Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,156 @@
+---
+base_model: meta-llama/Llama-3.2-3B-Instruct
+license: llama3.2
+language:
+- en
+library_name: transformers
+tags:
+- llama
+- gguf
+- summarization
+- fine-tuned
+- unsloth
+- trl
+- sft
+model_name: leader-comment-summarizer
+pipeline_tag: text-generation
+quantized_by: llama.cpp
+---
+
+# leader-comment-summarizer — Ecclesiastical Comment Summarization (GGUF)
+
+A fine-tuned Llama 3.2 3B Instruct model that summarizes ecclesiastical leader comments into concise, assignment-relevant summaries for missionary placement meetings. Strips endorsement boilerplate, focuses on actionable details (languages, health, skills, concerns).
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Base model** | `meta-llama/Llama-3.2-3B-Instruct` |
+| **Fine-tuning method** | QLoRA via Unsloth (rank=16, alpha=32) |
+| **Training framework** | TRL SFTTrainer, completion-only loss |
+| **Training data** | 1,464 PII-obfuscated leader comments with gold-standard summaries |
+| **Quantization** | Q4_K_M (1.9 GB) via llama.cpp |
+| **VRAM requirement** | ~3 GB (Q4_K_M) |
+| **Output format** | 30-40 word plain-text summary |
+
+## Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `model-q4km.gguf` | 1.9 GB | Q4_K_M quantization (recommended) |
+| `Modelfile` | — | Ollama Modelfile with system prompt embedded |
+| `system_prompt.txt` | — | System prompt (for API usage without Modelfile) |
+
+## Quick Start — Ollama
+
+```bash
+# Download the GGUF and Modelfile, then:
+ollama create leader-summarizer -f Modelfile
+
+# Call via API:
+curl -s http://localhost:11434/api/chat -d '{
+  "model": "leader-summarizer",
+  "stream": false,
+  "messages": [
+    {"role": "user", "content": "[[Name]] is a wonderful young man with a strong testimony. He speaks fluent Spanish from living in [[City]] for three years. Has mild anxiety that is well-managed with medication. Very independent and hardworking. Parents served in the [[Mission]] mission."}
+  ]
+}'
+```
+
+Expected response:
+```
+Fluent Spanish from three years in a Spanish-speaking city. Mild anxiety, well-managed with medication. Independent and hardworking. Family mission service background.
+```
+
+## Quick Start — Python
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="model-q4km.gguf", n_ctx=2048, n_gpu_layers=-1)
+response = llm.create_chat_completion(
+    messages=[
+        {"role": "system", "content": open("system_prompt.txt").read()},
+        {"role": "user", "content": leader_comment_text},
+    ],
+    temperature=0.3,
+    top_p=0.9,
+    max_tokens=128,
+)
+print(response["choices"][0]["message"]["content"])
+```
+
+## Input/Output Format
+
+**Input:** Raw leader comment text (may contain PII placeholders like `[[Name]]`, `[[City]]`).
+
+**Output:** A 30-40 word plain-text summary focusing on assignment-relevant details.
+
+### What the Model Keeps
+- Languages spoken and proficiency
+- Health/medical conditions and management
+- Specific skills (musical, technical, athletic)
+- Concerns about independence or readiness
+- Personality traits affecting placement
+- Service preferences
+
+### What the Model Strips
+- General endorsement ("strong testimony", "wonderful young man")
+- Worthiness/recommend statements
+- Boilerplate language that applies to all candidates
+
+## Important Usage Notes
+
+- **The Modelfile embeds the system prompt.** When using Ollama with the provided Modelfile, you don't need to send a separate system message — just send the comment as the user message.
+- **If using the raw GGUF** (without Modelfile), include `system_prompt.txt` as the system message in every request.
+- **Temperature 0.3** produces consistent, focused summaries. Higher values introduce variability.
+- **max_tokens 128** is sufficient — summaries are typically 30-40 words.
+
+## Training Details
+
+- **Method:** QLoRA with Unsloth on WSL2 Ubuntu 24.04
+- **GPU:** NVIDIA RTX 1000 Ada (6 GB VRAM)
+- **Epochs:** 3
+- **Learning rate:** 2e-4 with cosine scheduler
+- **Effective batch size:** 8 (batch=2, grad_accum=4)
+- **Final training loss:** 0.4296
+- **Final eval loss:** 0.7495
+- **Loss type:** Completion-only (only trains on assistant response tokens)
+- **LoRA targets:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+
+## Evaluation Results (258 held-out examples)
+
+| Metric | Fine-tuned | Baseline (untuned 3B) |
+|--------|------------|----------------------|
+| Word count avg | 36.4 | 33.9 |
+| In 25-45 word range | 69.0% | 91.9% |
+| Endorsement boilerplate leak | **10.1%** | 18.6% |
+| Format compliance | 100% | 100% |
+
+Key win: the fine-tuned model filters endorsement boilerplate significantly better (10% vs 19% leak rate).
+
+## Privacy Note
+
+All training data was PII-obfuscated before use. Names, locations, schools, wards, and missions are replaced with `[[Name]]`, `[[City]]`, etc. The model has never seen real PII during training.
+
+## Limitations
+
+- Trained on a specific style of ecclesiastical leader comments. May not generalize to other summarization tasks without additional training.
+- Endorsement leak rate is 10% — some boilerplate still passes through.
+- Word count compliance (69% in 25-45 range) is lower than the untuned model (92%), though this is a tradeoff for better filtering.
+
+## Source Code
+
+Training scripts and data pipeline:
+[github.com/rkevan/AI-Experiments](https://github.com/rkevan/AI-Experiments)
+
+## Citation
+
+```bibtex
+@misc{leader-comment-summarizer-2026,
+  title={leader-comment-summarizer: Fine-tuned Llama 3.2 3B for Ecclesiastical Comment Summarization},
+  author={Robert Kevan},
+  year={2026},
+  url={https://huggingface.co/rkevan/leader-comment-summarizer}
+}
+```