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Model: intrect/VELA
Source: Original Platform

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+vela-dpo-v4-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
+vela-dpo-v6-q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+language:
+  - ko
+  - en
+library_name: transformers
+tags:
+  - finance
+  - korean
+  - stock-analysis
+  - reasoning
+  - dpo
+  - gguf
+  - llama-cpp
+  - llama-cpp-python
+  - mlx
+  - apple-silicon
+  - 4bit
+  - quantized
+  - vllm
+  - ollama
+base_model: Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+---
+
+# VELA (Vector-Encoded Learning Agent)
+
+[![GitHub](https://img.shields.io/badge/Framework-GitHub-181717?logo=github)](https://github.com/Intrect-io/vela-framework)
+[![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Model-intrect%2FVELA-yellow)](https://huggingface.co/intrect/VELA)
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Demo-Spaces-blue)](https://huggingface.co/spaces/intrect/vela-demo)
+
+**한국 주식시장 전문 AI 애널리스트**
+
+VELA는 한국 주식시장 뉴스 분석 및 투자 리서치를 위해 특화된 7B 파라미터 언어 모델입니다.
+KOSPI/KOSDAQ 2,135개 종목에 대한 뉴스 영향 분석, 증권사 리포트 해석,
+Reasoning Trace 기반 구조화된 투자 분석을 수행합니다.
+
+> **58K+ SFT 샘플**과 **26K+ DPO 페어**로 학습하여,
+> 한국어 금융 도메인에서 정확하고 구조화된 분석을 제공합니다.
+>
+> **Agent Framework**: [github.com/Intrect-io/vela-framework](https://github.com/Intrect-io/vela-framework) — `pip install vela-framework`
+
+---
+
+## Model Details
+
+| 항목 | 내용 |
+|------|------|
+| **Base Model** | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
+| **Training** | SFT (58,206 samples) + DPO (26,421 pairs) |
+| **Parameters** | 7.6B |
+| **Context Length** | 8,192 tokens |
+| **RT Format** | Markdown Reasoning Trace |
+| **Stock Coverage** | 2,135 종목 (KOSPI + KOSDAQ) |
+| **License** | Apache 2.0 |
+
+### Available Formats
+
+| Format | File | Size | Use Case |
+|--------|------|------|----------|
+| **BF16** (safetensors) | [`model-*.safetensors`](https://huggingface.co/intrect/VELA/tree/main) | 14.5 GB | Full precision, GPU inference |
+| **GGUF Q4_K_M** | [`vela-dpo-v6-q4_k_m.gguf`](https://huggingface.co/intrect/VELA/blob/main/vela-dpo-v6-q4_k_m.gguf) | 4.4 GB | llama.cpp / Ollama / LM Studio |
+| **MLX 4-bit** | [`mlx-int4/`](https://huggingface.co/intrect/VELA/tree/main/mlx-int4) | 4.0 GB | Apple Silicon (M1/M2/M3/M4) |
+
+---
+
+## What Can VELA Do?
+
+### 1. 뉴스 영향 분석
+주식 관련 뉴스가 주가에 미치는 영향을 단계적으로 추론합니다.
+
+### 2. Reasoning Trace (단계별 사고 과정)
+분석 과정을 투명하게 보여주는 Markdown Reasoning Trace를 생성합니다:
+
+```
+**Step 1**:
+**Thought**: 삼성전자 3나노 양산 성공 뉴스의 기술적 의미를 파악해야 합니다.
