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--- |
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license: mit |
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tags: |
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- causal_lm |
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- generated_from_trainer |
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base_model: broadfield-dev/gemma-3-270m-tuned-0102-0441 |
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datasets: |
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- broadfield-dev/abisee_cnn_dailymail_concise-Broadfield |
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model-index: |
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- name: gemma-3-270m-tuned-0102-0441-tuned-0102-1157 |
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results: [] |
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--- |
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# gemma-3-270m-tuned-0102-0441-tuned-0102-1157 |
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This model is a fine-tuned version of [broadfield-dev/gemma-3-270m-tuned-0102-0441](https://huggingface.co/broadfield-dev/gemma-3-270m-tuned-0102-0441) on the [broadfield-dev/abisee_cnn_dailymail_concise-Broadfield](https://huggingface.co/broadfield-dev/abisee_cnn_dailymail_concise-Broadfield) dataset. |
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## Training Details |
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- **Task:** CAUSAL_LM |
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- **Epochs:** 1 |
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- **Learning Rate:** 2e-05 |
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- **Gradient Accumulation Steps:** 4 |
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## Entity Labels |
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`['LABEL_0', 'LABEL_1']` |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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model_id = "broadfield-dev/gemma-3-270m-tuned-0102-0441-tuned-0102-1157" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16) |
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messages = [ |
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{"role": "system", "content": "Summarize this: "}, |
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{"role": "user", "content": "Your input here..."} |
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] |
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device) |
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outputs = model.generate(inputs, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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