spillai/llama-3-2-11b-instruct-overfit
Updated • 6
Sudeep Pillai PRO
spillai
AI & ML interests
Self-supervised learning, Few-shot learning, Computer Vision, Robotics
Recent Activity
reacted to nwaughachukwuma's post with 👀 2 days ago
# One API for Every Visual & OCR Models.
The VLM Run Gateway is a fully compatible API for OpenAI chat completions for visual intelligence. If you’re building document extraction or visual understanding, the Gateway exposes OCR, VQA, and detection behind a single interface you already know.
Read the docs: https://docs.vlm.run/gateway/introduction.
We actively support the following recent OCR and VQA models, which you can try today at no cost:
* zai-org/glm-ocr
* rednote-hilab/dots.mocr
* paddleocr/pp-ocrv6
* qwen/qwen3.5-0.8b
## Quickstart
### Python
```
from openai import OpenAI
client = OpenAI(base_url="https://gateway.vlm.run/v1/openai")
response = client.chat.completions.create(
model="zai-org/glm-ocr",
messages=[
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/finance.sec-filings/tsla-8k.pdf"
},
},
],
}
],
extra_body={"method": "markdown", "document_dpi": 150},
)
print(response.choices[0].message.content)
```
### Curl
```
curl https://gateway.vlm.run/v1/openai/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer vlmrun" \
-d '{
"model": "zai-org/glm-ocr",
"messages": [
{
"role": "user",
"content": [
{
"type": "document_url",
"document_url": {
"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/finance.sec-filings/tsla-8k.pdf"
}
}
]
}
],
"method": "markdown",
"document_dpi": 150
}'
```
## Auth and limits
Anonymous auth is enabled, so you can omit the authorization header entirely, or send Bearer "" or Bearer vlmrun. Rate limits are 60 req/min and 1000 req/hr. liked a model about 2 months ago
RedHatAI/Qwen3.6-35B-A3B-NVFP4 reacted to theirpost with 🔥 2 months ago
mm-ctx – fast, multimodal context for agents.
LLM-based agents handle text incredibly well, but images, videos, or PDFs with visual content are hard to interpret. mm-ctx gives your CLI agent multi-modal skills.
Try it interactively in Spaces: https://huggingface.co/spaces/vlm-run/mm-ctx
Readme: https://vlm-run.github.io/mm/
PyPI: https://pypi.org/project/mm-ctx
SKILL.md: https://github.com/vlm-run/skills/blob/main/skills/mm-cli-skill/SKILL.md
mm-ctx is meant to feel familiar: the UNIX tools we already love (find/cat/grep/wc), rebuilt for file types LLMs can't read natively and designed to work with agents via the CLI.
- mm grep "invoice #1234" ~/Downloads searches across PDFs and returns line-numbered matches
- mm cat <document>.pdf returns a metadata description of the file
- mm cat <photo>.jpg returns a caption of the photo
- mm cat <video>.mp4 returns a caption of the video
A few things we obsessed over:
⚡ Speed: Rust core for the hot paths
🏠 Local-first, BYO model: Uses any OpenAI-compatible endpoint: Ollama, vLLM/SGLang, LMStudio with any multimodal LLM (Gemma4, Qwen3.5, GLM-4.6V).
🔗 Composable: stdin + structured outputs
🤖 Drops into any agent via mm-cli-skills: Claude Code, Codex, Gemini CLI, OpenClaw.
We’d love to hear your feedback! Especially on the CLI and what file types and workflows you would like to see next.Organizations
