README.md

metadata

license: mit
language:
  - en
library_name: transformers
tags:
  - scribelm
  - hemanth-hm
  - causal-lm
  - style-transfer
  - writing-assistant
  - onnx
  - wasm
datasets:
  - gnumanth/scribelm-dataset
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
pipeline_tag: text-generation

gnumanth/scribelm — 134.8M Multi-Head Authoring & Voice Critic Model (Hemanth HM Style)

scribelm (ScribeLMForAuthoring) is an intact 30-layer, 134.85M-parameter causal transformer and 5-axis voice critic fine-tuned on 388 Markdown blog posts (~230,000 tokens across 15+ years) from h3manth.com/scribe.

Architecture (ScribeLMForAuthoring)

• Backbone: Intact 30-layer HuggingFaceTB/SmolLM2-135M-Instruct (d_model=576, n_heads=9, n_kv_heads=3, intermediate_size=1536, vocab_size=49,152, eager attention for deterministic browser ONNX export, bottom 20 layers frozen to preserve fluent English & code syntax).
• Four Joint Heads:
  1. lm_head: Causal autoregressive language modeling head for writing blog posts, rewriting dense specs, and drafting social launch threads.
  2. voice_head: Calibrated p_hemanth authenticity classifier (AUROC = 1.0, ECE = 0.037).
  3. style_head: 5-axis style telemetry regressor predicting (directness, hook_strength, zero_fluff, code_first, cadence).
  4. task_head: 4-way writing task router (blog, rewrite, social, critic) (91.1% accuracy).

Three Runtime Tiers (N = 146 Held-Out Test Split · B = 1,000 Bootstrap 95% CIs)

Runtime Tier	Artifact	Size	Composite	Voice Auth Acc	Task Acc	5-Axis Fidelity	p_hemanth AUROC	p50 Latency
Tier 1A (PyTorch FP32)	model.safetensors	514.45 MB	91.7%	96.58%	91.1%	85.79%	1.0	155.177 ms
Tier 1B (ONNX Int8 Browser/CPU)	onnx/model_quantized.onnx	157.84 MB	89.57%	92.47%	89.73%	85.54%	1.0	135.594 ms
Tier 2 (Zero-Dep JS/Py Engine)	scribelm npm / PyPI	0 MB	99.64%	100.0%	100.0%	98.79%	1.0	0.584 ms
Hybrid (Fast + ONNX Int8)	Combined	157.84 MB	99.59%	100.0%	100.0%	98.63%	1.0	0.666 ms

Live Qualitative Generations (134.8M Neural Checkpoint)

nullspan structured extraction launch (rewrite)

• Input Draft: Baked nullspan a single-pass structured extraction with exact [start, end] character spans, calibrated p_null abstention, algebraic invariant checks (subtotal == nodes * hourly_rate), and p10/p50/p90 quantiles.
• Neural Generation (134.8M, p_hemanth=1.0):

One-pass structured extraction with exact `start`, `end` character spans, calibrated `p_null` abstentions, algebraic `invariance` checks, and 95% quantile `p_hemanth=0.2` `p_hemanth=0.4` `p_hemanth=0.6` `p_hemanth=0.8` `p_hemanth=0.9` `p_hemanth=1.0`. Zero overhead for all

Skills vs MCP for AI Agents (rewrite)

• Input Draft: In contemporary artificial intelligence agent architectures, practitioners often debate whether modular Skill definitions supersede the Model Context Protocol (MCP).
• Neural Generation (134.8M, p_hemanth=1.0):

Skills vs MCP? Wrong question.

Both are tools for building AI agents. But they solve different problems:
- Skills = what your agent knows how to do
- MCP = what your agent can access

One teaches you how to do it, while the other provides a way for your agent to access it.

But they solve different problems:
- Skills = what your agent knows how to do
- MCP = what your agent can access

One teaches you how to do it, while the

Why Every Agent Needs an Escape Hatch (blog)

• Input Draft: Deterministic guardrails vs probabilistic LLM loops, calibrated p_null abstention, and zero-dep local fallbacks.
• Neural Generation (134.8M, p_hemanth=1.0):

# Why Every Agent Needs an Escape Hatch

Probabilistic LLM loops are great until they hallucinate at 3 AM. Short answer: **keep the fast path deterministic and wire a calibrated `p_null` escape hatch.**

- **Deterministic fast path:** handle 95% of structured payloads in `<$1M-$2M>` payloads by wireing `<$0.1M-$0.4M>`. Handle 75% of structured payloads in `<$1M-$

Usage (Python + Transformers)

import torch
from transformers import AutoConfig, AutoModel, AutoTokenizer

repo_id = "gnumanth/scribelm"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
config = AutoConfig.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, config=config, trust_remote_code=True)

res = model.generate_text(
    tokenizer,
    "Rewrite this into Hemanth HM style:\nIn contemporary AI agent architectures, practitioners debate whether modular Skill definitions supersede MCP.",
    max_new_tokens=96,
)
print(res["text"])
print(res["p_hemanth"], res["style_scores"])