{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "datamllab-openebfm--chat-with-published-sft-model",
 "repo": "datamllab/OpenEBFM",
 "repo_url": "https://github.com/datamllab/OpenEBFM",
 "repo_slug": "datamllab-openebfm",
 "title": "Chat with the published EBFM SFT model in a browser",
 "page_title": "How to chat with the published EBFM SFT model in a browser with Open EBFM",
 "summary": "Serve the EBFM-FineWeb-1B-CHAT model through a local web chat interface.",
 "who": "A researcher or hobbyist with a GPU server who wants to try a published energy-based language model interactively.",
 "problem": "Trying a new language model usually means writing inference code, and it is not obvious how to serve it to a browser.",
 "outcome": "A running web chat at port 8000 that talks to the published SFT checkpoint.",
 "steps": [
  "Run `uv sync --extra gpu --extra hf --extra web` and `source .venv/bin/activate` to set up the environment.",
  "Run `bash runs/chat.sh --web --checkpoint guan-wang/EBFM-FineWeb-1B-CHAT --host 0.0.0.0 --port 8000` to start the server. (inferred, check the README)",
  "Open http://localhost:8000 in a browser on the server machine.",
  "If the server is a remote GPU node, use your cluster's port forwarding to expose port 8000 on your local machine."
 ],
 "agent_prompt": "In the datamllab/OpenEBFM repo, start the web chat with runs/chat.sh using guan-wang/EBFM-FineWeb-1B-CHAT on host 0.0.0.0 and port <port>. Report the URL to open and confirm the server responds on that port.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The README says the chat runs on a GPU and the published checkpoint is downloaded on first use, so the first launch depends on network speed and GPU availability.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python with uv installed",
  "A GPU environment (the README installs the gpu extra and loads checkpoints with device=\"cuda\")",
  "Network access for data preparation and Hugging Face downloads",
  "Enough disk space for the datasets and checkpoints (sizes are not stated in the README)"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.9,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/datamllab-openebfm/chat-with-published-sft-model/",
 "md": "https://dedupedai.com/use-cases/datamllab-openebfm/chat-with-published-sft-model/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/datamllab-openebfm--chat-with-published-sft-model.json",
 "api_version": "v1"
}