Chukwuma Nwaugha

nwaughachukwuma

5 2 18

https://nwaughachukwuma.github.io/portfolio

AI & ML interests

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

Recent Activity

liked a model 3 days ago

Cloudflare/clef

posted an update 11 days ago

Cheap, fast, and efficient OCR + RAG pipeline with VLM Run Gateway and TypeSafe AI Jev.

liked a dataset 21 days ago

teknium/openhermes

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Organizations

liked a model 3 days ago

Cloudflare/clef

Image-Text-to-Text • 27B • Updated 4 days ago • 4.21k • 1.24k

posted an update 11 days ago

Post

Cheap, fast, and efficient OCR + RAG pipeline with VLM Run Gateway and TypeSafe AI Jev.

liked a dataset 21 days ago

teknium/openhermes

Viewer • Updated Sep 7, 2023 • 243k • 1.18k • 229

posted an update 27 days ago

Post

3233

It’s easy to get distracted by benchmarks, throughput (tok/s), and all the hype around frontier model releases.

This is Shiny Model Syndrome, which makes engineers and teams forget the basic physics of production software, i.e., using the right tool for the job and optimizing for ease of integration.

- Teams spend huge amounts of money on frontier models for document parsing, OCR, detection, segmentation, and other task-specific visual AI workflows.

- Inference marketplaces don’t find it profitable to list task-specific models like glm-ocr, paddleocr, or dots.mocr, even though they’re all superior to frontier VLMs for document parsing and OCR.

- Engineers stitch together multiple endpoints for different use cases across the long tail of visual AI. Those who choose to self-host instead deal with painful infrastructure and GPU ops.

At VLM Run, we wanted one place to run OCR models, VLMs, and ViTs that we could confidently use for our own internal agents and evals. The gateway was born out of that need, and we’ve since opened it to the public.

The gateway exposes a single OpenAI-compatible endpoint for the long tail of visual AI across OCR, document parsing, VQA, detection, segmentation, embeddings, and transcription. Simply point the base_url of your OpenAI SDK at gateway.vlm.run/v1/openai, or ask your agent to connect via MCP (gateway.vlm.run/mcp).

You can swap the model name to compare glm-ocr, dots.mocr, paddleocr-vl-1.6, qwen3.8-27b, gemma4-26b-a4b, and more. We handle serving, runtime, and pipelining behind the scenes to give you high-quality visual intelligence.

- https://vlm.run/gateway
- https://huggingface.co/blog/vlm-run/introducing-gateway
- https://www.vlm.run/blog/introducing-gateway

reacted to spillai's post with 🚀 30 days ago

Post

3208

We're excited to introduce VLM Run Gateway - a new unified OpenAI-compatible API for running open-weight VLMs, OCR VLMs and ViT-based vision models.

https://vlm.run/gateway
Full model catalog: https://vlm.run/gateway/models
Blog post announcement: https://www.vlm.run/blog/introducing-gateway

Try different models on the gateway simply by updating the model name. Free to use and no sign-up required for now (in alpha).

$ uvx vlmrun gw models
$ uvx vlmrun gw chat <doc>.pdf -m glm-ocr
$ uvx vlmrun gw chat <doc>.pdf -m deepseek-ocr-2
$ uvx vlmrun gw chat <doc>.pdf -m pp-ocrv6
$ uvx vlmrun gw chat <img>.jpg -m qwen/qwen3.5-0.8b -p "describe the image"
$ uvx vlmrun gw chat <vid>.mp4 -m qwen/qwen3.5-0.8b -p "describe the video"

upvoted an article about 1 month ago

Article

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

vlm-run

•

about 1 month ago

• 6

posted an update about 1 month 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.

New activity in huggingface/InferenceSupport about 1 month ago

deepseek-ai/DeepSeek-OCR

🔥🚀 383

#5627 opened 12 months ago by

khushwant04

deepseek-ai/DeepSeek-OCR-2

🔥 62

#7669 opened 8 months ago by

J4BEZ

rednote-hilab/dots.mocr

👍 6

#8988 opened 6 months ago by

JohnyTheDoey

posted an update about 1 month 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"
            }
          }
        ]
      }
    ]
  }'

New activity in huggingface/InferenceSupport about 1 month ago

PaddlePaddle/PaddleOCR-VL-1.6

👍🤗 7

#10220 opened 4 months ago by

mamunhpath

posted an update about 2 months ago

Post

3587

# 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},
)

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

Read the full post here: https://huggingface.co/blog/vlm-run/intro-to-vlmrun-gateway

published an article about 2 months ago

Article

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

vlm-run

•

Aug 19

• 6

reacted to SeaWolf-AI's post with 👍 about 2 months ago

Post

4315

🧬 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 about 2 months ago

Post

2727

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** |

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published an article 2 months ago

Article

Text-Only Models with mm-ctx Vision Toolkit vs. Native Vision Models

vlm-run

•

Jul 30

Chukwuma Nwaugha

AI & ML interests

Recent Activity

Organizations

nwaughachukwuma's activity

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

deepseek-ai/DeepSeek-OCR

deepseek-ai/DeepSeek-OCR-2

rednote-hilab/dots.mocr

PaddlePaddle/PaddleOCR-VL-1.6

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

Text-Only Models with mm-ctx Vision Toolkit vs. Native Vision Models