Skip to main content
Hugging FaceProduct launch

Hugging Face released Carbon-X, an open-source 14.8B parameter Apache 2.0 decoder-only model trained jointly on DNA and text that fits on an 80 GB GPU.

What happened

Post: "Carbon-X is an open 14.8B model trained jointly on DNA and text [R]"

Source

RedditOct 22, 2026By u/jatingargiitk

r/MachineLearning

Carbon-X is an open 14.8B model trained jointly on DNA and text [R]

upvotes
44
comments
1

Post

Highlighted: the lines this signal was extracted from

Carbon-X is a 14.8B parameter decoder-only model that takes raw DNA in a normal chat and answers in English. It shares one token stream for text and DNA, using 6-mer tokens, and was mid-trained on 302B tokens. Hugging Face reports a 66.7% average across five DNA benchmarks with thinking disabled, versus 67.5% for GPT-6.1 Sol. The weights fit on one 80 GB GPU in bfloat16 and are Apache 2.0. It has not been clinically validated, and the model card says exact sequence operations should still be handled by code. the useful part is making sequence analysis local enough for sensitive datasets. has anyone tested whether the benchmark gains hold on private variant datasets? source: https://huggingface.co/HuggingFaceBio/Carbon-X

reddit.com/r/MachineLearning/comments/1xchayq/carbonx_is_an_open_148b...Read the full source

Extracted by Autobound

From the Signal API record
Signal
Product launch

What this signalsUser posts often show product pain before it reaches reviews or churn.

Subreddit
r/MachineLearning
Stage
Confirmed
Event date
Oct 2026

Companies

  • OpenAIAlso named

The full record

From the Signal API record

Numbers

Mentions
5

Details

Timing
Completed
Virality
Low
Post kind
Multi media
Prominence
Core
Company's role
Subject
Signal category
Event

Topics and mentions

Topics

  • open source
  • genomics
  • machine learning
  • ai

Flair

  • Research

Products named

  • Carbon-X

Extraction

Sentiment
Positive
Detected
Oct 22, 2026
signal_type
reddit-company
signal_subtype
productLaunch

Use this data

Get every Reddit signal for Hugging Face and the companies you sell to, in the tools you already use.

  1. Ask Claude about it

    Connect Autobound to Claude, Claude Code or Cursor with MCP. Then ask: “What changed at Hugging Face this week?”

  2. Send it to your own tools

    The Signal API returns Reddit signals for any list of companies as JSON, for your CRM, warehouse or app.

  3. Try it free

    Sign up and spend your free credits on the companies you sell to.

    Start Free1,000 free credits

The API returns more than this page shows

This page shows a preview. The full reddit-company record in the Signal API and MCP can also have these 8 fields. Some fields are empty for some signals.

Company

  • linkedin_urlValue in the API
  • industriesValue in the API
  • employee_count_lowValue in the API
  • employee_count_highValue in the API
  • revenueValue in the API
  • descriptionValue in the API

Signal

  • signal_nameValue in the API
  • associationValue in the API
Show the full JSONThe record on this page and the API request

GET /v1/signals/dc0ee20b-90f9-5897-a0e3-0f1b35982e6a returns this record as JSON. POST /v1/companies/enrich returns every signal for huggingface.co.

{
  "signal_id": "dc0ee20b-90f9-5897-a0e3-0f1b35982e6a",
  "signal_type": "reddit-company",
  "signal_subtype": "productLaunch",
  "detected_at": "2026-10-22T12:32:56+00:00",
  "company": {
    "name": "Hugging Face",
    "domain": "huggingface.co"
  },
  "data": {
    "nsfw": false,
    "stage": "confirmed",
    "awards": 0,
    "timing": "completed",
    "topics": [
      "ai",
      "open source",
      "genomics",
      "machine learning"
    ],
    "post_id": "1xchayq",
    "summary": "Hugging Face released Carbon-X, an open-source 14.8B parameter Apache 2.0 decoder-only model trained jointly on DNA and text that fits on an 80 GB GPU.",
    "evidence": [
      "[post] Carbon-X is an open 14.8B model trained jointly on DNA and text [R]",
      "[post] Hugging Face reports a 66.7% average across five DNA benchmarks with thinking disabled, versus 67.5% for GPT-6.1 Sol. The weights fit on one 80 GB GPU in bfloat16 and are Apache 2.0."
    ],
    "virality": "low",
    "post_date": "2026-10-22T12:32:56.000Z",
    "post_kind": "multi_media",
    "post_text": "Carbon-X is a 14.8B parameter decoder-only model that takes raw DNA in a normal chat and answers in English. It shares one token stream for text and DNA, using 6-mer tokens, and was mid-trained on 302B tokens.\n\nHugging Face reports a 66.7% average across five DNA benchmarks with thinking disabled, versus 67.5% for GPT-6.1 Sol. The weights fit on one 80 GB GPU in bfloat16 and are Apache 2.0. It has not been clinically validated, and the model card says exact sequence operations should still be handled by code.\n\nthe useful part is making sequence analysis local enough for sensitive datasets. has anyone tested whether the benchmark gains hold on private variant datasets?\n\nsource: https://huggingface.co/HuggingFaceBio/Carbon-X",
    "sentiment": "positive",
    "subreddit": "MachineLearning",
    "event_date": "2026-10",
    "post_flair": [
      "Research"
    ],
    "post_title": "Carbon-X is an open 14.8B model trained jointly on DNA and text [R]",
    "prominence": "core",
    "source_url": "https://www.reddit.com/r/MachineLearning/comments/1xchayq/carbonx_is_an_open_148b_model_trained_jointly_on/",
    "entity_role": "subject",
    "post_author": "jatingargiitk",
    "upvote_ratio": 0.9074074074074074,
    "mention_count": 5,
    "mention_surge": true,
    "subreddit_url": "https://www.reddit.com/r/MachineLearning/",
    "total_upvotes": 44,
    "comments_total": 1,
    "total_comments": 1,
    "other_companies": [
      {
        "name": "OpenAI",
        "role": "competitor",
        "domain": "openai.com"
      }
    ],
    "post_author_url": "https://www.reddit.com/user/jatingargiitk/",
    "signal_category": "event",
    "comments_included": 0,
    "products_mentioned": [
      "Carbon-X"
    ]
  }
}

Long text fields are shortened on this page.

Looking up one signal by its id is free. Enrich costs 2 credits per signal returned; a call with no results is free.