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library_name: transformers
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Model Card for Model ID

We developed a Large Language Model (LLM) on top of DeepSeek, achieving ChatGPT-4-level performance specifically for the Move programming language. This model offers advanced code generation, error handling, and context-aware support, optimized for Move’s unique requirements. By combining DeepSeek’s foundation with a Move focus, our LLM provides reliable, high-performance assistance for smart contract and blockchain development within the Move ecosystem.

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • Developed by: FLock.io
  • Funded by [optional]: [More Information Needed]
  • Shared by [optional]: [More Information Needed]
  • Model type: [More Information Needed]
  • Language(s) (NLP): [More Information Needed]
  • License: [More Information Needed]
  • Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Start with this prompt:

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("flock-io/move-llm")
model = AutoModelForCausalLM.from_pretrained("flock-io/move-llm")
# Tokenize input text
sys_prompt = "You are an expert in Aptos Move programming language."
input_text = sys_prompt + "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))