Chukwuma Nwaugha

nwaughachukwuma

5 2 16

https://nwaughachukwuma.github.io/portfolio

AI & ML interests

high-performance Inference for visual AI (vlm-run gateway). context engineering (mm-ctx)

Recent Activity

upvoted an article 21 minutes ago

VLM Run Gateway: Run open-weight OCR, VLM and vision models behind one API

posted an update about 5 hours ago

In 1887, Eadweard Muybridge lined up twelve cameras to answer one question: how does a body actually move? Today, you can easily tell by pointing the video at @vlmrun Gateway. ``` $ uvx vlmrun gw chat clip.mp4 -m vitpose-plus-large --method pose ``` One call. 612 keypoints. 36 frames. $0.000998.

new activity 11 days ago

huggingface/InferenceSupport:deepseek-ai/DeepSeek-OCR

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posted an update about 5 hours ago

Post

In 1887, Eadweard Muybridge lined up twelve cameras to answer one question: how does a body actually move?

Today, you can easily tell by pointing the video at @vlmrun Gateway.

$ uvx vlmrun gw chat clip.mp4 -m vitpose-plus-large --method pose

One call. 612 keypoints. 36 frames. $0.000998.

posted an update 15 days ago

Post

You can now use paddleocr-vl on VLM Run Gateway.

Via the CLI:

uvx vlmrun gw models
uvx vlmrun config set --api-key '<VLMRUN_API_KEY>' # anon-user, rate-limited
uvx vlmrun gw chat <doc>.pdf -m paddleocr/pp-ocrv6

uvx vlmrun gw chat <doc>.pdf -m zai-org/glm-ocr
uvx vlmrun gw chat <doc>.pdf -m zai-org/glm-ocr --json-mode
uvx vlmrun gw chat <doc>.pdf -m deepseek-ai/deepseek-ocr-2
uvx vlmrun gw chat <doc>.pdf -m rednote-hilab/dots.mocr

OpenAI SDK:

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="paddlepaddle/paddleocr-vl",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.receipt/playground/2.jpg"
                    },
                },
            ],
        }
    ],
    extra_body={"method": "table"},
)

print(response.choices[0].message.content)

or Curl:

curl https://gateway.vlm.run/v1/openai/chat/completions \
  -X POST \
  -H "Authorization: Bearer <VLMRUN_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "paddlepaddle/paddleocr-vl",
    "method": "table",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "image_url",
            "image_url": {
              "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.receipt/playground/2.jpg"
            }
          }
        ]
      }
    ]
  }'

Docs: https://docs.vlm.run/gateway

Catalog: https://docs.vlm.run/gateway/models

MCP: https://docs.vlm.run/gateway/mcp-server

Colab Quickstart: https://colab.research.google.com/drive/1RkuVIyuc5Po-UlcSlFyJCam5tjCm9IHM?usp=sharing

posted an update 16 days ago

Post

3567

# VLM Run Gateway: Run GLM-OCR, DeepSeek-OCR-2, and Dots.mocr with an OpenAI Compatible API

Open-weight OCR VLMs have advanced significantly over the past year, yet most teams still rely on frontier VLMs for document parsing because researching, evaluating, and deploying the right models remains challenging.

So we built VLM Run Gateway: one OpenAI-compatible endpoint for open-weight OCR and VLM models.

If you’re using frontier VLMs primarily for OCR/document parsing, open-weight OCR models can be dramatically cheaper and often very accurate. With a one-line change, you can switch between open-weight OCR VLMs (DeepSeek OCR 2, GLM-OCR, dots.mocr, Paddle OCR VL, PP-OCRv6, etc.) and process 100K+ pages for under $60.

Try it out quickly via the CLI:

uvx vlmrun gw models
uvx vlmrun config set --api-key '<VLMRUN_API_KEY>' # anon-user, rate-limited
uvx vlmrun gw chat <doc>.pdf -m zai-org/glm-ocr

uvx vlmrun gw chat <doc>.pdf -m zai-org/glm-ocr --json-mode
uvx vlmrun gw chat <doc>.pdf -m deepseek-ai/deepseek-ocr-2
uvx vlmrun gw chat <doc>.pdf -m rednote-hilab/dots.mocr
uvx vlmrun gw chat <doc>.pdf -m paddleocr/pp-ocrv6

OpenAI SDK:

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="rednote-hilab/dots.mocr",
    messages=[{
      "role": "user",
		  "content": [{
            "type": "document_url",
            "document_url": {"url": "https://.../invoice.pdf"},
      }],
    }],
    extra_body={"document_dpi": 72},
)

reacted to SeaWolf-AI's post with 👍 20 days ago

Post

4298

🧬 Your AI can design a malaria drug candidate. Can it tell you whether it's any good?

Open Discovery Challenge #1 — Malaria is live. Design a molecule with any model — OpenAI, Claude, Gemini, Qwen, KIMI, DeepSeek, open weights, or by hand — submit it as SMILES, and it's scored in minutes on whole-cell activity, target binding, selectivity over the human enzyme, ADMET, novelty and synthesisability.

You can check the scoring instead of trusting it. Approved drugs sit on the same leaderboard as the entries: DSM265, a clinical-stage antimalarial, scores 50.9. Teriflunomide — approved, but it hits the human enzyme — scores 2.8. Caffeine scores 1.8. If the clinical candidate lands on top and coffee lands at the bottom, the scorer discriminates.

We caught 14 defects before opening — conventional toxicity cutoffs rejected all three approved antimalarials and coffee. All written up, along with the rule we now hold everything to: a gate that rejects an approved drug is a broken gate.

Your molecule stays yours. No patent interest, nothing into our pipeline. You choose whether it's published — and publishing can cost you patentability, so we say so.

USD 1,000 to the top entry when Season #1 closes 30 September 2026 — not payment for your tokens, but a way of saying the work had worth.

Malaria killed ~597,000 people in 2023, three quarters of them children under five. Not for want of chemistry — for want of a market.

No chemistry needed: the guide ships five prompts you can paste straight into your model, and the full rubric is published.

📖 https://huggingface.co/blog/FINAL-Bench/open-discovery-challenge
🚀 FINAL-Bench/open-discovery-challenge

Computational assessments of candidates — not measurements, not claims of efficacy.

12 replies

posted an update 22 days ago

Post

2713

Following this post - https://huggingface.co/posts/nwaughachukwuma/366831808712777, I made another run on a fresh RTX PRO 4000 using inkling, gemini-3.5-flash, and glm-5.2+mm-ctx

| case | gemini-3.5-flash @k=1 | glm-5.2+mm @k=1 | inkling @k=1 |
| --- | --- | --- | --- |
| **MEAN correctness** | **78.2** | **79.5** | **57.9** |
| **MEAN speed (s)** | **111** | **123** | **69** |
| **completed (case-runs)** | **20/23** | **21/23** | **18/23** |