+**Action**: search
+**Query**: 삼성전자 3나노 파운드리 수율 경쟁력 TSMC
+**Confidence**: 35%
+
+**Step 2**:
+**Thought**: TSMC 대비 수율 격차가 핵심 변수이며, 양산 성공은 기술력 입증이나 수율 안정화까지 시간 필요.
+**Action**: analyze
+**Confidence**: 65%
+
+**Step 3**:
+**Thought**: 3나노 양산 성공은 중장기 긍정 시그널이나, 단기적으로 수율 이슈 리스크가 존재합니다.
+**Action**: conclude
+**Confidence**: 80%
+```
+
+### 3. 증권사 리포트 해석
+애널리스트 리포트를 기반으로 핵심 포인트와 투자 시사점을 도출합니다.
+
+### 4. 투자 리서치 리포트
+7개 섹션으로 구조화된 투자 분석 보고서를 생성합니다:
+Executive Summary / Key Metrics / 시장 동향 / 수급 분석 / 뉴스 영향 / 리스크 / 투자 의견
+
+### 5. 도구 호출 (Tool Calling)
+Search, Price, Investor 등 외부 도구와 연동하는 분석을 수행합니다.
+
+---
+
+## Training Pipeline
+
+```
+Qwen/Qwen2.5-7B-Instruct
+        |
+        v
+   SFT (58,206 samples)
+   ├── 뉴스 분류 분석      10,830  (18.6%)
+   ├── 극단 시그널 분석      9,603  (16.5%)
+   ├── 증권사 리포트 (GPT-4o) 5,117   (8.8%)
+   ├── 뉴스 영향 분석        4,839   (8.3%)
+   ├── 멀티턴 대화           8,000  (13.8%)
+   ├── Gap Fill (12 카테고리) 12,635  (21.7%)
+   │   ├── 밸류에이션 분석    2,000
+   │   ├── 수급/리스크/EOD    3,000
+   │   ├── 거절/유보 응답     1,000
+   │   ├── 간결 분석          1,000
+   │   ├── 심층 추론 (5+ steps) 1,000
+   │   └── 기타 (매크로, 섹터 등) 4,635
+   └── 기타                  7,182  (12.3%)
+        |
+        v
+   DPO (26,421 pairs)
+   ├── 중복 제거 기본 페어   12,000  (45.4%)
+   ├── 다국어 leak 보강      5,997  (22.7%)
+   ├── VELA ChatML 정렬      5,000  (18.9%)
+   ├── 불충분 분석 교정      1,642   (6.2%)
+   ├── 중국어 leak 교정 v2   1,216   (4.6%)
+   └── Reasoning Trace 정렬    566   (2.1%)
+        |
+        v
+      VELA v1.3
+```
+
+---
+
+## Training Data Details
+
+### SFT v3 (58,206 samples)
+
+| Source | Samples | Ratio | Description |
+|--------|---------|-------|-------------|
+| `classified_news` | 10,830 | 18.6% | GPT-4o 분류된 뉴스 Reasoning Trace |
+| `extreme_signals` | 9,603 | 16.5% | 급등/급락 시그널 뉴스 분석 |
+| `securities_report_gpt4o` | 5,117 | 8.8% | 증권사 리포트 GPT-4o 재구성 |
+| `analysis_news` | 4,839 | 8.3% | 일반 뉴스 영향 분석 |
+| `multi_turn_2t` | 4,000 | 6.9% | 단일 턴 다양 종목 분석 |
+| `multi_turn_4t` | 4,000 | 6.9% | 2턴 follow-up 대화 |
+| **`valuation`** | **2,000** | **3.4%** | 밸류에이션 분석 (v3 Gap Fill) |
+| `tool_calling` | 1,965 | 3.4% | Search/Price/Investor 도구 호출 |
+| **`supply_demand_ext`** | **1,000** | **1.7%** | 수급 확장 분석 (v3) |
+| **`risk`** | **1,000** | **1.7%** | 리스크 분석 (v3) |
+| **`eod_report`** | **1,000** | **1.7%** | EOD 시황 리포트 (v3) |
+| **`refusal`** | **1,000** | **1.7%** | 거절/유보 응답 (v3) |
+| **`short_analysis`** | **1,000** | **1.7%** | 간결 분석 <500자 (v3) |
+| **`deep_reasoning`** | **1,000** | **1.7%** | 심층 추론 5+ steps (v3) |
+| **`low_confidence`** | **1,000** | **1.7%** | 저확신도 분석 (v3) |
