---
library_name: peft
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
tags:
- generated_from_trainer
datasets:
- OpenSciLM/OS_Train_Data
model-index:
- name: outputs/deepseek-R1-distill-llama-8B-openscholar-data-lora-8000-1epoch
  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: `0.6.0`
```yaml
adapter: lora
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: last_run_prepared
datasets:
- field_message: messages
  message_field_content: content
  message_field_role: role
  path: OpenSciLM/OS_Train_Data
  drop_system_message: true
  split: train
  type: chat_template
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: true
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
learning_rate: 2e-5
load_in_4bit: false
load_in_8bit: true
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_modules_to_save:
- embed_tokens
- lm_head
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 8000
micro_batch_size: 1
model_type: LlamaForCausalLM
num_epochs: 1
optimizer: paged_adamw_8bit
output_dir: ./outputs/deepseek-R1-distill-llama-8B-openscholar-data-lora-8000-1epoch
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sdp_attention: true
sequence_len: 4096 #2048
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 1
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# outputs/deepseek-R1-distill-llama-8B-openscholar-data-lora-8000-1epoch

This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) on the OpenSciLM/OS_Train_Data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3808

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 2
- training_steps: 8000

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7391        | 0.3814 | 8000 | 0.3808          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0