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-v3.1-granite-8b-instruct-beginner")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("braindao/iq-code-evmind-v3.1-granite-8b-instruct-beginner")
model = AutoModelForCausalLM.from_pretrained("braindao/iq-code-evmind-v3.1-granite-8b-instruct-beginner", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
Quick Links

This LLM, built on the robust ibm-granite/granite-8b-code-instruct model, is meticulously designed for generating high-quality Solidity code.

It utilizes the "average" column from the braindao/Solidity-Dataset to ensure a balanced and effective learning process.

Ideal for beginners and seasoned developers alike, this model aims to simplify the process of writing smart contracts and boost productivity in blockchain development.

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Dataset used to train braindao/iq-code-evmind-v3.1-granite-8b-instruct-beginner