+| **`macro_impact_ext`** | **1,000** | **1.7%** | 거시경제 확장 (v3) |
+| **`sector_theme`** | **1,000** | **1.7%** | 섹터/테마 분석 (v3) |
+| `multi_stock_comparison` | 981 | 1.7% | 복수 종목 비교 분석 |
+| `earnings_impact` | 971 | 1.7% | 실적 발표 영향 분석 |
+| `risk_alert` | 948 | 1.6% | 리스크 경고 분석 |
+| **`null_impact`** | **900** | **1.5%** | 주가 무영향 응답 (v3) |
+| Other | 2,050 | 3.5% | batch5 fallback, 기존 수급/섹터/매크로 |
+
+> v3에서 12개 카테고리 12,635개 샘플을 추가하여 데이터 갭을 보강했습니다.
+> 생성 비용: Perplexity Sonar 2K ($4.60) + OpenAI gpt-4o-mini Batch API 10.6K ($2.76) = **~$7.36**
+
+### DPO v2 (26,421 pairs)
+
+| Source | Pairs | Ratio | Rejection Type |
+|--------|-------|-------|----------------|
+| `dpo_dedup` | 12,000 | 45.4% | 짧은/저품질 응답 vs 상세 분석 |
+| `multilingual_aug` | 5,997 | 22.7% | 중국어/영어 leak, 짧은 응답, 저확신 |
+| `vela_chatml` | 5,000 | 18.9% | 형식 오류, 짧은 응답 |
+| `batch5_insuf_dpo` | 1,642 | 6.2% | 불충분 분석 품질 교정 |
+| `chinese_leak_v2` | 1,216 | 4.6% | 중국어 문자 leak 집중 교정 |
+| `reasoning_trace_2k` | 566 | 2.1% | 영어 leak, RT 형식 오류 |
+
+### Data Version History
+
+| Version | SFT Samples | DPO Pairs | Changes |
+|---------|-------------|-----------|---------|
+| v1.0 | 36,713 | 24,779 | 초기 학습 데이터 (JSON RT) |
+| v1.1 | 36,713 | 24,779 | RT JSON → Markdown 변환 |
+| v2.0 | 45,571 | 26,421 | +멀티턴 8K, +batch5 불충분 DPO |
+| **v3.0** | **58,206** | **26,421** | **+Gap Fill 12개 카테고리 12,635** |
+
+---
+
+## Benchmarks
+
+### 한국어 LLM 벤치마크 (KMMLU + HAE-RAE)
+
+모든 모델 **Q4_K_M 양자화**, **0-shot** 조건으로 평가. `lm-evaluation-harness` v0.4.9 + `llama.cpp`, Apple M1 Max 32GB 환경.
+
+#### KMMLU (한국어 MMLU, 10과목)
+
+| 과목 | VELA DPO v6 | Qwen2.5-7B-Instruct | EXAONE-3.5-7.8B |
+|------|:-----------:|:--------------------:|:---------------:|
+| 마케팅 | **75.7** | 72.5 | 75.6 |
+| 컴퓨터과학 | **73.7** | 69.7 | 69.7 |
+| 경영학 | 54.0 | 55.2 | **57.3** |
+| 정치사회학 | 49.0 | 49.3 | **56.0** |
+| 경제학 | 45.4 | 47.7 | **51.5** |
+| 법학 | 43.4 | 46.1 | **49.9** |
+| 심리학 | 39.2 | 39.3 | **45.7** |
+| 회계 | 38.0 | 33.0 | **42.0** |
+| 수학 | **33.0** | **33.7** | 27.7 |
+| 한국사 | **31.0** | 29.0 | 22.0 |
+| **평균** | **48.2** | **47.6** | **49.7** |
+
+#### HAE-RAE Bench (한국어 특화)
+
+| 영역 | VELA DPO v6 | Qwen2.5-7B-Instruct | EXAONE-3.5-7.8B |
+|------|:-----------:|:--------------------:|:---------------:|
+| 희귀어 | 69.9 | 68.4 | **78.8** |
+| 표준명칭 | 64.7 | 66.0 | **71.9** |
+| 외래어 | 48.5 | 57.4 | **81.1** |
+| 한국사 | 45.7 | 42.6 | **77.7** |
+| 일반상식 | **44.3** | 42.1 | 44.3 |
+| **평균** | **54.5** | **55.3** | **70.7** |
+
+#### 주요 발견
+
+- **Catastrophic forgetting 없음**: 도메인 특화 fine-tuning 후에도 베이스 모델(Qwen2.5) 능력 유지 (KMMLU 평균 48.2% vs 47.6%)
