---
license: apache-2.0
base_model: Qwen/Qwen3.6-27B
language:
- en
pipeline_tag: text-generation
library_name: gguf
tags:
- legal
- consumer-protection
- fdcpa
- fcra
- letter-writing
- grant-writing
- gguf
- llama-cpp
- ollama
- qwen3
- lora
---

# LocalLegal-27B

**A calm, dignified consumer-rights letter-writer & grant writer.** It *organizes* the facts a person gives it and *drafts* clear, statute-grounded documents they can review, sign, and send themselves — debt-validation and cease-contact letters (FDCPA), credit-report disputes and method-of-verification letters (FCRA), goodwill and medical-billing letters, identity-theft blocks, and grant/assistance narratives.

> **This is document preparation, not legal advice.** LocalLegal is not a lawyer, does not give legal advice, and never predicts the outcome of a case. It states what a statute *says* and what a letter *requests* — and hands anything past the letter stage (lawsuits, garnishment, liens, court dates) to free legal aid or a licensed attorney.

Built by **[Swarm & Bee](https://huggingface.co/SwarmandBee)** — the brain behind the LocalLegal "write + send certified from home" flow.

---

## 🏆 It beat base — decisively, on both domains

Stage-5 gate: **held-out, per-domain, deterministic** evaluation. Teacher-forced cross-entropy / perplexity on chat-templated eval, base vs cooked, **same tokenizer & template, N=400/domain, seed 1117, seq 4096, bf16** on an RTX PRO 6000. **No LLM-as-judge** — a number anyone can re-derive.

| Domain | Base ppl | LocalLegal-27B ppl | Δ | Base CE → Cooked CE |
|---|--:|--:|--:|---|
| **Legal** (FDCPA/FCRA letters) | 16.662 | **2.058** | **−87.65%** | 2.813 → 0.722 |
| **Grant** (proposal narratives) | 3.250 | **2.022** | **−37.80%** | 1.179 → 0.704 |

**Beat base on both. Killed domains: none.** Cooked CE (~0.70–0.72) matches the training landing (~0.64–0.77) → coherent, **no overfit**. The legal gain exceeds our DiabeticAnchor-27B reference (+57%). The 44%-share, 9%-truncation grant tail passed clean — not undercooked.

Receipts (deterministic, re-runnable): `beat_base_27b.py` · `beat_base_27b.log` · `beat_base_27b_verdict.json`.

---

## 🔧 Usage

This repo ships a **Q4 GGUF** (~16 GB) — runs on llama.cpp / Ollama / LM Studio. Qwen3.6 is a **thinking model**, so the chat template prefills an empty `<think>` block; the included `Modelfile` handles this for you.

### Ollama
```bash
# with the included Modelfile (carries the template + system prompt + params)
ollama create locallegal-27b -f locallegal-27b.Modelfile
ollama run locallegal-27b "Draft an FDCPA debt-validation letter. Collector: Midland Credit. Account #4402, $1,284 medical debt I don't recognize."
```

### llama.cpp
```bash
./llama-cli -m locallegal-27b-q4.gguf -c 8192 --temp 0.6 --top-p 0.9 \
  -p "<your chat-templated prompt>"
```

**Recommended sampling:** `temperature 0.6`, `top_p 0.9`, `num_ctx 8192`. Stop tokens `<|im_start|>` / `<|im_end|>` (ChatML).

### System prompt (its identity — every letter includes header, RE: line, statute cite, specific request, response window, signature block)
The model is trained to the LocalLegal persona: calm, factual, never shaming, uses the verbs *organize / draft / prepare / review / track*, and refuses to say "legal advice," "sue them," "you'll win," or "guaranteed." Full system prompt ships in the `Modelfile`.

---

## 🍳 How it was cooked

- **Base:** [`Qwen/Qwen3.6-27B`](https://huggingface.co/Qwen/Qwen3.6-27B) (Apache-2.0) — hybrid Gated-DeltaNet + Gated-Attention arch, thinking model.
- **Method:** LoRA r32 / α16 on attn+mlp · LR 1e-5 · cosine · seq 4096 · bf16 · Unsloth + TRL. Clean 16-bit merge → Q4 GGUF.
- **Corpus:** **78,231 train / 2,500 eval**, curated down from ~340K raw rows via cross-domain dedup, eval carve-out, hash-scrub of contaminants, and near-dup pruning. Split **Legal 57.8% / Grant 42.2%** (natural balance, zero synthetic upsampling). **Per-domain true holdout** eval (legal 1,500 / grant 1,000).
- **Loss:** 2.156 → 0.768 (−64%), smooth monotone, grad_norm 0.20 — textbook curve, no spikes/NaN, no overcook.
- **Rig:** SwarmRails (owned) · 1× RTX PRO 6000 Blackwell 96 GB · 350 W thermal cap · 45.7 h wall. Sovereign compute — cooked on our own iron.
- **Discipline:** full canary-then-cook (5-stage senior-hack review) — beat-base-or-kill, per domain, no blended half-truths.

## 📁 Files
- `locallegal-27b-q4.gguf` — Q4 quantized weights (~16 GB)
- `locallegal-27b.Modelfile` — Ollama template + system prompt + params

## 🔒 Defendable
Every claim here has a receipt: deterministic per-domain beat-base eval (no LLM judge), monotone loss curve, hash-verified corpus, and a full cook flightsheet. Show the math, verify it yourself.

## ⚖️ Scope & safety
Document preparation only. Not legal advice, not a lawyer, no outcome predictions. Anything beyond the letter stage → free legal aid (LawHelp.org) / a licensed consumer-protection attorney / your state Attorney General. As a fine-tune of Qwen3.6-27B, it inherits the base model's Apache-2.0 terms and general LLM limitations (it can be wrong — a human reviews and signs every letter).

## Citation
```
@misc{locallegal27b2026,
  title  = {LocalLegal-27B: a statute-grounded consumer-rights letter-writer},
  author = {Swarm and Bee},
  year   = {2026},
  note   = {LoRA fine-tune of Qwen3.6-27B; deterministic per-domain beat-base eval},
  url    = {https://huggingface.co/SwarmandBee/LocalLegal-27B}
}
```