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--- |
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library_name: transformers |
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license: gemma |
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base_model: unsloth/gemma-3-270m-it |
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tags: |
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- axolotl |
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- generated_from_trainer |
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datasets: |
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- allura-org/EU01-S2 |
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- allenai/tulu-3-sft-personas-instruction-following |
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- ToastyPigeon/mixed-medical-reasoning-formatted |
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- ToastyPigeon/steve-and-marvin |
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- ToastyPigeon/kimi-stories-instruct |
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- ToastyPigeon/new-story-dataset |
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- allura-org/fujin-instruct-v2 |
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- ToastyPigeon/gutenberg-sft |
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- ToastyPigeon/SpringDragon |
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- ToastyPigeon/some-erotica |
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model-index: |
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- name: micro-glitter |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.11.0.dev0` |
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```yaml |
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# === Model Configuration === |
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base_model: unsloth/gemma-3-270m-it |
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load_in_8bit: false |
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load_in_4bit: false |
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# === HF Configuration === |
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hub_model_id: allura-forge/micro-glitter |
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hub_strategy: "checkpoint" |
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output_dir: /workspace/aibox-standalone-pool/axolotl/lilglitter-ckpts |
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# === Training Setup === |
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num_epochs: 2 |
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micro_batch_size: 4 |
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gradient_accumulation_steps: 8 |
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sequence_len: 8192 |
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sample_packing: true |
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pad_to_sequence_len: true |
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#max_steps: 10 |
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# === Evaluation === |
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val_set_size: 0.05 |
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evals_per_epoch: 10 |
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#eval_steps: 20 |
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#max_steps: 60 |
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#eval_table_size: |
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eval_max_new_tokens: 128 |
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eval_sample_packing: true |
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#eval_strategy: "no" |
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# === LoRA Configuration === |
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#adapter: qlora |
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#lora_model_dir: |
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#lora_r: 128 |
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#lora_alpha: 16 |
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#lora_dropout: 0.25 |
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#lora_target_linear: true |
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#lora_target_modules: |
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# - embed_tokens |
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# - lm_head |
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lora_fan_in_fan_out: |
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lora_target_modules: |
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#peft_use_rslora: true |
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lora_modules_to_save: |
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# - embed_tokens |
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# - lm_head |
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#fix_untrained_tokens: true |
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#lora_mlp_kernel: true |
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#lora_qkv_kernel: true |
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#lora_o_kernel: true |
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# === Hyperparameter Configuration === |
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#optimizer: apollo_adamw_layerwise |
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warmup_steps: 0 |
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optimizer: adamw_torch_fused |
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#optimizer: paged_adamw_8bit |
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#optim_args: |
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# enable_stochastic_rounding: true |
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# enable_cautious: true |
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# enable_8bit: true |
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# Apollo-mini configuration: |
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#optim_args: "proj=random,rank=128,scale=128.0,scale_type=tensor,update_proj_gap=100" |
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# Regular Apollo configuration: |
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# optim_args: |
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#optim_target_modules: all_linear |
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learning_rate: 1e-5 |
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lr_scheduler: cosine |
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#cosine_min_lr_ratio: 0.2 |
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#lr_scheduler: cosine_with_min_lr |
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#lr_scheduler_kwargs: |
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# cosine_min_lr: 1e-6 |
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weight_decay: 0.01 |
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max_grad_norm: 2.0 |
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#warmup_steps: 0 |
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#warmup_ratio: 0.025 |
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# === Data Configuration === |
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# |
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#chat_template: jinja |
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#chat_template_jinja: "{% for message in messages %}{% if not loop.first %}{{' \n\n' }}{% endif %}{% if message['role'] == 'system' %}{{ '### System:\n' + message['content'].strip() }}{% elif message['role'] == 'user' %}{{ '### Instruction:\n' + message['content'].strip() }}{% elif message['role'] == 'assistant' %}{{ '### Response:\n' + message['content'].strip() + eos_token }}{% endif %}{% endfor %}" |
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#chat_template_jinja: "{%- set default_system_message = \"You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris. You obediently fulfill the user's requests.\" %}\n\n{{- bos_token }}\n\n{%- if messages[0]['role'] == 'system' %}\n {%- if messages[0]['content'] is string %}\n {%- set system_message = messages[0]['content'] %}\n {%- else %}\n {%- set system_message = messages[0]['content'][0]['text'] %}\n {%- endif %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = default_system_message %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{{- '[SYSTEM_PROMPT]' + system_message + '[/SYSTEM_PROMPT]' }}\n\n{%- for message in loop_messages %}\n {%- if message['role'] == 'user' %}\n {%- if message['content'] is string %}\n {{- '[INST]' + message['content'] + '[/INST]' }}\n {%- else %}\n {{- '[INST]' }}\n {%- for bl (line truncated to 1000 characters) |
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#chat_template: chatml |
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#special_tokens: |
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# eos_token: "<|im_end|>" |
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# eos_token: "</s>" |
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#tokenizer_use_mistral_common: true |
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shuffle_merged_datasets: true |
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datasets: |
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- path: allura-org/EU01-S2 |
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type: chat_template |
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field_messages: conversations |
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message_property_mappings: |
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role: from |
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content: value |
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- path: allenai/tulu-3-sft-personas-instruction-following |
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type: chat_template |
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split: train[:10%] |
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- path: ToastyPigeon/mixed-medical-reasoning-formatted |
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type: chat_template |
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data_files: mixed-medical-thinking.json |
