Edit model card

This model is a finely-tuned version specifically designed to generate and resolve queries related to the Solidity programming language. This model has been developed from the robust foundation provided by ajibawa-2023/Code-Llama-3-8B and has undergone specialized fine-tuning to optimize its performance in tasks associated with Solidity, the primary language used for developing smart contracts on the Ethereum blockchain.

Key Features:

Solidity Code Generation: The model can generate Solidity code snippets, offering quick and accurate solutions for various development needs. Query Resolution: It answers technical and conceptual questions about Solidity, covering basic concepts to advanced topics, facilitating learning and problem-solving. Customized Optimization: The fine-tuning ensures the model is optimized to handle specific contexts and nuances of Solidity, providing more relevant and detailed responses. Applications:

Smart Contract Development: Assists developers in creating, optimizing, and debugging smart contracts in Solidity. Education and Training: Serves as an educational tool for those looking to learn Solidity, providing clear explanations and practical examples. Technical Assistance: Acts as a virtual technical assistant, answering queries and providing solutions to complex issues in smart contract development. Base Model:

This model is based on ajibawa-2023/Code-Llama-3-8B, known for its advanced code generation capabilities and deep understanding of programming languages.

How to Use:

You can integrate this model into your projects via the Hugging Face platform, utilizing the provided APIs and tools to facilitate its implementation and use in various applications.

Example Usage:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/iq-code-evmind-v1-code-llama3-8b-instruct-pro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "How can I define a basic contract structure in Solidity?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With iq-code-evmind-v1-code-llama3-8b-instruct-pro, you will have a powerful and specialized tool to handle everything related to Solidity development, from code generation to technical query resolution.

Downloads last month
11
Safetensors
Model size
8.03B params
Tensor type
FP16
·
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train braindao/iq-code-evmind-v2-llama3-code-8b-instruct

Collection including braindao/iq-code-evmind-v2-llama3-code-8b-instruct