mrcuddle/Typescript-QWen2.5-Coder-3B-Instruct

See axolotl config

axolotl version: 0.6.0

# axolotl_config.yaml

# Model configuration
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
hub_model_id: mrcuddle/Qwen2.5-Coder-3B-Instruct-TS

# Training parameters
learning_rate: 0.0001  # Adjusted for potential stability improvement
train_batch_size: 4  # Increased for better gradient estimates
eval_batch_size: 4  # Increased for better evaluation stability
num_epochs: 1
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
gradient_accumulation_steps: 2
micro_batch_size: 1


# Distributed training settings
distributed_type: GPU
num_devices: 2  # Adjusted to utilize multiple GPUs if available
total_train_batch_size: 8  # Adjusted to match train_batch_size * num_devices * gradient_accumulation_steps
total_eval_batch_size: 8  # Adjusted to match eval_batch_size * num_devices * gradient_accumulation_steps

# Random seed for reproducibility
seed: 42

datasets:
  - path: mhhmm/typescript-instruct-20k
    type: alpaca
    field_instruction: instruction
    field_output: output
    format: "[INST] {instruction} [/INST]\n{output}"
    no_input_format: "[INST] {instruction} [/INST]"
    roles:
      input: ["USER"]
      output: ["ASSISTANT"]

Qwen2.5-Coder-3B-Instruct-TS

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-3B-Instruct on the mhhmm/typescript-instruct-20k 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: 0.0001
• train_batch_size: 1
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 2
• optimizer: Use adamw_hf 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: 1

Training results

Framework versions

• Transformers 4.47.1
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0