初始化项目，由ModelHub XC社区提供模型

Model: koreallmdev/8bcustom-model
Source: Original Platform

.gitattributes

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PUBLIC_RELEASE_STATUS.json

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+{
+  "model_name": "8bcustom-model",
+  "previous_public_name": "7bcustom-model",
+  "served_model_name": "dgx-stable-current",
+  "hf_repo_id": "koreallmdev/8bcustom-model",
+  "runtime": "vLLM OpenAI-compatible API",
+  "openwebui_compatible": true,
+  "model_class": "8B-class",
+  "benchmark_decision": "DEPLOY_CANDIDATE",
+  "stable_changed": false,
+  "corrected_title_at": "20260623_214256"
+}

README.md

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+---
+license: other
+pipeline_tag: text-generation
+tags:
+- text-generation
+- coding
+- korean
+- vllm
+- open-webui
+- local-llm
+- lora
+- qwen
+- 8b
+language:
+- ko
+- en
+---
+
+<!-- DGX_LEGACY_NOTICE_START -->
+
+# Legacy / Experimental Release
+
+This repository is kept public as part of the development portfolio.
+
+Latest production release:
+
+https://huggingface.co/koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf
+
+최신 공개 운영 모델은 아래 repo를 사용하세요.
+
+```text
+koreallmdev/qwen2-5-14b-korean-coding-assistant-gguf
+```
+
+<!-- DGX_LEGACY_NOTICE_END -->
+
+
+---
+
+# 8bcustom-model
+
+**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.
+
+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.
+
+## What this model is for
+
+This model is designed as a practical development assistant for:
+
+- 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
+
+The target behavior is direct, procedural, and operational: diagnose the problem, provide exact commands, and explain the result clearly in Korean honorific style.
+
+## Validated local runtime
+
+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
+
+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 \
+  --model ./ \
+  --served-model-name 8bcustom-model \
+  --dtype float16 \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --max-model-len 1536 \
+  --gpu-memory-utilization 0.50 \
+  --max-num-seqs 8
+```
+
+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.docker.internal:8000/v1
+API Key : dummy
+Model   : 8bcustom-model
+```
+
+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:
+
+- Local LLM deployment
+- vLLM serving
+- Open-WebUI integration
+- Korean coding assistant customization
+- LoRA fine-tuning and repair workflows
+- On-premise AI assistant setup
+
+For collaboration, please contact through the Hugging Face profile associated with this repository.
+
+## Disclaimer
+
+This is an experimental local LLM deployment release. Validate outputs before use in production environments.

chat_template.jinja

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+{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
+You are a helpful assistant.<|im_end|>
+' }}{% endif %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.12.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.12.1"
+}

merge_meta.json

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+{
+  "base_model": "Qwen/Qwen2-7B-Instruct",
+  "stable_adapter": "/home/saul9523/dgx_ai_factory/06_train/lora_quality_v5_643_repair",
+  "merged_model": "/home/saul9523/dgx_ai_factory/86_vllm_models/merged_current_stable_repair_20260623_190058",
+  "created_at": "20260623_190058",
+  "purpose": "vLLM standalone HF model from current_stable_repair",
+  "stable_changed": false
+}

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runtime_policy/LORA_STABLE_POLICY_TEMPLATE.txt

@@ -0,0 +1,25 @@
+LoRA adapter 학습 결과를 바로 stable로 바꾸면 안 됩니다.
+
+이유는 새 adapter가 일부 질문에서는 좋아 보여도, 다른 항목에서 회귀할 수 있기 때문입니다. 특히 한국어 존댓말, 명령어 형식, CJK 누출, 필수 키워드 포함 여부는 별도로 확인해야 합니다.
+
+검증 순서는 다음과 같습니다.
+
+1. smoke test를 먼저 실행합니다.
+   - 짧은 핵심 질문으로 기본 동작을 확인합니다.
+   - CJK 누출, 과도하게 짧은 답변, 필수 명령어 누락이 있으면 다음 단계로 가지 않습니다.
+
+2. smoke test를 통과한 adapter만 benchmark를 실행합니다.
+   - 기존 stable adapter와 같은 기준으로 비교합니다.
+   - 평균 점수만 보지 말고 pass_70_plus, strong_85_plus, CJK flags를 같이 봅니다.
+
+3. benchmark 결과가 기존 stable보다 확실히 좋아야 합니다.
+   - 기준을 통과하지 못하면 stable로 승격하지 않습니다.
+   - 애매한 결과는 hold candidate로 둡니다.
+
+4. 실패한 adapter는 rejected로 분리합니다.
+   - CJK 누출이나 점수 회귀가 있으면 rejected 처리합니다.
+   - rejected adapter에서 추가 학습을 이어가면 회귀가 누적될 수 있습니다.
+
+5. stable 승격은 마지막에만 합니다.
+   - smoke test와 benchmark를 모두 통과한 뒤에만 current_stable_repair를 변경합니다.
+   - 통과하지 못하면 기존 stable을 유지합니다.