+- **도메인 전이 효과**: 금융 관련 과목에서 베이스 모델 대비 향상 — 마케팅(+3.2%), 컴퓨터과학(+4.0%), 회계(+5.0%)
+- **한국어 네이티브 모델과 경쟁**: 대규모 한국어 코퍼스로 사전학습된 EXAONE-3.5-7.8B (LG AI Research) 대비 KMMLU 10과목 중 4개에서 우위
+
+### 양자화 벤치마크 (GGUF)
+
+RTX 3060 12GB, llama-cpp-python, `n_gpu_layers=-1`, `n_ctx=4096`
+
+| 포맷 | 속도 | 중국어 Leak | 품질 |
+|------|------|-------------|------|
+| **Q4_K_M (v6)** | **36 tok/s** | 0/5 클린 | RT + 리포트 정상 |
+
+> 스트레스 테스트 5회: Synthesis + 3K Reasoning Trace 교대 — 전 구간 **중국어 leak 제로**
+
+### MLX 벤치마크 (Apple Silicon)
+
+M1 Max 32GB, MLX 4-bit 양자화
+
+| 구성 | 양자화 | 로딩 시간 | 추론 속도 | 메모리 |
+|------|--------|----------|----------|--------|
+| **MLX 4-bit** | 4-bit (4.5 bpw) | 0.59초 | **15.93 tok/s** | 4.4 GB |
+| PyTorch (CPU) | BF16 | 0.10초 | 4.93 tok/s | 0.3 GB |
+| PyTorch + LoRA (CPU) | BF16 | 1.64초 | 4.22 tok/s | 14.1 GB |
+
+MLX 4-bit vs PyTorch CPU:
+- 추론 속도 **3.2배** (15.93 vs 4.93 tok/s)
+- 모델 크기 **73% 감소** (4 GB vs 15 GB)
+- 메모리 **68% 절약** (4.4 vs 14.1 GB)
+
+### DPO 학습 품질 개선
+
+| 지표 | DPO 전 | DPO 후 |
+|------|--------|--------|
+| 중국어 leak | 빈번 | **0/10 클린** |
+| 영어 leak | 간헐적 | 최소화 |
+| RT 형식 준수율 | ~80% | **~98%** |
+| 한국어 유창성 | 양호 | **우수** |
+---
+
+## Usage
+
+### llama-cpp-python (Recommended for GGUF)
+
+```python
+from llama_cpp import Llama
+
+model = Llama(
+    model_path="vela-dpo-v6-q4_k_m.gguf",
+    n_ctx=4096,
+    n_gpu_layers=-1,
+    chat_format="chatml",
+)
+
+response = model.create_chat_completion(
+    messages=[
+        {"role": "system", "content": "당신은 한국 주식 전문 애널리스트입니다."},
+        {"role": "user", "content": "삼성전자 HBM 사업 전망을 분석해주세요."},
+    ],
+    max_tokens=1024,
+    temperature=0.7,
+)
+print(response["choices"][0]["message"]["content"])
+```
+
+### Transformers (BF16)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "intrect/VELA",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("intrect/VELA")
+
+messages = [
+    {"role": "system", "content": "당신은 한국 주식 전문 애널리스트입니다."},
+    {"role": "user", "content": "삼성전자 HBM 사업 전망을 분석해주세요."},
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    temperature=0.7,
+    do_sample=True,
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### vLLM
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="intrect/VELA", dtype="bfloat16")
+params = SamplingParams(temperature=0.7, max_tokens=1024)
+
+outputs = llm.generate(
+    ["삼성전자 HBM 시장 전망을 분석해주세요."],
+    params,
+)
+print(outputs[0].outputs[0].text)
+```
+
+### MLX (Apple Silicon)
+
+```python
+from mlx_lm import load, generate
+
+# HF에서 mlx-int4 폴더만 다운로드
+from huggingface_hub import snapshot_download
