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--- |
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language: |
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- en |
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tags: |
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- llama |
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- fine-tuned |
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- causal-lm |
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license: apache-2.0 |
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base_model: YongganFu/Llama-400M-12L |
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--- |
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# data4elm_full_finetuned_no_lora |
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Fine-tuned Llama-400M model |
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## Model Details |
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This model is a fully fine-tuned version of [YongganFu/Llama-400M-12L](https://huggingface.co/YongganFu/Llama-400M-12L). |
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## Model Files |
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The model directory contains: |
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- `config.json` - Model configuration |
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- `generation_config.json` - Generation settings |
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- `model.safetensors` - Model weights in safetensors format |
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- `special_tokens_map.json` - Special token mapping |
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- `tokenizer.json` - Tokenizer configuration |
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- `tokenizer.model` - Tokenizer model |
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- `trainer_state.json` - Training state information |
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- `training_args.bin` - Training arguments |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the fine-tuned model |
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model = AutoModelForCausalLM.from_pretrained("lxaw/data4elm_full_finetuned_no_lora") |
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tokenizer = AutoTokenizer.from_pretrained("lxaw/data4elm_full_finetuned_no_lora") |
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# Example usage |
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input_text = "What is the capital of France?" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(inputs.input_ids, max_length=50) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Training Details |
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This model was fine-tuned using standard full fine-tuning (not parameter-efficient methods like LoRA). |
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