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---
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license: apache-2.0
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language:
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- ko
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- en
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library_name: transformers
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tags:
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- finance
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- korean
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- stock-analysis
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- reasoning
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- dpo
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- gguf
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- llama-cpp
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- llama-cpp-python
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- mlx
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- apple-silicon
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- 4bit
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- quantized
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- vllm
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- ollama
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base_model: Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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---
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# VELA (Vector-Encoded Learning Agent)
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[](https://github.com/Intrect-io/vela-framework)
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[](https://huggingface.co/intrect/VELA)
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[](https://huggingface.co/spaces/intrect/vela-demo)
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**한국 주식시장 전문 AI 애널리스트**
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VELA는 한국 주식시장 뉴스 분석 및 투자 리서치를 위해 특화된 7B 파라미터 언어 모델입니다.
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KOSPI/KOSDAQ 2,135개 종목에 대한 뉴스 영향 분석, 증권사 리포트 해석,
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Reasoning Trace 기반 구조화된 투자 분석을 수행합니다.
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> **58K+ SFT 샘플**과 **26K+ DPO 페어**로 학습하여,
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> 한국어 금융 도메인에서 정확하고 구조화된 분석을 제공합니다.
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>
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> **Agent Framework**: [github.com/Intrect-io/vela-framework](https://github.com/Intrect-io/vela-framework) — `pip install vela-framework`
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---
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## Model Details
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| 항목 | 내용 |
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|------|------|
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| **Base Model** | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
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| **Training** | SFT (58,206 samples) + DPO (26,421 pairs) |
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| **Parameters** | 7.6B |
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| **Context Length** | 8,192 tokens |
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| **RT Format** | Markdown Reasoning Trace |
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| **Stock Coverage** | 2,135 종목 (KOSPI + KOSDAQ) |
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| **License** | Apache 2.0 |
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### Available Formats
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| Format | File | Size | Use Case |
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|--------|------|------|----------|
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| **BF16** (safetensors) | [`model-*.safetensors`](https://huggingface.co/intrect/VELA/tree/main) | 14.5 GB | Full precision, GPU inference |
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| **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 |
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| **MLX 4-bit** | [`mlx-int4/`](https://huggingface.co/intrect/VELA/tree/main/mlx-int4) | 4.0 GB | Apple Silicon (M1/M2/M3/M4) |
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---
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## What Can VELA Do?
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### 1. 뉴스 영향 분석
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주식 관련 뉴스가 주가에 미치는 영향을 단계적으로 추론합니다.
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### 2. Reasoning Trace (단계별 사고 과정)
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분석 과정을 투명하게 보여주는 Markdown Reasoning Trace를 생성합니다:
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```
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**Step 1**:
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**Thought**: 삼성전자 3나노 양산 성공 뉴스의 기술적 의미를 파악해야 합니다.
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**Action**: search
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**Query**: 삼성전자 3나노 파운드리 수율 경쟁력 TSMC
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**Confidence**: 35%
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**Step 2**:
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**Thought**: TSMC 대비 수율 격차가 핵심 변수이며, 양산 성공은 기술력 입증이나 수율 안정화까지 시간 필요.
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**Action**: analyze
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**Confidence**: 65%
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**Step 3**:
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**Thought**: 3나노 양산 성공은 중장기 긍정 시그널이나, 단기적으로 수율 이슈 리스크가 존재합니다.
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**Action**: conclude
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**Confidence**: 80%
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```
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### 3. 증권사 리포트 해석
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애널리스트 리포트를 기반으로 핵심 포인트와 투자 시사점을 도출합니다.
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### 4. 투자 리서치 리포트
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7개 섹션으로 구조화된 투자 분석 보고서를 생성합니다:
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Executive Summary / Key Metrics / 시장 동향 / 수급 분석 / 뉴스 영향 / 리스크 / 투자 의견
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### 5. 도구 호출 (Tool Calling)
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Search, Price, Investor 등 외부 도구와 연동하는 분석을 수행합니다.
