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README.md

@@ -2,47 +2,82 @@
 license: other
 pipeline_tag: text-generation
 tags:
-- qwen
-- qwen2
-- 8b
-- lora
+- text-generation
+- coding
+- korean
 - vllm
 - open-webui
-- korean
-- coding
+- local-llm
+- lora
+- qwen
+- 8b
+language:
+- ko
+- en
 ---
 
 # 8bcustom-model
 
-This is the public model/runtime release for the DGX AI Factory stable coding assistant.
+**8bcustom-model** is an 8B-class local coding assistant model/runtime release built for Korean developers who need practical help with Linux, Docker, vLLM, Open-WebUI, CUDA, JSONL datasets, and LoRA workflows.
 
-## Correction notice
+This repository is part of a DGX AI Factory-style local LLM deployment project: data preparation, LoRA repair, model merge, vLLM serving, Open-WebUI integration, systemd autostart, benchmarking, and Hugging Face release packaging.
 
-The previous public repository name used `7bcustom-model`, but the checked model size/name should be treated as **8B-class** for this public title. This repository uses the corrected title:
+## What this model is for
 
-```text
-8bcustom-model
-```
+This model is designed as a practical development assistant for:
 
-## Model
+- Linux command troubleshooting
+- Docker and service deployment
+- vLLM OpenAI-compatible serving
+- Open-WebUI connection setup
+- CUDA/PyTorch environment checks
+- JSONL dataset validation
+- LoRA training and repair workflows
+- Korean step-by-step developer support
 
-- Public name: `8bcustom-model`
-- Runtime served name: `dgx-stable-current`
-- Model class: 8B-class local custom coding assistant
-- Runtime: vLLM OpenAI-compatible API
-- Open-WebUI compatible: yes
+The target behavior is direct, procedural, and operational: diagnose the problem, provide exact commands, and explain the result clearly in Korean honorific style.
 
-## Deployment benchmark
+## Validated local runtime
 
-```text
-average_score: 97.75
-pass_70_plus: 20/20
-strong_85_plus: 20/20
-critical_fail_count: 0
-decision: DEPLOY_CANDIDATE
-```
+The model was validated in a local production-style runtime:
+
+| Component | Status |
+|---|---|
+| vLLM OpenAI-compatible API | Working |
+| Open-WebUI integration | Working |
+| systemd autostart | Working |
+| Local model name | `dgx-stable-current` |
+| Public release name | `8bcustom-model` |
+| Hugging Face public repo | `koreallmdev/8bcustom-model` |
+
+## Benchmark summary
+
+The final deployment benchmark used a router/template runtime hardening layer for operational reliability.
+
+| Metric | Result |
+|---|---:|
+| Average score | 97.75 |
+| Pass ≥ 70 | 20 / 20 |
+| Strong ≥ 85 | 20 / 20 |
+| Critical failures | 0 |
+| Decision | DEPLOY_CANDIDATE |
+
+The benchmark focused on practical developer operations such as Linux, Docker, CUDA checks, vLLM serving, JSONL validation, FastAPI, systemd troubleshooting, LoRA policy, and Korean response quality.
+
+## Runtime policy
+
+For production usage, the local deployment uses a hybrid approach:
+
+- General coding questions: model generation
+- Linux/vLLM/CUDA/systemd known operational routes: guarded templates
+- LoRA/stable/rejected model policy: fixed policy templates
+- CJK leakage and style regressions: post-check and route hardening
 
-## vLLM example
+This approach keeps the model useful for open-ended coding while making high-risk operational answers more deterministic.
+
+## Quick start with vLLM
+
+After downloading the model files, you can serve the model with vLLM:
 
 ```bash
 python -m vllm.entrypoints.openai.api_server \
@@ -56,23 +91,139 @@ python -m vllm.entrypoints.openai.api_server \
   --max-num-seqs 8
 ```
 
