Create README.md

README.md

@@ -0,0 +1,61 @@
+---
+base_model: unsloth/mistral-7b-v0.3-bnb-4bit
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- gguf
+---
+
+# LLAMA-3.1 8B Chat Nuclear Model
+
+- **Developed by:** inetnuc
+- **License:** apache-2.0
+- **Finetuned from model:** unsloth/mistral-7b-v0.3-bnb-4bit
+
+This mistral-7b-v0.3 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 nuclear-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/mistral-7b-v0.3-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/inetnuc/mistral-7b-v0.3-bnb-4bit-chat-nuclear-lora-f16")
+model = AutoModelForCausalLM.from_pretrained("inetnuc/inetnuc/mistral-7b-v0.3-bnb-4bit-chat-nuclear-lora-f16")
+
+# Example of generating text
+inputs = tokenizer("what is the iaea approach for cyber security?", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+