🤏 smolified-transcripts-checker

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.

📦 Asset Details

• Origin: Smolify Foundry (Job ID: 3a495556)
• 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.

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

model_id = "sugatobagchi/smolified-transcripts-checker"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {'role': 'system', 'content': '''Client Style Guide: 1. Numbers 0-9 spelled out, 10+ digits. 2. Speaker labels in all caps followed by a colon. 3. US currency uses '$' prefix. 4. Dates in 'Month Day, Year' format. 5. 'uh' and 'um' removed. 6. Sentences with any potential error, ambiguity, or awkwardness are flagged with '[??]' at the end.'''},
    {'role': 'user', 'content': '''Interviewer: We're talking about four key points here. Participant: Yes. Exactly four points to cover. Not three, not five.'''}
]
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 sugatobagchi. Generated via Smolify.ai.