Dora-Smol

<!-- 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>

<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

base_model: /media/renfroe/llms/SmolLM-360M/

model_type: LlamaForCausalLM
tokenizer_type: GPT2Tokenizer
seed: 122887 
load_in_8bit: false
load_in_4bit: false
strict: false

max_steps: 0
resume_from_checkpoint: 
datasets:
  - path: /home/renfroe/Desktop/sqa_tiny-llama_dataset/Dynamic_Optimization_Methods_with_Applications_sqa_answers_only.json
    type:
      field_instruction: question
      field_output: answer
      format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
      no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
  - path: /home/renfroe/Dev/tinyllama-models/dataset/open_hermes_top_tech.json
    type: sharegpt
  - path:  /home/renfroe/Desktop/sqa_tiny-llama_dataset/hermes_prior_knowledge_question_expansion_with_answers.json
    type:
      field_instruction: question
      field_output: answer
      format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
      no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
  - path:  /home/renfroe/Desktop/sqa_tiny-llama_dataset/hermes_prior_knowledge_question_expansion_with_answers.json
    type:
      field_instruction: question
      field_output: answer
      format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
      no_input_format: "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
  - path:  /home/renfroe/Desktop/sqa_tiny-llama_dataset/or-farm_sharegpt.json
    type: sharegpt
  


dataset_prepared_path:
val_set_size: 0.2
output_dir: ./SmolLM-Ora
auto_resume_from_checkpoints: false

sequence_len: 2048
sample_packing: true
chat_template: chatml

wandb_project: SmolLM-Ora
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 10
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: linear
weight_decay: 0.0000001
learning_rate: 0.0001
lr_scheduler_kwargs:
  #  num_cycles: 3 

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

eval_sample_packing: False

warmup_steps: 50
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 4
debug:
deepspeed:

fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|endoftext|>"
  eos_token: "<|endoftext|>"
  pad_token: "<|endoftext|>"

</details><br>

SmolLM-Ora

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8298

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: 10
• eval_batch_size: 10
• seed: 122887
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• num_epochs: 4

Training results

Framework versions

• Transformers 4.40.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.18.0
• Tokenizers 0.15.0