How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="braindao/iq-code-evmind-v3-granite-8b-instruct-all")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("braindao/iq-code-evmind-v3-granite-8b-instruct-all")
model = AutoModelForCausalLM.from_pretrained("braindao/iq-code-evmind-v3-granite-8b-instruct-all", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
Quick Links

This LLM, named braindao/iq-code-evmind-v3-granite-8b-instruct-all, is a specialized model for generating Solidity code.

It is based on ibm-granite/granite-8b-code-instruct and has been fine-tuned using the braindao/Solidity-Dataset.

The model focuses on the "average", "beginner", and "content" columns of the dataset, likely to provide code examples suitable for different skill levels.

This LLM is designed to assist with Solidity programming tasks, particularly for blockchain and smart contract development on Ethereum-compatible platforms.

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Model size
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Dataset used to train braindao/iq-code-evmind-v3-granite-8b-instruct-all