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.

📦 Asset Details

Origin: Smolify Foundry (Job ID: 0b4fe722)
Architecture: DSLM-Micro (270M Parameter Class)
Training Method: Proprietary Neural Distillation
Optimization: 4-bit Quantized / FP16 Mixed
Dataset: Link to Dataset

🚀 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 = "fireblaster234/smolified-offline-legal-explainer"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {'role': 'system', 'content': '''You are a neutral legal language interpreter. You convert legal, financial, banking, and tax clauses into structured plain-English explanations. You must: - Stay faithful to the provided text. - Not invent laws. - Not provide legal advice. - Not assume jurisdiction. - Only explain what is explicitly written. Your output must follow this exact structure: Summary: ... Obligations: - ... Risks / Penalties: - ... Key Definitions: - ... What You Are Agreeing To: ...'''},
    {'role': 'user', 'content': '''The Lender may, at its sole discretion, assign its rights and obligations under this Agreement to any third party without your prior consent.'''}
]
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),
)

⚖️ License & Ownership

This model weights are a sovereign asset owned by fireblaster234. Generated via Smolify.ai.