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---
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## Training Pipeline
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```
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Qwen/Qwen2.5-7B-Instruct
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v
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SFT (58,206 samples)
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├── 뉴스 분류 분석 10,830 (18.6%)
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├── 극단 시그널 분석 9,603 (16.5%)
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├── 증권사 리포트 (GPT-4o) 5,117 (8.8%)
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├── 뉴스 영향 분석 4,839 (8.3%)
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├── 멀티턴 대화 8,000 (13.8%)
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├── Gap Fill (12 카테고리) 12,635 (21.7%)
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│ ├── 밸류에이션 분석 2,000
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│ ├── 수급/리스크/EOD 3,000
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│ ├── 거절/유보 응답 1,000
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│ ├── 간결 분석 1,000
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│ ├── 심층 추론 (5+ steps) 1,000
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│ └── 기타 (매크로, 섹터 등) 4,635
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└── 기타 7,182 (12.3%)
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v
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DPO (26,421 pairs)
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├── 중복 제거 기본 페어 12,000 (45.4%)
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├── 다국어 leak 보강 5,997 (22.7%)
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├── VELA ChatML 정렬 5,000 (18.9%)
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├── 불충분 분석 교정 1,642 (6.2%)
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├── 중국어 leak 교정 v2 1,216 (4.6%)
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└── Reasoning Trace 정렬 566 (2.1%)
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v
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VELA v1.3
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```
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---
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## Training Data Details
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### SFT v3 (58,206 samples)
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| Source | Samples | Ratio | Description |
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|--------|---------|-------|-------------|
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| `classified_news` | 10,830 | 18.6% | GPT-4o 분류된 뉴스 Reasoning Trace |
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| `extreme_signals` | 9,603 | 16.5% | 급등/급락 시그널 뉴스 분석 |
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| `securities_report_gpt4o` | 5,117 | 8.8% | 증권사 리포트 GPT-4o 재구성 |
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| `analysis_news` | 4,839 | 8.3% | 일반 뉴스 영향 분석 |
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| `multi_turn_2t` | 4,000 | 6.9% | 단일 턴 다양 종목 분석 |
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| `multi_turn_4t` | 4,000 | 6.9% | 2턴 follow-up 대화 |
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| **`valuation`** | **2,000** | **3.4%** | 밸류에이션 분석 (v3 Gap Fill) |
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| `tool_calling` | 1,965 | 3.4% | Search/Price/Investor 도구 호출 |
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| **`supply_demand_ext`** | **1,000** | **1.7%** | 수급 확장 분석 (v3) |
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| **`risk`** | **1,000** | **1.7%** | 리스크 분석 (v3) |
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| **`eod_report`** | **1,000** | **1.7%** | EOD 시황 리포트 (v3) |
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| **`refusal`** | **1,000** | **1.7%** | 거절/유보 응답 (v3) |
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| **`short_analysis`** | **1,000** | **1.7%** | 간결 분석 <500자 (v3) |
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| **`deep_reasoning`** | **1,000** | **1.7%** | 심층 추론 5+ steps (v3) |
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| **`low_confidence`** | **1,000** | **1.7%** | 저확신도 분석 (v3) |
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| **`macro_impact_ext`** | **1,000** | **1.7%** | 거시경제 확장 (v3) |
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| **`sector_theme`** | **1,000** | **1.7%** | 섹터/테마 분석 (v3) |
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| `multi_stock_comparison` | 981 | 1.7% | 복수 종목 비교 분석 |
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| `earnings_impact` | 971 | 1.7% | 실적 발표 영향 분석 |
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| `risk_alert` | 948 | 1.6% | 리스크 경고 분석 |
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| **`null_impact`** | **900** | **1.5%** | 주가 무영향 응답 (v3) |
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| Other | 2,050 | 3.5% | batch5 fallback, 기존 수급/섹터/매크로 |
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> v3에서 12개 카테고리 12,635개 샘플을 추가하여 데이터 갭을 보강했습니다.
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> 생성 비용: Perplexity Sonar 2K ($4.60) + OpenAI gpt-4o-mini Batch API 10.6K ($2.76) = **~$7.36**
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### DPO v2 (26,421 pairs)
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| Source | Pairs | Ratio | Rejection Type |
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|--------|-------|-------|----------------|
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| `dpo_dedup` | 12,000 | 45.4% | 짧은/저품질 응답 vs 상세 분석 |
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| `multilingual_aug` | 5,997 | 22.7% | 중국어/영어 leak, 짧은 응답, 저확신 |
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| `vela_chatml` | 5,000 | 18.9% | 형식 오류, 짧은 응답 |
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| `batch5_insuf_dpo` | 1,642 | 6.2% | 불충분 분석 품질 교정 |
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| `chinese_leak_v2` | 1,216 | 4.6% | 중국어 문자 leak 집중 교정 |
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| `reasoning_trace_2k` | 566 | 2.1% | 영어 leak, RT 형식 오류 |
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### Data Version History
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| Version | SFT Samples | DPO Pairs | Changes |
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|---------|-------------|-----------|---------|
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| v1.0 | 36,713 | 24,779 | 초기 학습 데이터 (JSON RT) |
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| v1.1 | 36,713 | 24,779 | RT JSON → Markdown 변환 |
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| v2.0 | 45,571 | 26,421 | +멀티턴 8K, +batch5 불충분 DPO |
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| **v3.0** | **58,206** | **26,421** | **+Gap Fill 12개 카테고리 12,635** |
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---
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## Benchmarks
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### 한국어 LLM 벤치마크 (KMMLU + HAE-RAE)
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모든 모델 **Q4_K_M 양자화**, **0-shot** 조건으로 평가. `lm-evaluation-harness` v0.4.9 + `llama.cpp`, Apple M1 Max 32GB 환경.
