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Training in progress, step 40

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axolotl_config.yaml ADDED
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+ base_model: JackFram/llama-160m
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+ batch_size: 32
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+ bf16: true
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+ chat_template: tokenizer_default_fallback_alpaca
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+ datasets:
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+ - format: custom
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+ path: argilla/databricks-dolly-15k-curated-en
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+ type:
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+ field_input: original-instruction
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+ field_instruction: original-instruction
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+ field_output: original-response
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+ format: '{instruction} {input}'
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+ no_input_format: '{instruction}'
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+ system_format: '{system}'
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+ system_prompt: ''
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+ eval_steps: 20
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+ flash_attention: true
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+ gpu_memory_limit: 80GiB
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+ group_by_length: true
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+ hub_model_id: SystemAdmin123/llama-160m
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+ hub_strategy: checkpoint
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+ learning_rate: 0.0002
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+ logging_steps: 10
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+ lr_scheduler: cosine
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+ max_steps: 2500
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+ micro_batch_size: 2
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+ model_type: AutoModelForCausalLM
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+ num_epochs: 100
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+ optimizer: adamw_bnb_8bit
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+ output_dir: /root/.sn56/axolotl/outputs/llama-160m
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+ pad_to_sequence_len: true
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+ resize_token_embeddings_to_32x: false
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+ save_steps: 40
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+ save_total_limit: 1
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+ sequence_len: 2048
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+ special_tokens:
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+ pad_token: </s>
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+ tokenizer_type: LlamaTokenizerFast
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+ trust_remote_code: true
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+ val_set_size: 0.1
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+ wandb_entity: ''
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+ wandb_mode: online
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+ wandb_name: JackFram/llama-160m-argilla/databricks-dolly-15k-curated-en
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+ wandb_project: Gradients-On-Demand
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+ wandb_run: your_name
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+ wandb_runid: default
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+ warmup_ratio: 0.05
config.json ADDED
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+ "_name_or_path": "JackFram/llama-160m",
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+ "vocab_size": 32000
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+ }
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+ "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{ '### Instruction: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ '### Response: ' + message['content'] + eos_token}}{% endif %}{% endfor %}",
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+ "use_default_system_prompt": false,
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