+mlx_path = snapshot_download("intrect/VELA", allow_patterns="mlx-int4/*")
+model, tokenizer = load(f"{mlx_path}/mlx-int4")
+
+response = generate(
+    model,
+    tokenizer,
+    prompt="삼성전자 3나노 양산 뉴스 분석",
+    max_tokens=1024,
+)
+print(response)
+```
+
+### Ollama
+
+```bash
+# Modelfile
+FROM ./vela-q4_k_m.gguf
+TEMPLATE """<|im_start|>system
+{{ .System }}<|im_end|>
+<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+"""
+PARAMETER temperature 0.7
+PARAMETER num_ctx 4096
+```
+
+---
+
+## Output Format
+
+VELA는 두 가지 출력 모드를 지원합니다.
+
+### 1. Reasoning Trace (분석 과정)
+
+Markdown 형식으로 단계별 사고 과정을 투명하게 보여줍니다:
+
+```markdown
+**Step 1**:
+**Thought**: 삼성전자 HBM3E 12단 양산 관련 뉴스를 확인합니다. 수주 현황과 시장 점유율 파악이 필요합니다.
+**Action**: search
+**Query**: 삼성전자 HBM3E 12단 수주 시장점유율
+**Confidence**: 45%
+
+**Step 2**:
+**Thought**: SK하이닉스 대비 삼성전자의 HBM 시장 점유율 확대 추세를 분석합니다.
+**Action**: analyze
+**Confidence**: 70%
+
+**Step 3**:
+**Thought**: HBM3E 양산 성공은 긍정적이나, NVIDIA 인증 지연 리스크가 존재합니다.
+**Action**: conclude
+**Confidence**: 82%
+```
+
+### 2. Synthesis Report (최종 리포트)
+
+7개 섹션으로 구조화된 투자 분석 보고서:
+
+```markdown
+# 분석 리포트: 삼성전자 (005930.KS)
+
+## Executive Summary
+[2-3문장 핵심 요약]
+
+## Key Metrics
+| 지표 | 수치 |
+|------|------|
+| 현재가 | 181,200원 |
+| 12M FWD PER | 15.4배 |
+| PBR | 3.13배 |
+
+## 시장 동향 분석
+## 수급 분석
+## 뉴스 영향 분석
+## 리스크 요인
+## 투자 의견
+```
+
+---
+
+## Example Output
+
+> 아래는 VELA 모델이 실제로 생성하는 응답 예시입니다 (SFT 학습 데이터 기반).
+>
+> **Query**: "LGU+ 오픈랜 상용망 구축 뉴스가 CS(065770) 주가에 미치는 영향을 분석해주세요."
+
+<details>
+<summary><b>Reasoning Trace (추론 과정) 펼치기</b></summary>
+
+```
+**Step 1**:
+**Thought**: CS는 통신장비 제조업체로, LGU+ 오픈랜 상용망 구축 뉴스는
+직접적 수혜 가능성이 있다. 뉴스에서 언급된 '삼지전자·노키아'와의 협력 관계,
+기술 실증 의미를 파악해야 한다. CS의 오픈랜 관련 포지셔닝 확인 필요.
+**Action**: search
+**Query**: CS 065770 오픈랜 5G 통신장비 사업
+**Confidence**: 40%
+
+**Step 2**:
+**Thought**: 뉴스 본문에서 '삼지전자·노키아'가 장비 구축을 담당했다고 명시.
+CS가 이 프로젝트에 직접 참여했는지, 아니면 간접 수혜인지 구분이 필수.
+LGU+ 오픈랜 실증 프로젝트의 규모와 향후 상용화 일정도 중요.
+**Action**: search
+**Query**: LGU+ 오픈랜 실증단지 삼지전자 CS 참여
+**Confidence**: 50%
+
+**Step 3**:
+**Thought**: 뉴스에서 CS의 직접 언급이 없으므로, CS가 오픈랜 밸류체인 내 어느
+위치에 있는지 불명확. 다만 통신장비 제조사로서 오픈랜 시장 확대는 장기 성장 기회.
+실제 1일/3일 수익률이 각각 +0.73%, +0.87%로 완만한 상승이므로,
+뉴스 직접 영향보다는 섹터 긍정 심리 반영으로 해석.
+**Action**: analyze
+**Confidence**: 65%
+```
+
+</details>
+
+<details>
+<summary><b>Quick Assessment (빠른 판정) 펼치기</b></summary>
+
+```json
+{
+  "category": "product_innovation",
+  "sentiment": "bullish",
+  "impact": "medium",
+  "confidence": 0.68,
+  "reasoning": "LGU+ 오픈랜 상용망 구축은 국내 통신장비 산업의 기술 고도화 신호이나,
+    뉴스에서 CS의 직접 참여가 명시되지 않음. 간접 수혜 가능성과 섹터 긍정 심리로
+    인한 완만한 상승으로 평가."
