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smolify/smolified-text-to-sql

sourceHugging Faceapache-2.0updated 9mo agoView on Hugging Face
2likes19downloads
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๐Ÿค smolified-text-to-sql

Intelligence, Distilled.

This is a Domain Specific Language Model (DSLM) generated by the Smolify Foundry.

It has been synthetically distilled from SOTA reasoning engines into a high-efficiency architecture, optimized for deployment on edge hardware (CPU/NPU) or low-VRAM environments.

๐Ÿ“ฆ Asset Details

  • โ€”Origin: Smolify Foundry (Job ID: 4b2d8a60)
  • โ€”Architecture: DSLM-Micro (270M Parameter Class)
  • โ€”Training Method: Proprietary Neural Distillation
  • โ€”Optimization: 4-bit Quantized / FP16 Mixed
  • โ€”Dataset: Link to Dataset

๐Ÿš€ Usage (Inference)

This model is compatible with standard inference backends like vLLM.

python
# Example: Running your Sovereign Model
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "smolify/smolified-text-to-sql"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {'role': 'system', 'content': '''You are a SQL generator. Schema: Table 'orders' (id, customer_name, amount, status, date). Translate the user question into a valid SQLite query. Output SQL only.'''},
    {'role': 'user', 'content': '''Find the most recent order from 'Jane Smith'.'''}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
).removeprefix('<bos>')

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1000,
    temperature = 1, top_p = 0.95, top_k = 64,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

โš–๏ธ License & Ownership

This model weights are a sovereign asset owned by smolify. Generated via Smolify.ai.

<img src="https://smolify.ai/smolify.gif" width="100"/>