inetnuc
/

TurkishLlama-3.1-8B-4bit-chat-nuclear-lora

@@ -1,200 +1,61 @@
 ---
-library_name: transformers
 tags:
 - unsloth
 ---
-# Model Card for Model ID
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 ---
+base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
+language:
+- en
+license: apache-2.0
 tags:
+- text-generation-inference
+- transformers
 - unsloth
+- llama
+- gguf
 ---
+# LLAMA-3.1 8B Chat Turkish Model
+- **Developed by:** inetnuc
+- **License:** apache-2.0
+- **Finetuned from model:** unsloth/Meta-Llama-3.1-8B-bnb-4bit
+This LLAMA-3.1 model was finetuned to enhance capabilities in text generation for nuclear-related topics. The training was accelerated using [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library, achieving a 2x faster performance.
+## Finetuning Process
+The model was finetuned using the Unsloth library, leveraging its efficient training capabilities. The process included the following steps:
+1. **Data Preparation:** Loaded and preprocessed turkish-related data.
+2. **Model Loading:** Utilized `unsloth/llama-3-8b-bnb-4bit` as the base model.
+3. **LoRA Patching:** Applied LoRA (Low-Rank Adaptation) for efficient training.
+4. **Training:** Finetuned the model using Hugging Face's TRL library with optimized hyperparameters.
+## Model Details
+- **Base Model:** `unsloth/llama-3.1-8b-bnb-4bit`
+- **Language:** English (`en`)
+- **License:** Apache-2.0
+## Author
+**MUSTAFA UMUT OZBEK**
+**https://www.linkedin.com/in/mustafaumutozbek/**
+**https://x.com/m_umut_ozbek**
+## Usage
+### Loading the Model
+You can load the model and tokenizer using the following code snippet:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("inetnuc/TurkishLlama-3.1-8B-4bit-chat-nuclear-lora")
+model = AutoModelForCausalLM.from_pretrained("inetnuc/TurkishLlama-3.1-8B-4bit-chat-nuclear-lora")
+# Example of generating text
+inputs = tokenizer("Türki̇ye'de nükleer enerji̇ yatirimlari artirilmali mi, ne düşünüyorsun?", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))