+}
+```
+
+</details>
+
+<details open>
+<summary><b>Analysis Report (상세 리포트)</b></summary>
+
+### 1. Executive Summary
+
+LG유플러스의 오픈랜 상용망 구축은 국내 통신 인프라의 5G-6G 기술 고도화를 시사하는 긍정적 신호입니다.
+뉴스에서는 삼지전자와 노키아의 장비 구축을 명시했으나, CS의 직접 참여 여부는 공개되지 않았습니다.
+다만 CS는 통신장비 제조업체로서 국내 오픈랜 시장 확대의 장기 수혜자가 될 가능성이 있습니다.
+실제 주가 반응(1일 +0.73%, 3일 +0.87%)은 제한적이며, 이는 뉴스가 개별 종목보다는
+섹터 전체에 대한 긍정 심리를 반영한 것으로 해석됩니다. **종합 신뢰도: 0.68 (MEDIUM)**
+
+### 2. Key Metrics
+
+| 지표 | 수치 | 평가 |
+|------|------|------|
+| 1일 수익률 | +0.73% | 약한 상승 |
+| 3일 수익률 | +0.87% | 지속적 약세 상승 |
+| 뉴스 영향력 | Medium | 섹터 영향 > 개별 종목 영향 |
+| 직접 언급 | 없음 | CS 미언급 |
+| 기술 카테고리 | 오픈랜(Open RAN) | 5G-6G 핵심 기술 |
+
+### 3. 시장 동향 분석
+
+오픈랜은 기존 폐쇄형 통신장비 시스템을 개방형 표준으로 전환하는 구조적 변화입니다.
+LGU+의 금오공과대학교 캠퍼스 구축은 한국지능정보사회진흥원(NIA)과 과학기술정보통신부 주도의
+국가 실증 프로젝트로, 정부 차원의 오픈랜 생태계 조성 의지를 보여줍니다.
+
+"기존 5G 네트워크와 동등한 수준의 서비스"를 제공한다고 명시한 것은 국내 오픈랜 기술이
+글로벌 수준에 도달했음을 의미합니다. 이는 국내 통신장비 산업 전반에 긍정적 신호이며,
+CS와 같은 관련 업체들의 기술 고도화 필요성을 강조합니다.
+
+### 4. 리스크 요인
+
+- CS의 오픈랜 프로젝트 직접 참여 여부 불확실
+- 삼지전자·노키아 등 경쟁사 대비 기술 포지셔닝 미확인
+- 오픈랜 상용화 일정 및 수주 가시성 부족
+
+### 5. 투자 의견
+
+오픈랜 시장 확대는 **중장기 긍정 요인**이나, CS의 직접적 수혜 경로가 확인되기 전까지
+**관망** 포지션이 적절합니다. 향후 CS의 오픈랜 관련 수주 공시나 기술 제휴 발표 시
+재평가가 필요합니다.
+
+</details>
+
+---
+
+## Architecture
+
+VELA는 단독 LLM으로도 동작하지만, 에이전트 시스템과 결합하면 실시간 데이터 기반 분석이 가능합니다:
+
+```
+[사용자 쿼리]
+      |
+      v
+[멀티소스 검색] ─── DuckDuckGo (최신 뉴스)
+      |          ├── 한국투자증권 KIS (현재가, PER, PBR, EPS, 수급)
+      |          ├── FnGuide (사업개요, 재무정보)
+      |          └── FAISS 317K (과거 유사 뉴스 + 주가 반응)
+      v
+[컨텍스트 주입] → System Prompt + 검색 결과
+      |
+      v
+[VELA LLM] → Reasoning Trace (Step-by-Step)
+      |
+      v
+[리서치 리포트]
+```
+
+---
+
+## Limitations
+
+- **실시간 시세**: 모델 자체는 실시간 데이터에 접근하지 못합니다 (에이전트 시스템 필요)
+- **수치 할루시네이션**: 구체적 수치(주가, PER 등)는 외부 검증이 필요합니다
+- **컨텍스트**: 8K 토큰 제한으로 긴 문서 처리에 한계가 있습니다
+- **투자 조언 아님**: 정보 제공 목적이며, 투자 결정은 본인의 판단과 책임입니다
+
+---
+
+## Citation
+
+```bibtex
+@misc{vela2026,
+  title={VELA: Vector-Encoded Learning Agent for Korean Stock Analysis},
+  author={intrect},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/intrect/VELA}
+}
+```
+
+---
+
+## Version History
+
+| Version | Date | Changes |
+|---------|------|---------|
+| **v1.3** | **2026-03-31** | **DPO v6 모델 업데이트 (Unsloth 학습, SFT v6 + DPO v6 merged)** |
+| v1.2 | 2026-02-16 | SFT v3 (58K) Gap Fill 12카테고리, Markdown RT, 벤치마크 추가 |
+| v1.1 | 2026-02-12 | GGUF 양자화 모델 추가 (Q4_K_M, Q8_0) |
+| v1.0 | 2026-01-28 | DPO 병합, 중국어/영어 leak 해결 |
+| v0.9 | 2026-01-15 | SFT 베이스 모델 공개 |
+
+---
+
+**Disclaimer**: 이 모델의 출력은 투자 조언이 아닙니다. 모든 투자 결정은 본인의 판단과 책임 하에 이루어져야 합니다.

