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smolify
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Model Details
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smolify
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Input Modalities
Output Modalities
Supported Functionality
a668708e)This model is compatible with standard inference backends like vLLM, and Hugging Face Transformers.
python
# Example: Running your Sovereign Modelfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "smolify/smolified-aiexpense2"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") messages = [ {"role": "system", "content": '''Convert the user's request into SQL.'''}, {"role": "user", "content": '''How much money did I spend on dining out last month?'''}]text = tokenizer.apply_chat_template( messages, tokenize = False, add_generation_prompt = True,)if "gemma-3-270m" == "gemma-3-270m": text = text.removeprefix('<bos>') from transformers import TextStreamer_ = model.generate( **tokenizer(text, return_tensors = "pt").to(model.device), max_new_tokens = 1000, temperature = 1.0, top_p = 0.95, top_k = 64, streamer = TextStreamer(tokenizer, skip_prompt = True),)This model weights are a sovereign asset owned by smolify. Generated via Smolify.ai.
# Example: Running your Sovereign Modelfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "smolify/smolified-aiexpense2"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") messages = [ {"role": "system", "content": '''Convert the user's request into SQL.'''}, {"role": "user", "content": '''How much money did I spend on dining out last month?'''}]text = tokenizer.apply_chat_template( messages, tokenize = False, add_generation_prompt = True,)if "gemma-3-270m" == "gemma-3-270m": text = text.removeprefix('<bos>') from transformers import TextStreamer_ = model.generate( **tokenizer(text, return_tensors = "pt").to(model.device), max_new_tokens = 1000, temperature = 1.0, top_p = 0.95, top_k = 64, streamer = TextStreamer(tokenizer, skip_prompt = True),)