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The model that didn't exist, so you made it yourself
How I prompt ML Intern 1. A model that knows your field 2. A model that draws your character 3. A model that does a new trick 4. A model that fits your device What it cost Make yours Last week, I wanted a small version of the prompt rewriter that ships with Qwen-Image 2.1. The official one is a 9B model that needs abo…
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An environment gives an agent a task, responds to its actions with observations, and scores the outcome. The resulting rewards can measure an agent's performance during evaluation or provide a learning signal during training. For an introduction to this interaction loop, see our blogpost on environments . Within the e…
Hugging FacePublished Sep 28, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
Our latest paper, ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents (read it on Hugging Face , or on arXiv in the meantime), targets that gap. The failure mode we care about is one we call cross-source conflation: a claim that is true somewhere in the evidence, but attributed to the wrong…
Hugging FacePublished Sep 29, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
Evaluation, however, hasn't kept pace: it remains fragmented and unstandardized. The gold standard is human preference scores such as MOS or MUSHRA (more on metrics ). To this end, several arena-based leaderboards have established themselves as useful reference points for the community:
Hugging FacePublished Sep 30, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026