writ-lint-0.8B

A fine-tuned Qwen3.5-0.8B model that evaluates the quality of AI agent instructions and generates actionable improvement feedback.

Part of the Tier 2.5 hybrid architecture in enwrit -- the communication layer for AI agents.

How It Works

This model is one half of a hybrid scoring system:

1. LightGBM (bundled in the enwrit CLI) predicts headline + 6 dimension scores (~1ms)
2. writ-lint-0.8B (this model) generates issues (ERROR/WARNING/INFO) and improvement suggestions, using the instruction text and LightGBM-predicted scores as context

The model focuses entirely on generating actionable feedback, not scores. Scores from LightGBM are passed in the prompt so the model can target weak dimensions.

Usage

Via the enwrit CLI (recommended)

pip install enwrit
pip install llama-cpp-python  # CPU inference, ~10s per instruction

writ lint AGENTS.md --deep-local

The model is auto-downloaded to ~/.writ/models/ on first use.

Direct inference with llama-cpp-python

from llama_cpp import Llama
import json

model = Llama(
    model_path="writ-lint-0.8B-Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,  # GPU acceleration (0 for CPU-only)
    verbose=False,
)

prompt = """<|im_start|>system
You are an expert instruction quality evaluator. Given an instruction and its quality scores, generate specific issues and improvement suggestions.<|im_end|>
<|im_start|>user
## Instruction to evaluate

{instruction_text}

## Quality scores (predicted)

Headline: 52/100
Clarity: 58 | Structure: 65 | Coverage: 42 | Brevity: 71 | Examples: 28 | Verification: 35

Analyze the instruction. Return JSON with "issues" (level + message) and "suggestions".<|im_end|>
<|im_start|>assistant
"""

output = model.create_completion(
    prompt,
    max_tokens=1024,
    temperature=0.3,
    response_format={"type": "json_object"},
)

feedback = json.loads(output["choices"][0]["text"])
print(json.dumps(feedback, indent=2))

Output Format

{
  "issues": [
    {"level": "ERROR", "message": "Missing concrete code examples for error handling patterns."},
    {"level": "WARNING", "message": "Verification steps are subjective rather than binary."},
    {"level": "INFO", "message": "Consider adding a 'Rules' section for behavioral constraints."}
  ],
  "suggestions": [
    "Add a 'Code Examples' section with 'Good vs Bad' patterns for the most critical rules.",
    "Replace subjective verification with specific CLI commands (e.g., `pytest`, `ruff check`).",
    "Include numeric thresholds for measurable constraints (e.g., 'max 100 lines per function')."
  ]
}

Training Details

Parameter	Value
Base model	Qwen/Qwen3.5-0.8B
Method	LoRA (r=32, alpha=64, dropout=0) via Unsloth
Training data	6,536 Tier 3 AI evaluations (Gemini-scored instructions)
Issues in training data	30,830 (avg 4.7 per instruction)
Suggestions in training data	19,602 (avg 3.0 per instruction)
Non-coding examples	145 seed instructions across 15 domains
Epochs	1
Batch size	1 (gradient accumulation: 16, effective batch: 16)
Max sequence length	4096 tokens
Learning rate	2e-4 (cosine schedule, 10% warmup)
Precision	bf16
Quantization	Q4_K_M (GGUF)
Training hardware	NVIDIA RTX 5090 (32GB VRAM)
Training time	~5.5 hours

Evaluation

Compared against retrieval-based approaches (v1/v2/v3) on a held-out validation set:

Approach	Relevance	All-Feedback Specificity	Issues/Instruction	Type
v1_shap_knn	0.157	0.129	N/A	retrieval
v2_hybrid	0.266	0.136	N/A	retrieval
v3_tfidf	0.262	0.144	N/A	retrieval
writ-lint-0.8B	0.236	0.364	4.7	generative

Key strengths:

• 100% JSON parse success (via constrained decoding)
• Generates novel, context-specific feedback (not limited to seen examples)
• Weak-dimension targeting: 0.47 (issues correlate with low-scoring dimensions)
• Low domain mismatch: 0.014 (doesn't give coding feedback to non-coding instructions)

Scoring Dimensions

The 6 quality dimensions (scored by LightGBM, targeted by this model):

Dimension	What it measures
Clarity	Unambiguous language, precise terminology, defined jargon
Structure	Logical sections, hierarchy, scannable formatting
Coverage	Completeness of rules, edge cases, responsibilities
Brevity	Concise without sacrificing meaning, no redundancy
Examples	Code samples, input/output patterns, good vs bad
Verification	Testable criteria, CLI commands, specific thresholds

Files

• writ-lint-0.8B-Q4_K_M.gguf -- Quantized model for inference (504 MB)

Links

• enwrit CLI -- Open-source CLI tool
• enwrit.com -- Platform with Hub, AI scoring, and more
• PyPI -- pip install enwrit

License

MIT