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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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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. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [FLock.io](https://www.flock.io/) |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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Start with this prompt: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("flock-io/move-llm") |
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model = AutoModelForCausalLM.from_pretrained("flock-io/move-llm") |
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# Tokenize input text |
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sys_prompt = "You are an expert in Aptos Move programming language." |
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input_text = sys_prompt + "Your input text here" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=1024) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