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#### KMMLU (한국어 MMLU, 10과목)
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| 과목 | VELA DPO v6 | Qwen2.5-7B-Instruct | EXAONE-3.5-7.8B |
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|------|:-----------:|:--------------------:|:---------------:|
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| 마케팅 | **75.7** | 72.5 | 75.6 |
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| 컴퓨터과학 | **73.7** | 69.7 | 69.7 |
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| 경영학 | 54.0 | 55.2 | **57.3** |
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| 정치사회학 | 49.0 | 49.3 | **56.0** |
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| 경제학 | 45.4 | 47.7 | **51.5** |
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| 법학 | 43.4 | 46.1 | **49.9** |
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| 심리학 | 39.2 | 39.3 | **45.7** |
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| 회계 | 38.0 | 33.0 | **42.0** |
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| 수학 | **33.0** | **33.7** | 27.7 |
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| 한국사 | **31.0** | 29.0 | 22.0 |
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| **평균** | **48.2** | **47.6** | **49.7** |
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#### HAE-RAE Bench (한국어 특화)
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| 영역 | VELA DPO v6 | Qwen2.5-7B-Instruct | EXAONE-3.5-7.8B |
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|------|:-----------:|:--------------------:|:---------------:|
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| 희귀어 | 69.9 | 68.4 | **78.8** |
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| 표준명칭 | 64.7 | 66.0 | **71.9** |
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| 외래어 | 48.5 | 57.4 | **81.1** |
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| 한국사 | 45.7 | 42.6 | **77.7** |
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| 일반상식 | **44.3** | 42.1 | 44.3 |
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| **평균** | **54.5** | **55.3** | **70.7** |
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#### 주요 발견
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- **Catastrophic forgetting 없음**: 도메인 특화 fine-tuning 후에도 베이스 모델(Qwen2.5) 능력 유지 (KMMLU 평균 48.2% vs 47.6%)
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- **도메인 전이 효과**: 금융 관련 과목에서 베이스 모델 대비 향상 — 마케팅(+3.2%), 컴퓨터과학(+4.0%), 회계(+5.0%)
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- **한국어 네이티브 모델과 경쟁**: 대규모 한국어 코퍼스로 사전학습된 EXAONE-3.5-7.8B (LG AI Research) 대비 KMMLU 10과목 중 4개에서 우위
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### 양자화 벤치마크 (GGUF)
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RTX 3060 12GB, llama-cpp-python, `n_gpu_layers=-1`, `n_ctx=4096`
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| 포맷 | 속도 | 중국어 Leak | 품질 |
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|------|------|-------------|------|
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| **Q4_K_M (v6)** | **36 tok/s** | 0/5 클린 | RT + 리포트 정상 |
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|
||||
> 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.
|
||||
24
added_tokens.json
Normal file
24
added_tokens.json
Normal file
@@ -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
|
||||
}
|
||||
120
benchmarks/benchmark_comparison_20260401.json
Normal file
120
benchmarks/benchmark_comparison_20260401.json
Normal file
@@ -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
|
||||
}
|
||||
}
|
||||
}
|
||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -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 %}
|
||||
64
config.json
Normal file
64
config.json
Normal file
@@ -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
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -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"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
24
mlx-int4/added_tokens.json
Normal file
24
mlx-int4/added_tokens.json
Normal file
@@ -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
|
||||
}
|
||||
54
mlx-int4/chat_template.jinja
Normal file
54
mlx-int4/chat_template.jinja
Normal file
@@ -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 %}
|
||||
73
mlx-int4/config.json
Normal file
73
mlx-int4/config.json
Normal file
@@ -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": [
|
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||||
"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
|
||||
}
|
||||
3
vela-dpo-v6-q4_k_m.gguf
Normal file
3
vela-dpo-v6-q4_k_m.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f4fe02ae656d093ab78018b25a8850ffe0014c4ab628a113f71d9df79ffab5e3
|
||||
size 4683073408
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user