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CAI-Supernova-r1

Text Generation
Transformers
PyTorch
Safetensors
llama
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
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metadata
library_name: transformers
license: llama3
base_model: arcee-ai/Llama-3.1-SuperNova-Lite
tags:
  - generated_from_trainer
model-index:
  - name: outputs
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

base_model: arcee-ai/Llama-3.1-SuperNova-Lite
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: NewEden/CharacterAI-logs-sharegpt-Ngram-Cleaned
    type: sharegpt
    conversation: llama3
  - path: NewEden/OpenCAI-ShareGPT
    type: sharegpt
    conversation: llama3


chat_template: llama3

  #val_set_size: 0.01
output_dir: ./outputs

adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:

sequence_len: 16384
# sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true


wandb_project: CAI-Supernova
wandb_entity:
wandb_watch:
wandb_name: CAI-Supernova-1
wandb_log_model:


plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-6
weight_decay: 0.05

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

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint:
#auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 5
  #evals_per_epoch: 4
eval_table_size:
  #eval_max_new_tokens: 128
saves_per_epoch: 1

debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_params.json
fsdp:
fsdp_config:

special_tokens:
  pad_token: <|finetune_right_pad_id|>
  eos_token: <|eot_id|>

outputs

This model is a fine-tuned version of arcee-ai/Llama-3.1-SuperNova-Lite on the None 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: 2e-06
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • total_eval_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 5
  • num_epochs: 4

Training results

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1