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Training in progress, epoch 5

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README.md ADDED
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+ ---
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+ base_model: google/gemma-3-270m-it
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+ library_name: transformers
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+ model_name: gemma3-qat-lora
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+ tags:
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+ - generated_from_trainer
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+ - trl
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+ - sft
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+ licence: license
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+ ---
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+
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+ # Model Card for gemma3-qat-lora
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+
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+ This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
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+ It has been trained using [TRL](https://github.com/huggingface/trl).
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+
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+ ## Quick start
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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+ generator = pipeline("text-generation", model="Nihal2000/gemma3-qat-lora", device="cuda")
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+ output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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+ print(output["generated_text"])
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+ ```
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+
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+ ## Training procedure
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+
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+
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+
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+
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+ This model was trained with SFT.
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+
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+ ### Framework versions
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+
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+ - TRL: 0.22.1
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+ - Transformers: 4.56.0
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+ - Pytorch: 2.6.0+cu124
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+ - Datasets: 3.6.0
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+ - Tokenizers: 0.22.0
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+
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+ ## Citations
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+
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+
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+
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+ Cite TRL as:
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+
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+ ```bibtex
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+ @misc{vonwerra2022trl,
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+ title = {{TRL: Transformer Reinforcement Learning}},
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+ author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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+ year = 2020,
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+ journal = {GitHub repository},
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+ publisher = {GitHub},
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+ howpublished = {\url{https://github.com/huggingface/trl}}
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+ }
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+ ```
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+ "alpha_pattern": {},
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+ "auto_mapping": null,
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+ "base_model_name_or_path": "google/gemma-3-270m-it",
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+ "bias": "none",
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+ "corda_config": null,
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+ "eva_config": null,
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+ "exclude_modules": null,
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layer_replication": null,
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+ "layers_pattern": null,
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+ "loftq_config": {},
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+ "lora_dropout": 0.1,
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+ "megatron_core": "megatron.core",
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+ "peft_type": "LORA",
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+ "r": 64,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "k_proj",
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+ "v_proj",
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+ "gate_proj",
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+ "up_proj",
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+ "o_proj",
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+ "q_proj",
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+ "down_proj"
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+ ],
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+ "task_type": "CAUSAL_LM",
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+ "trainable_token_indices": null,
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+ "use_dora": false,
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+ "use_rslora": false
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+ }
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+ {{ bos_token }}
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+ {%- if messages[0]['role'] == 'system' -%}
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+ {%- if messages[0]['content'] is string -%}
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+ {%- set first_user_prefix = messages[0]['content'] + '
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+ ' -%}
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+ {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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+ ' -%}
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+ {%- set loop_messages = messages[1:] -%}
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+ {%- set loop_messages = messages -%}
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+ {%- endif -%}
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+ {%- for message in loop_messages -%}
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+ {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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+ {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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+ {%- endif -%}
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+ {%- if (message['role'] == 'assistant') -%}
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+ {%- set role = "model" -%}
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+ {%- else -%}
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+ {%- set role = message['role'] -%}
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+ {%- endif -%}
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+ {{ '<start_of_turn>' + role + '
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+ ' + (first_user_prefix if loop.first else "") }}
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+ {%- if message['content'] is string -%}
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+ {{ message['content'] | trim }}
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+ {%- elif message['content'] is iterable -%}
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+ {%- for item in message['content'] -%}
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+ {%- if item['type'] == 'image' -%}
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+ {{ '<start_of_image>' }}
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+ {%- elif item['type'] == 'text' -%}
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+ {{ item['text'] | trim }}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- else -%}
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+ {{ raise_exception("Invalid content type") }}
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+ {%- endif -%}
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+ {{ '<end_of_turn>
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+ ' }}
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+ {%- endfor -%}
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+ {%- if add_generation_prompt -%}
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+ {{'<start_of_turn>model
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+ '}}
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+ {%- endif -%}
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