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CodeGPTPlus
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deepseek-coder-1.3b-typescript

Text Generation
Transformers
PyTorch
Safetensors
llama
axolotl
Generated from Trainer
text-generation-inference
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deepseek-coder-1.3b-typescript / README.md
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metadata
license: other
base_model: deepseek-ai/deepseek-coder-1.3b-base
tags:
  - generated_from_trainer
model-index:
  - name: deepseek_coder_1.3b_typescript
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.3.0

base_model: deepseek-ai/deepseek-coder-1.3b-base
model_type: AutoModelForCausalLM
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false


datasets:
  - path: CodeGPTPlus/typescript-0-500000-seq1024
    type: completion
    field: text
#dataset_prepared_path:

#pretraining_dataset: CodeGPTPlus/typescript-0-500000-seq1024

val_set_size: 0.001
output_dir:  ./fft-out

sequence_len: 1024

adapter:
lora_model_dir:
lora_r: 
lora_alpha: 
lora_dropout: 
lora_target_linear: 
lora_fan_in_fan_out:
lora_modules_to_save:

wandb_project: deepseek_1.3_fft
wandb_entity:
wandb_watch:
wandb_name: aws_a10g
wandb_log_model: end


gradient_accumulation_steps: 2
micro_batch_size: 20
num_epochs: 1
#max_steps: 1 # REMOVE IT
optimizer: adamw_bnb_8bit
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 0.000001
max_grad_norm: 1.0
weight_decay: 0.1
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

hub_model_id: CodeGPTPlus/deepseek_coder_1.3b_typescript
hub_strategy: every_save
warmup_ratio: 0.01
evals_per_epoch: 20
saves_per_epoch: 3
debug:
deepspeed:

fsdp:
fsdp_config:
special_tokens:
  bos_token: "<｜begin▁of▁sentence｜>"
  eos_token: "<｜end▁of▁sentence｜>"
  pad_token: "<｜end▁of▁sentence｜>"
  # fim_prefix: "<｜fim▁begin｜>"
  # fim_middle: "<｜fim▁hole｜>"
  # fim_suffix: "<｜fim▁end｜>"

deepseek_coder_1.3b_typescript

This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on an unknown 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-05
  • train_batch_size: 20
  • eval_batch_size: 20
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 40
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 261
  • num_epochs: 1

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0