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split: train[:10%] |
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- path: ToastyPigeon/steve-and-marvin |
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type: completion |
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data_files: marvin.json |
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- path: ToastyPigeon/kimi-stories-instruct |
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type: chat_template |
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- path: ToastyPigeon/new-story-dataset |
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# type: customcompletion-regex |
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type: completion |
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data_files: new-story-dataset-v2.json |
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- path: allura-org/fujin-instruct-v2 |
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# type: customchatml-regex |
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type: chat_template |
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field_messages: conversations |
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message_property_mappings: |
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role: from |
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content: value |
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# - path: ToastyPigeon/some-rp-extended |
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# type: customchatml-regex |
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# type: chat_template |
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# field_messages: conversations |
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# message_property_mappings: |
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# role: from |
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# content: value |
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# roles_to_train: ["user","assistant"] |
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- path: ToastyPigeon/gutenberg-sft |
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# type: customchatml-regex |
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type: chat_template |
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field_messages: conversations |
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message_property_mappings: |
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role: from |
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content: value |
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- path: ToastyPigeon/SpringDragon |
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# type: customcompletion-regex |
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type: completion |
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split: train |
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- path: ToastyPigeon/some-erotica |
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# type: customcompletion-regex |
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type: completion |
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split: train[:10%] |
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dataset_prepared_path: last_run_prepared |
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# === Plugins === |
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plugins: |
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- axolotl.integrations.liger.LigerPlugin |
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin |
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# === Hardware Optimization === |
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#gradient_checkpointing: offload |
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#gradient_checkpointing_kwargs: |
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# use_reentrant: false |
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liger_rope: true |
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liger_rms_norm: true |
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liger_layer_norm: true |
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liger_glu_activation: true |
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#liger_fused_linear_cross_entropy: true |
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cut_cross_entropy: true |
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#deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json |
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# === FSDP Config === |
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#fsdp: |
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# - full_shard |
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# - auto_wrap |
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#fsdp_config: |
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# fsdp_limit_all_gathers: true |
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# fsdp_sync_module_states: true |
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# fsdp_offload_params: true |
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# fsdp_activation_checkpointing: true |
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# fsdp_use_orig_params: false |
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# fsdp_cpu_ram_efficient_loading: true |
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# fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP |
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# fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer |
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# fsdp_state_dict_type: FULL_STATE_DICT |
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# fsdp_sharding_strategy: FULL_SHARD |
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# fsdp_version: 2 |
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# === Wandb Tracking === |
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wandb_project: TinyGemma |
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# wandb_entity: [WANDB_ENTITY] |
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# wandb_name: [WANDB_RUN_NAME] |
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# === Checkpointing === |
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#save_steps: 10 |
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saves_per_epoch: 10 |
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save_total_limit: 1 |
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# === Advanced Settings === |
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bf16: auto |
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flash_attention: true |
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train_on_inputs: false |
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group_by_length: false |
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save_safetensors: true |
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logging_steps: 1 |
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gc_steps: 10 |
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seed: 69 |
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``` |
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</details><br> |
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# micro-glitter |
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This model is a fine-tuned version of [unsloth/gemma-3-270m-it](https://huggingface.co/unsloth/gemma-3-270m-it) on the allura-org/EU01-S2, the allenai/tulu-3-sft-personas-instruction-following, the ToastyPigeon/mixed-medical-reasoning-formatted, the ToastyPigeon/steve-and-marvin, the ToastyPigeon/kimi-stories-instruct, the ToastyPigeon/new-story-dataset, the allura-org/fujin-instruct-v2, the ToastyPigeon/gutenberg-sft, the ToastyPigeon/SpringDragon and the ToastyPigeon/some-erotica datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.7387 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 69 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 8 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 8 |
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- training_steps: 296 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| No log | 0 | 0 | 3.8582 | |
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| 3.4802 | 0.1008 | 15 | 3.5118 | |
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| 3.4608 | 0.2017 | 30 | 3.4890 | |
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| 3.5272 | 0.3025 | 45 | 3.5189 | |
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| 3.559 | 0.4034 | 60 | 3.5753 | |
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| 3.5817 | 0.5042 | 75 | 3.6121 | |
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| 3.6349 | 0.6050 | 90 | 3.6471 | |
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| 3.68 | 0.7059 | 105 | 3.6721 | |
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| 3.6597 | 0.8067 | 120 | 3.6970 | |
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| 3.6462 | 0.9076 | 135 | 3.7068 | |
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| 3.7009 | 1.0067 | 150 | 3.7213 | |
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| 3.6717 | 1.1076 | 165 | 3.7313 | |
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| 3.7631 | 1.2084 | 180 | 3.7338 | |
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| 3.7535 | 1.3092 | 195 | 3.7346 | |
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| 3.668 | 1.4101 | 210 | 3.7375 | |
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| 3.679 | 1.5109 | 225 | 3.7383 | |
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| 3.6539 | 1.6118 | 240 | 3.7386 | |
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| 3.6547 | 1.7126 | 255 | 3.7386 | |
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| 3.7533 | 1.8134 | 270 | 3.7400 | |
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| 3.6983 | 1.9143 | 285 | 3.7387 | |
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### Framework versions |
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- Transformers 4.52.4 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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