packing: true
pad_to_sequence_len: true
adapter: qlora
lora_r: 256
lora_alpha: 512
lora_dropout: 0.05
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- up_proj
- down_proj
- gate_proj
lora_modules_to_save:
- embed_tokens
- lm_head
wandb_project: cosmosearch
wandb_entity:
wandb_watch:
wandb_name: subset_each5k_multimedqa_Nemotron-70B
wandb_log_model:
gradient_accumulation_steps: 3
micro_batch_size: 4
num_epochs: 6
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 5e-6
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 6
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
save_total_limit: 6
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# out/subset_each5k_multimedqa
This model is a fine-tuned version of [nvidia/Llama-3.1-Nemotron-70B-Instruct-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) on the ugaoo/subset_each5k_multimedqa dataset.
## 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 3
- total_train_batch_size: 36
- total_eval_batch_size: 12
- 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: 100
- num_epochs: 6.0
### Training results
### Framework versions
- PEFT 0.15.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1 |