added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

benchmarks/benchmark_comparison_20260401.json

@@ -0,0 +1,120 @@
+{
+  "benchmark_info": {
+    "date": "2026-04-01",
+    "framework": "lm-evaluation-harness 0.4.9.2",
+    "inference": "llama.cpp (llama-server b8330)",
+    "hardware": "Apple M1 Max 32GB",
+    "quantization": "Q4_K_M",
+    "n_shot": 0,
+    "tasks": "KMMLU direct (10 subjects) + HAE-RAE (5 subtasks)",
+    "method": "generate_until with regex extraction"
+  },
+  "models": {
+    "vela-dpo-v6": {
+      "full_name": "VELA DPO v6 (Qwen2.5-7B + SFT + DPO v6)",
+      "file": "vela-dpo-v6-q4km.gguf",
+      "size_gb": 4.4
+    },
+    "qwen2.5-7b-instruct": {
+      "full_name": "Qwen2.5-7B-Instruct (baseline)",
+      "file": "qwen2.5-7b-instruct-q4_k_m-00001-of-00002.gguf",
+      "size_gb": 4.4
+    },
+    "exaone-3.5-7.8b": {
+      "full_name": "EXAONE-3.5-7.8B-Instruct",
+      "file": "EXAONE-3.5-7.8B-Instruct-Q4_K_M.gguf",
+      "size_gb": 4.4
+    }
+  },
+  "kmmlu": {
+    "accounting": {
+      "vela_dpo_v6": 0.38,
+      "qwen25_7b": 0.33,
+      "exaone_35_7_8b": 0.42
+    },
+    "computer_science": {
+      "vela_dpo_v6": 0.737,
+      "qwen25_7b": 0.697,
+      "exaone_35_7_8b": 0.697
+    },
+    "economics": {
+      "vela_dpo_v6": 0.454,
+      "qwen25_7b": 0.477,
+      "exaone_35_7_8b": 0.515
+    },
+    "korean_history": {
+      "vela_dpo_v6": 0.31,
+      "qwen25_7b": 0.29,
+      "exaone_35_7_8b": 0.22
+    },
+    "law": {
+      "vela_dpo_v6": 0.434,
+      "qwen25_7b": 0.461,
+      "exaone_35_7_8b": 0.499
+    },
+    "management": {
+      "vela_dpo_v6": 0.54,
+      "qwen25_7b": 0.552,
+      "exaone_35_7_8b": 0.573
+    },
+    "marketing": {
+      "vela_dpo_v6": 0.757,
+      "qwen25_7b": 0.725,
+      "exaone_35_7_8b": 0.756
+    },
+    "math": {
+      "vela_dpo_v6": 0.33,
+      "qwen25_7b": 0.337,
+      "exaone_35_7_8b": 0.277
+    },
+    "political_science_and_sociology": {
+      "vela_dpo_v6": 0.49,
+      "qwen25_7b": 0.493,
+      "exaone_35_7_8b": 0.56
+    },
+    "psychology": {
+      "vela_dpo_v6": 0.392,
+      "qwen25_7b": 0.393,
+      "exaone_35_7_8b": 0.457
+    }
+  },
+  "haerae": {
+    "general_knowledge": {
+      "vela_dpo_v6": 0.4375,
+      "qwen25_7b": 0.4205,
+      "exaone_35_7_8b": 0.4432
+    },
+    "history": {
+      "vela_dpo_v6": 0.4574,
+      "qwen25_7b": 0.4255,
+      "exaone_35_7_8b": 0.7766
+    },
+    "loan_words": {
+      "vela_dpo_v6": 0.4852,
+      "qwen25_7b": 0.574,
+      "exaone_35_7_8b": 0.8107
+    },
+    "rare_words": {
+      "vela_dpo_v6": 0.6988,
+      "qwen25_7b": 0.684,
+      "exaone_35_7_8b": 0.7877
+    },
+    "standard_nomenclature": {
+      "vela_dpo_v6": 0.6471,
+      "qwen25_7b": 0.6601,
+      "exaone_35_7_8b": 0.719
+    }
+  },
+  "summary": {
+    "kmmlu_avg": {
+      "vela_dpo_v6": 0.482,
+      "qwen25_7b": 0.476,
+      "exaone_35_7_8b": 0.497
+    },
+    "haerae_avg": {
+      "vela_dpo_v6": 0.545,
+      "qwen25_7b": 0.553,
+      "exaone_35_7_8b": 0.707
+    }
+  }
+}

