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--- |
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license: apache-2.0 |
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language: |
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- tr |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/Turkcell-LLM-7b-v1-GGUF |
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This is quantized version of [TURKCELL/Turkcell-LLM-7b-v1](https://huggingface.co/TURKCELL/Turkcell-LLM-7b-v1) created using llama.cpp |
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# Original Model Card |
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<img src="https://huggingface.co/TURKCELL/Turkcell-LLM-7b-v1/resolve/main/icon.jpeg" |
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alt="Turkcell LLM" width="300"/> |
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# Turkcell-LLM-7b-v1 |
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This model is an extended version of a Mistral-based Large Language Model (LLM) for Turkish. It was trained on a cleaned Turkish raw dataset containing 5 billion tokens. The training process involved using the DORA method initially. Following this, we utilized Turkish instruction sets created from various open-source and internal resources for fine-tuning with the LORA method. |
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## Model Details |
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- **Base Model**: Mistral 7B based LLM |
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- **Tokenizer Extension**: Specifically extended for Turkish |
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- **Training Dataset**: Cleaned Turkish raw data with 5 billion tokens, custom Turkish instruction sets |
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- **Training Method**: Initially with DORA, followed by fine-tuning with LORA |
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### DORA Configuration |
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- `lora_alpha`: 128 |
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- `lora_dropout`: 0.05 |
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- `r`: 64 |
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- `target_modules`: "all-linear" |
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### LORA Fine-Tuning Configuration |
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- `lora_alpha`: 128 |
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- `lora_dropout`: 0.05 |
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- `r`: 256 |
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- `target_modules`: "all-linear" |
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## Usage Examples |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1") |
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tokenizer = AutoTokenizer.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1") |
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messages = [ |
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{"role": "user", "content": "Türkiye'nin başkenti neresidir?"}, |
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] |
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") |
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eos_token = tokenizer("<|im_end|>",add_special_tokens=False)["input_ids"][0] |
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model_inputs = encodeds.to(device) |
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model.to(device) |
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generated_ids = model.generate(model_inputs, |
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max_new_tokens=1024, |
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do_sample=True, |
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eos_token_id=eos_token) |
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decoded = tokenizer.batch_decode(generated_ids) |
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print(decoded[0]) |
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