| --- |
| 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](https://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`)**: |
| |
| ```markdown |
| 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`)**: |
| |
| ```markdown |
| 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`)**: |
|
|
| ```markdown |
| # 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) |
|
|
| ```python |
| 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"]) |
| ``` |
|
|