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- '당신은 한국 주식시장 전문 AI 애널리스트 VELA입니다. 뉴스 영향 분석, 투자 리서치, Reasoning Trace 기반 구조화된 분석을 수행합니다.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\n당신은 한국 주식시장 전문 AI 애널리스트 VELA입니다. 뉴스 영향 분석, 투자 리서치, Reasoning Trace 기반 구조화된 분석을 수행합니다.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,64 @@
+{
+  "_name_or_path": "/workspace/vela_training/output/vela-sft-v6-merged",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "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,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.47.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "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": "5.2.0"
+}

mlx-int4/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

mlx-int4/chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

mlx-int4/config.json

@@ -0,0 +1,73 @@
+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "dtype": "bfloat16",
+    "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,
+    "quantization": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine"
+    },
+    "quantization_config": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine"
+    },
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
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+        "rope_type": "default"
+    },
+    "rope_scaling": null,
+    "rope_theta": 10000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.47.0",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 152064
+}

mlx-int4/generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.2.0"
+}

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+    "model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
+    "model.norm.weight": "model-00003-of-00004.safetensors"
+  }
+}

special_tokens_map.json

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+{
+  "<|box_end|>": "<|box_end|>",
+  "<|box_start|>": "<|box_start|>",
+  "<|im_end|>": "<|im_end|>",
+  "<|im_start|>": "<|im_start|>",
+  "<|image_pad|>": "<|image_pad|>",
+  "<|object_ref_end|>": "<|object_ref_end|>",
+  "<|object_ref_start|>": "<|object_ref_start|>",
+  "<|quad_end|>": "<|quad_end|>",
+  "<|quad_start|>": "<|quad_start|>",
+  "<|video_pad|>": "<|video_pad|>",
+  "<|vision_end|>": "<|vision_end|>",
+  "<|vision_pad|>": "<|vision_pad|>",
+  "<|vision_start|>": "<|vision_start|>",
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

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+{
+  "<|box_end|>": "<|box_end|>",
+  "<|box_start|>": "<|box_start|>",
+  "<|im_end|>": "<|im_end|>",
+  "<|im_start|>": "<|im_start|>",
+  "<|image_pad|>": "<|image_pad|>",
+  "<|object_ref_end|>": "<|object_ref_end|>",
+  "<|object_ref_start|>": "<|object_ref_start|>",
+  "<|quad_end|>": "<|quad_end|>",
+  "<|quad_start|>": "<|quad_start|>",
+  "<|video_pad|>": "<|video_pad|>",
+  "<|vision_end|>": "<|vision_end|>",
+  "<|vision_pad|>": "<|vision_pad|>",
+  "<|vision_start|>": "<|vision_start|>",
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "backend": "tokenizers",
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- '당신은 한국 주식시장 전문 AI 애널리스트 VELA입니다. 뉴스 영향 분석, 투자 리서치, Reasoning Trace 기반 구조화된 분석을 수행합니다.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\n당신은 한국 주식시장 전문 AI 애널리스트 VELA입니다. 뉴스 영향 분석, 투자 리서치, Reasoning Trace 기반 구조화된 분석을 수행합니다.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {
+    "<|box_end|>": "<|box_end|>",
+    "<|box_start|>": "<|box_start|>",
+    "<|im_end|>": "<|im_end|>",
+    "<|im_start|>": "<|im_start|>",
+    "<|image_pad|>": "<|image_pad|>",
+    "<|object_ref_end|>": "<|object_ref_end|>",
+    "<|object_ref_start|>": "<|object_ref_start|>",
+    "<|quad_end|>": "<|quad_end|>",
+    "<|quad_start|>": "<|quad_start|>",
+    "<|video_pad|>": "<|video_pad|>",
+    "<|vision_end|>": "<|vision_end|>",
+    "<|vision_pad|>": "<|vision_pad|>",
+    "<|vision_start|>": "<|vision_start|>"
+  },
+  "is_local": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

vela-dpo-v6-q4_k_m.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4fe02ae656d093ab78018b25a8850ffe0014c4ab628a113f71d9df79ffab5e3
+size 4683073408