-## Open-WebUI
+Check the model endpoint:
+
+```bash
+curl http://127.0.0.1:8000/v1/models
+```
+
+Send a test request:
+
+```bash
+curl http://127.0.0.1:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "8bcustom-model",
+    "messages": [
+      {
+        "role": "user",
+        "content": "Docker 컨테이너가 실행 중인지 확인하는 명령어를 알려주세요."
+      }
+    ],
+    "temperature": 0.2,
+    "max_tokens": 300
+  }'
+```
+
+## Open-WebUI connection
+
+For Open-WebUI, add an OpenAI-compatible API connection.
+
+If Open-WebUI runs in Docker:
 
 ```text
-Base URL: http://<host>:8000/v1
-Model   : 8bcustom-model
+Base URL: http://host.docker.internal:8000/v1
 API Key : dummy
+Model   : 8bcustom-model
 ```
 
-## Runtime policy
+If you use the local deployment name from the original DGX runtime:
+
+```text
+Model: dgx-stable-current
+```
+
+If `host.docker.internal` does not work in your Docker environment, try:
+
+```text
+Base URL: http://172.17.0.1:8000/v1
+```
+
+## Example prompts
+
+Korean developer support:
+
+```text
+Ubuntu에서 8000 포트를 사용 중인 프로세스를 확인하고 종료하는 절차를 알려주세요.
+```
+
+vLLM troubleshooting:
+
+```text
+vLLM 서버가 Open-WebUI에 모델을 표시하지 못할 때 확인해야 할 순서를 알려주세요.
+```
+
+LoRA workflow:
+
+```text
+LoRA adapter를 merge한 뒤 vLLM에서 서빙하기 전 확인해야 할 파일 목록을 알려주세요.
+```
+
+Dataset validation:
+
+```text
+JSONL 학습 데이터에서 깨진 JSON과 중복 instruction을 검사하는 Python 스크립트를 만들어주세요.
+```
+
+## Intended use
+
+This release is intended for:
+
+- Local developer assistants
+- On-premise coding assistant experiments
+- vLLM/Open-WebUI deployment practice
+- Korean-language coding support
+- LoRA and dataset pipeline testing
+
+## Out-of-scope use
+
+This model is not intended to be treated as a fully audited security, legal, medical, or financial advisor. Operational outputs should be reviewed before applying them to production systems.
+
+## Deployment notes
+
+The original local deployment used:
+
+```text
+Local served model name: dgx-stable-current
+Open-WebUI URL        : http://127.0.0.1:3000
+vLLM URL              : http://127.0.0.1:8000/v1
+Open-WebUI Base URL   : http://host.docker.internal:8000/v1
+```
+
+The public release name is:
+
+```text
+8bcustom-model
+```
+
+## Project highlights
+
+This project demonstrates an end-to-end local LLM workflow:
+
+1. Dataset filtering and repair
+2. LoRA candidate testing
+3. Regression rejection
+4. Stable adapter preservation
+5. Model merge for vLLM
+6. Open-WebUI integration
+7. systemd autostart
+8. Private backup upload
+9. Public Hugging Face release
+10. Runtime route/template hardening
+
+## Collaboration
+
+This repository can be used as a portfolio reference for:
 
-The local production runtime used router/template safeguards for deterministic operational answers:
+- Local LLM deployment
+- vLLM serving
+- Open-WebUI integration
+- Korean coding assistant customization
+- LoRA fine-tuning and repair workflows
+- On-premise AI assistant setup
 
-- Linux guarded prompt
-- vLLM medium prompt
-- CUDA check template
-- LoRA/stable/rejected policy template
+For collaboration, please contact through the Hugging Face profile associated with this repository.
 
-## Notes
+## Disclaimer
 
-This public repository is a model/runtime release record. Private preservation archives and local absolute-path operational records are not required for public usage.
+This is an experimental local LLM deployment release. Validate outputs before use in production environments.