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  *.zip filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ base_model: google/gemma-3-270m-it
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+ library_name: peft
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+ pipeline_tag: text-generation
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+ language: en
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+ license: apache-2.0
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+ tags:
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+ - lora
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+ - sft
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+ - transformers
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+ - trl
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+ - unsloth
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+ - fine-tuned
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+ datasets:
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+ - theprint/titles-n-tags-alpaca
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+ ---
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+ # TiTan-Gemma3-0.27B
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+
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+ A fine-tuned Gemma 3 270M model, tuned for generating conversation titles and tags.
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+
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+ ## Model Details
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+
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+ This model is a fine-tuned version of google/gemma-3-270m-it using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.
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+
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+ - **Developed by:** theprint
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+ - **Model type:** Causal Language Model (Fine-tuned with LoRA)
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+ - **Language:** en
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+ - **License:** apache-2.0
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+ - **Base model:** google/gemma-3-270m-it
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+ - **Fine-tuning method:** LoRA with rank 128
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+
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+ ## Intended Use
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+
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+ Title and tag generation.
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+
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+
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+ ## GGUF Quantized Versions
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+
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+ Quantized GGUF versions are available in the [theprint/TiTan-Gemma3-0.27B-GGUF](https://huggingface.co/theprint/TiTan-Gemma3-0.27B-GGUF) repo.
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+
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+ - `TiTan-Gemma3-0.27B-f16.gguf` (837.7 MB) - 16-bit float (original precision, largest file)
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+ - `TiTan-Gemma3-0.27B-q3_k_m.gguf` (320.8 MB) - 3-bit quantization (medium quality)
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+ - `TiTan-Gemma3-0.27B-q4_k_m.gguf` (351.4 MB) - 4-bit quantization (medium, recommended for most use cases)
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+ - `TiTan-Gemma3-0.27B-q5_k_m.gguf` (368.0 MB) - 5-bit quantization (medium, good quality)
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+ - `TiTan-Gemma3-0.27B-q6_k.gguf` (439.9 MB) - 6-bit quantization (high quality)
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+ - `TiTan-Gemma3-0.27B-q8_0.gguf` (448.0 MB) - 8-bit quantization (very high quality)
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ The titles-n-tags set was specifically created for finetuning models on titling and tagging.
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+
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+ - **Dataset:** theprint/titles-n-tags-alpaca
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+ - **Format:** alpaca
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+
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+ ### Training Procedure
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+
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+ - **Training epochs:** 2
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+ - **LoRA rank:** 128
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+ - **Learning rate:** 0.0001
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+ - **Batch size:** 6
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+ - **Framework:** Unsloth + transformers + PEFT
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+ - **Hardware:** NVIDIA RTX 5090
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+
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+ ## Usage
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+
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+ ```python
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+ from unsloth import FastLanguageModel
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+ import torch
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+
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+ # Load model and tokenizer
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+ model, tokenizer = FastLanguageModel.from_pretrained(
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+ model_name="theprint/TiTan-Gemma3-0.27B",
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+ max_seq_length=4096,
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+ dtype=None,
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+ load_in_4bit=True,
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+ )
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+
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+ # Enable inference mode
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+ FastLanguageModel.for_inference(model)
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+
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+ # Example usage
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+ inputs = tokenizer(["Your prompt here"], return_tensors="pt")
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+ outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
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+ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ print(response)
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+ ```
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+
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+ ### Alternative Usage (Standard Transformers)
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ "theprint/TiTan-Gemma3-0.27B",
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+ torch_dtype=torch.float16,
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+ device_map="auto"
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained("theprint/TiTan-Gemma3-0.27B")
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+
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+ # Example usage
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+ messages = [
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+ {"role": "system", "content": "You are a helpful assistant."},
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+ {"role": "user", "content": "Your question here"}
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+ ]
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+
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+ inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
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+ outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
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+ response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
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+ print(response)
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+ ```
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+
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+ ### Using with llama.cpp
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+
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+ ```bash
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+ # Download a quantized version (q4_k_m recommended for most use cases)
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+ wget https://huggingface.co/theprint/TiTan-Gemma3-0.27B/resolve/main/gguf/TiTan-Gemma3-0.27B-q4_k_m.gguf
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+
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+ # Run with llama.cpp
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+ ./llama.cpp/main -m TiTan-Gemma3-0.27B-q4_k_m.gguf -p "Your prompt here" -n 256
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+ ```
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+ ## Limitations
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+
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+ May provide incorrect information.
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+
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+ ## Citation
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+
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+ If you use this model, please cite:
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+
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+ ```bibtex
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+ @misc{titan_gemma3_0.27b,
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+ title={TiTan-Gemma3-0.27B: Fine-tuned google/gemma-3-270m-it},
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+ author={theprint},
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+ year={2025},
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+ publisher={Hugging Face},
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+ url={https://huggingface.co/theprint/TiTan-Gemma3-0.27B}
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+ }
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+ ```
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+
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+ ## Acknowledgments
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+
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+ - Base model: [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it)
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+ - Training dataset: [theprint/titles-n-tags-alpaca](https://huggingface.co/datasets/theprint/titles-n-tags-alpaca)
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+ - Fine-tuning framework: [Unsloth](https://github.com/unslothai/unsloth)
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+ - Quantization: [llama.cpp](https://github.com/ggerganov/llama.cpp)
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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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+ ' -%}
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+ {%- else -%}
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+ {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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+
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+ ' -%}
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+ {%- endif -%}
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+ {%- set loop_messages = messages[1:] -%}
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+ {%- else -%}
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+ {%- set first_user_prefix = "" -%}
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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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