qwen-123go-taxi

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

axolotl version: 0.5.0

base_model: Qwen/Qwen2.5-7B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj

datasets:
  - path: training_data_expanded.jsonl
    type: chat_template
    chat_template: chatml
    field_messages: messages
    message_field_role: role
    message_field_content: content

output_dir: ./qwen-123go-finetuned
sequence_len: 512
sample_packing: false
pad_to_sequence_len: true
eval_sample_packing: false

micro_batch_size: 1
gradient_accumulation_steps: 4
num_epochs: 1

optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

bf16: true
fp16: false
tf32: false

logging_steps: 5
save_steps: 50

gradient_checkpointing: true
flash_attention: false

val_set_size: 0.1
evals_per_epoch: 1

seed: 42

</details><br>

qwen-123go-finetuned

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0000

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: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 4
• optimizer: Use OptimizerNames.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: 10
• num_epochs: 1

Training results

Framework versions

• PEFT 0.13.2
• Transformers 4.46.1
• Pytorch 2.3.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.3