runtime_policy/OPENWEBUI_BASE_SYSTEM_PROMPT.txt

@@ -0,0 +1,5 @@
+한국어 존댓말로 답변하는 코딩 개발 비서입니다.
+명령어와 설명을 구분해서 답변하세요.
+사용자가 터미널에 붙여넣을 명령어는 코드블록에 따로 넣으세요.
+로그와 명령어를 섞지 마세요.
+모르는 내용은 추측하지 말고 확인 명령어를 먼저 제시하세요.

runtime_policy/OPENWEBUI_LINUX_GUARDED_PROMPT.txt

@@ -0,0 +1,4 @@
+한국어 존댓말로 답변하세요.
+Linux 명령어는 ASCII 문자로만 작성하세요.
+답변은 짧은 설명과 명령어 코드블록으로 구성하세요.
+추상 경로가 필요하면 /home/saul9523/dgx_ai_factory 같은 실제 예시 경로를 사용하세요.

runtime_policy/OPENWEBUI_VLLM_MEDIUM_PROMPT.txt

@@ -0,0 +1,4 @@
+한국어 존댓말로 답변하는 vLLM 운영 비서입니다.
+vLLM 실행 예시는 python -m vllm.entrypoints.openai.api_server 형식을 우선 사용하세요.
+답변에는 실행 명령어 코드블록과 확인 방법을 함께 포함하세요.
+OpenAI 호환 API 확인에는 /v1/models 또는 /v1/chat/completions 확인 명령어를 사용하세요.

runtime_policy/RUNTIME_ROUTER_RULES.md

@@ -0,0 +1,95 @@
+# DGX Runtime Router Rules
+
+## 최종 통과 정책
+
+93단계에서 `router + template runtime` 방식이 통과했습니다.
+
+- passed: 6/6
+- decision: VLLM_API_ROUTER_TEMPLATE_SMOKE_PASS
+- model: dgx-stable-current
+- stable 변경: 없음
+
+## 라우팅 원칙
+
+일반 질문은 모델에게 자유 생성시켜도 됩니다.
+하지만 검증 정책처럼 정답이 정해진 항목은 모델에게 자유 생성시키지 말고 템플릿으로 응답합니다.
+
+## Route A: base prompt
+
+대상:
+- FastAPI
+- Docker
+- 일반 Python
+- 일반 오류 분석
+- 일반 파일/폴더 명령
+
+사용 파일:
+- OPENWEBUI_BASE_SYSTEM_PROMPT.txt
+
+## Route B: Linux guarded prompt
+
+대상:
+- df
+- du
+- ps
+- grep
+- ss
+- journalctl
+- systemctl
+- Ubuntu 디스크/프로세스/포트 확인
+
+사용 파일:
+- OPENWEBUI_LINUX_GUARDED_PROMPT.txt
+
+주의:
+- 추상 경로 대신 실제 예시 경로를 사용합니다.
+- 명령어는 ASCII로 작성합니다.
+
+## Route C: vLLM medium prompt
+
+대상:
+- vLLM 실행
+- OpenAI 호환 API
+- /v1/models
+- /v1/chat/completions
+- api_server
+- gpu-memory-utilization
+- max-model-len
+- max-num-seqs
+
+사용 파일:
+- OPENWEBUI_VLLM_MEDIUM_PROMPT.txt
+
+핵심:
+- `python -m vllm.entrypoints.openai.api_server` 형식을 우선 사용합니다.
+- 8000 포트 확인 명령을 포함합니다.
+
+## Route D: LoRA stable policy template
+
+대상 키워드:
+- LoRA
+- adapter
+- stable
+- current_stable_repair
+- smoke test
+- benchmark
+- rejected
+- hold candidate
+- 승격
+- 회귀
+- CJK 누출
+
+사용 파일:
+- LORA_STABLE_POLICY_TEMPLATE.txt
+
+핵심:
+- 이 항목은 모델 자유 생성 금지입니다.
+- 고정 템플릿을 반환합니다.
+- 이유: 모델이 LoRA 정의와 검증 절차를 잘못 생성하거나 CJK drift를 낼 수 있습니다.
+
+## 현재 운영 상태
+
+- production model served by vLLM: dgx-stable-current
+- merged model path source: 86_vllm_models/latest_merged_current_stable_repair_path.txt
+- stable adapter: 06_train/current_stable_repair
+- runtime policy: router_template_runtime

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:48f722bc04c884e2fe1525fdcd85a1293a8499b6e620c1ac7c083c49632305fb
+size 11418262

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}