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