metadata

base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
license: apache-2.0
language:
  - en
  - ar
datasets:
  - Abdulrhman37/metallurgy-qa
pipeline_tag: text2text-generation

Fine-Tuned Llama Model for Metallurgy and Materials Science

Developed by: Abdulrhman37
License: Apache-2.0
Base Model: unsloth/meta-llama-3.1-8b-bnb-4bit

This fine-tuned Llama model specializes in metallurgy, materials science, and engineering. It has been enhanced to provide precise and detailed responses to technical queries, making it a valuable tool for professionals, researchers, and enthusiasts in the field.

🛠️ Training Details

This model was fine-tuned with:

Unsloth: Enabled 2x faster training using efficient parameter optimization.
Hugging Face TRL: Used for advanced fine-tuning and training capabilities.

Fine-tuning focused on enhancing domain-specific knowledge using a dataset curated from various metallurgical research and practical case studies.

🔑 Features

Supports text generation with scientific and technical insights.
Provides domain-specific reasoning with references to key metallurgical principles and mechanisms.
Built for fast inference with bnb-4bit quantization for optimized performance.

🌟 Example Use Cases

Material property analysis (e.g., "How does adding rare earth elements affect magnesium alloys?").
Failure mechanism exploration (e.g., "What causes porosity in gas metal arc welding?").
Corrosion prevention methods (e.g., "How does cathodic protection work in marine environments?").

📦 How to Use

You can load the model using the transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Abdulrhman37/metallurgy-llama")
model = AutoModelForCausalLM.from_pretrained("Abdulrhman37/metallurgy-llama")

# Example Query
prompt = "Explain the role of manganese in Mg-Al-Mn systems."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=150)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)


This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)