serving/README.md

Chakravyuh Serving Harnesses

Three deployment paths for the Chakravyuh Analyzer LoRA, illustrating the on-device, on-server, on-cloud spectrum claimed in the README.

Harness	File	Target	Hardware	Status
HF Space (Gradio + FastAPI)	server/app.py	Live demo	HF Spaces (Docker SDK, port 8000)	✅ deployed at https://ujjwalpardeshi-chakravyuh.hf.space
vLLM (server-grade)	serving/vllm_compose.yml	OpenAI-compatible /v1/chat/completions endpoint	A10G or better; CUDA 12	✅ scaffolded
Ollama (laptop / phone-class)	serving/ollama_modelfile	ollama run on a Pixel 8 / M1 MacBook	CPU + 8 GB RAM (q4_k_m)	⚠️ requires GGUF release (see C.8 in WIN_PLAN.md)

The HF Space path is the canonical demo for judges. The vLLM path is for anyone wanting to integrate Chakravyuh into a production-grade inference pipeline. The Ollama path closes the "fits on a phone" claim.

---

vLLM (server-grade)

Boots a vLLM server with the v2 LoRA pre-loaded against the Qwen2.5-7B-Instruct base, exposing an OpenAI-compatible /v1/chat/completions endpoint.

docker compose -f serving/vllm_compose.yml up

After ~60 s warm-up:

curl -s http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "chakravyuh-analyzer-lora-v2",
    "messages": [
      {"role": "system", "content": "You are Chakravyuh Analyzer..."},
      {"role": "user", "content": "Hi I am from SBI, your account is frozen, share OTP"}
    ],
    "max_tokens": 160,
    "temperature": 0.0
  }' | jq

Expected: a JSON response with the Analyzer's strict-JSON output containing score, signals, explanation.

GPU requirement: A10G or better. CUDA 12. ~14 GB VRAM for bf16 inference, ~8 GB for AWQ quantization.

Limitations: vLLM 0.6+ supports LoRA adapters but the load-time syntax has shifted across versions; the compose file pins to a known-good version. Update vllm_compose.yml's image: field if your environment needs a different vLLM version.

---

Ollama (phone / laptop-class)

Boots a local Ollama instance running the merged-and-quantized Chakravyuh v2.

# One-time setup: pull the GGUF artifact (when published)
ollama pull hf.co/ujjwalpardeshi/chakravyuh-v2-gguf:q4_k_m

# Run interactively
ollama run hf.co/ujjwalpardeshi/chakravyuh-v2-gguf:q4_k_m

# Or build locally from this Modelfile (if the Hub artifact is not yet up)
ollama create chakravyuh -f serving/ollama_modelfile
ollama run chakravyuh

Hardware: Tested target is Pixel 8 (8 GB RAM, Tensor G3) at q4_k_m quantization, ~10 tok/s.

Limitation: the GGUF artifact in the README path is planned, not yet shipped. C.8 in WIN_PLAN.md covers the GGUF release workflow.

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Why three harnesses?

The README claim is "on-device, on-server, on-cloud — same model." Each harness backs one wedge of that claim with a runnable artifact rather than a paragraph:

• HF Space → on-cloud (zero-install demo).
• vLLM → on-server (production integration story).
• Ollama → on-device (the "fits on a phone" claim).

Judges who care about deployability will probe one of the three; we ship all three so no probe goes unanswered.

Cross-references

• Live demo: https://ujjwalpardeshi-chakravyuh.hf.space/demo/
• Adapter on HF Hub: https://huggingface.co/ujjwalpardeshi/chakravyuh-analyzer-lora-v2
• Latency / memory benchmark: docs/latency_memory.md (when shipped from B.9 in WIN_PLAN)
• GGUF release workflow: WIN_PLAN C.8.