ISLAM-PO/platform-test-models

Safetensors Studio & Bench

Run, test, and benchmark local Safetensors models with surgical VRAM observability.

Safetensors Studio & Bench is a full-stack, local-first platform to load any *.safetensors + config.json + tokenizer.json folder, stream chat with real TPS/TTFT, run automated benchmarks (Reasoning/Coding/Arabic/Summarization), and profile GPU/CPU/RAM live — no cloud required.

📖 Arabic README: README_AR.md | 📚 Docs: docs/ | 🤝 Contribute: CONTRIBUTING.md

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✨ Key Features

Area	What you get
Safetensors Loader	Local folder picker • Float16 / Bfloat16 / Float32 • 4-bit NF4 / 8-bit via bitsandbytes • device_map: auto / balanced / cpu / cuda:0 + offloading
Live Playground	Token Streaming (SSE) • Real Tokens/sec & TTFT • Temperature / Top-P / Max Tokens • Copy & clear
Automated Benchmark	15 preset tasks: Reasoning (4), Coding (4), Arabic Quality (4), Summarization (3) • Exact / Regex / LLM-as-Judge • Custom datasets: drop any folder with .csv/.json/.jsonl/.txt/.md → auto-detects language (Arabic/English) & category
Hardware Profiling	pynvml + psutil → live VRAM/CPU/RAM/Power via Recharts • VRAM peak timeline • TPS vs VRAM scatter • 600-point history
Reports & Sharing	Export PDF (Arabic-capable) / JSON / CSV • Shareable link /share/{token} • Full tables + by_category insights

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🌍 Supported Languages

Language	Coverage
English	UI, prompts, docs, code, benchmarks (Reasoning, Coding, Summarization)
Arabic (العربية)	Full UI RTL, benchmarks (Arabic Quality, Summarization), PDF with tahoma.ttf + arabic-reshaper, auto-detection \u0600-\u06FF for custom datasets
Mixed	Auto language_counts: {ar, en} per folder, per report

Add a new language: add prompts to backend/app/benchmark.py:14 and categories to dataset_parser.py:11. See SUPPORTED_LANGUAGES.md.

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🚀 Quick Start

1. Requirements

• Node 22+ / Python 3.12+
• 8GB RAM minimum (CPU mode), 12GB+ VRAM recommended for 7B models on GPU

2. Backend

cd backend
pip install -r requirements.txt          # torch CPU + transformers are included
# optional GPU: pip install torch --index-url https://download.pytorch.org/whl/cu121
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
# → http://localhost:8000/docs

3. Frontend

cd frontend
npm install
npm run dev
# → http://localhost:3000
# env: frontend/.env.local → NEXT_PUBLIC_API_URL=http://localhost:8000

4. Docker (alternative)

docker compose up --build
# frontend http://localhost:3000  backend http://localhost:8000

5. Load a model

• Place a HF-style folder anywhere, e.g. C:\models\mistral-7b containing model.safetensors, config.json, tokenizer.json
• In UI / or /models or new: top bar of /benchmark → paste path → Validate → Load
• Or via API: POST /api/model/load {"model_path":"C:\\models\\my-model","dtype":"float16","quantization":"4bit","device_map":"auto"}

No GPU? The platform auto-enters Demo Mode (simulated generation) so you can still test all features.

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📁 Project Structure

backend/app/
  main.py                 # FastAPI + CORS + routers
  model_manager.py        # Safetensors loader (torch dtype/quant/device_map)
  telemetry.py            # pynvml/psutil + 600-point history
  benchmark.py            # 15 tasks + evaluate_answer()
  dataset_parser.py       # CSV/JSON/JSONL/TXT/MD → BenchmarkTask + lang detect
  schemas.py
  routers/{model,inference,telemetry,benchmark,custom_benchmark,share,export}.py
frontend/src/
  app/{page, playground, benchmark, hardware, models, share/[token]}
  components/{ModelLoader, ModelPathSelector, Playground, BenchmarkPanel, CustomDatasetPanel, TelemetryCharts}
  lib/{api.ts, utils.ts}
example_dataset/          # 9-task mixed sample (AR/EN)
docs/                     # English developer docs

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🔌 API Reference (core)

Method	Path	Description
POST	/api/model/load	Load Safetensors folder
GET	/api/model/status	Loaded info
GET	/api/model/validate?path=	Check *.safetensors + config.json
POST	/api/generate (SSE)	Stream tokens with ttft_ms, tokens_per_sec
GET	/api/telemetry/	Snapshot (VRAM/GPU/CPU/RAM)
WS	/ws/telemetry	Live push (fallback to HTTP polling)
POST	/api/benchmark/run	Sync benchmark (blocking)
POST	/api/benchmark/run-stream	SSE progress (type: progress/task_done/done)
POST	/api/benchmark/custom/scan?folder_path=	Scan custom folder
POST	/api/benchmark/custom/run-from-folder	Run on custom folder
POST	/api/benchmark/custom/upload	Multipart upload
POST	/api/share/{report_id}	Create share token
GET	/api/share/{token}	Fetch shared report
GET	/api/export/{json,csv,pdf}?report_id=	Export

Full spec: http://localhost:8000/docs

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🧪 Benchmark Suites

Suite	Tasks	Judge
reasoning	arithmetic, sequence, logic puzzle, fraction	Regex \b7\b
coding	fibonacci, loop output, reverse_string, sorted	Regex def\s+
arabic	spelling, synonym, i'rab, summarization	Regex ذهبت, فرح
summarization	tech text, Transformer, bullet points	Regex 7 مليار

Custom folder example (CSV):

prompt,expected,expected_regex,category,name
"ما مرادف سعيد؟",فرح,فرح,arabic,syn

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📊 Hardware Profiling

• telemetry.py:19 polls pynvml.nvmlDeviceGetMemoryInfo + psutil.virtual_memory every 1.2s
• Recharts Area/Line/Scatter: VRAM timeline, CPU/RAM %, Power vs VRAM, TPS vs VRAM
• vram_peak_mb tracked per task and globally

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📄 Export & Sharing

• PDF: Unicode via tahoma.ttf + arabic-reshaper + python-bidi (fallback to ? sanitization)
• Share: in-memory share_store[token]=report_id → /share/{token} page

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🛠️ Development

See docs/DEVELOPMENT.md and docs/ARCHITECTURE.md.

# Frontend
npm run build   # production
npm run lint

# Backend
python -m pytest  # (add tests)
pip install -r requirements.txt

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🤗 Hugging Face

• Use any HF model: download snapshots and point model_path to it.
• Model card template: docs/HUGGINGFACE.md
• Dataset parser auto-handles HF datasets exported as JSONL/CSV.

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🤝 Contributing

Please read CONTRIBUTING.md and CODE_OF_CONDUCT.md. PRs for new languages, benchmarks, and quant backends are welcome!

🔒 Security

See SECURITY.md.

📝 License

MIT — see LICENSE.

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Built with ❤️ for local AI — no cloud, full control.