huggingface

Why the downvotes? Are people completely oblivious to huggingface/tau ? That far removed from the fact that the harness is not the TUI?

FineDNA: the largest dataset to teach any LLM DNA (midtraining, SFT, RL) Hi everyone, we're releasing FineDNA, all the data we used to turn Qwen3-14B into Carbon-X, the strongest open language model on DNA. It contains: - Midtraining: 300B+ tokens of genomes and DNA-rich text - SFT: ~200K conversations - RL: ~98K verifiable prompts Code and recipe are open too, so you can apply it to other LLMs. Dataset: huggingface.co Code: huggingface/carbon

Open data and code to teach LLMs to read DNA

TRL has day-0 support to post-train Le Chonk on a single node 🤯 LoRA SFT on a 1T-parameter model, training adapters on top of FP8 weights: ⚡ 32.7k tokens per step in ~8 seconds 💾 142 GiB per GPU on 8× B300 🔀 Expert parallelism + FSDP Recipe: github.com/huggingface/trl/blob/ea936f801deeea713… Open weights = open training after all ;)

SafeAgents: an open-source proxy that checks every tool call your coding agent makes before it runs SafeAgents is a small local proxy that sits between a coding agent and its model server. It checks each tool call in the model's response before the harness receives it. A call it flags is replaced with a short text notice, so the harness gets a plain text reply and runs nothing. You point the agent's base URL at the proxy instead of the model server. All checks run locally on your machine: the proxy only forwards traffic to the model server you already use, and doesn't send your prompts, code or tool calls anywhere else unless you point the optional judge at a remote server. Each tool call goes through up to three checks: 1. Two local checks that only need the request: whether the tool was declared by the harness, and whether the call reaches a host that appears nowhere in the conversation. The model's own earlier turns don't count as a source, so a URL the model came up with on its own is flagged. 2. atif-scan by u/evalstate, which runs about 110 deterministic detectors on the pending call, in-process, with no model or network call: sandbox side channels, proxy and network bypasses, namespace escapes and reward hacking. Examples are curl to the cloud metadata endpoint, curl --noproxy posting ~/.netrc somewhere, or reading /proc/1/environ. 3. Optionally, a judge model for milder findings. It can be any OpenAI-compatible server, including a small model on your own machine through llama.cpp, so this step can stay local too. It answers allow or block as JSON. It sees only the user's request, the call and the findings, not the agent's reasoning or tool outputs. It can add blocks but not remove them. Catching reward hacking in RL and evals The same detectors cover the tool calls a policy makes to game its reward instead of solving the task: writing its own reward file, editing the verifier's tests, or patching pytest so everything passes. Since safeagents is just a proxy in front of an OpenAI-compatible server, it can sit in front of the inference server used for rollouts or eval runs. In --log-only mode it records these attempts without changing the trajectories, which gives a measure of how often a model tries to cheat. In blocking mode, the call is replaced with a notice and never runs. Some practical details: - It handles OpenAI Chat Completions and Anthropic Messages, streamed or not. Text is forwarded as it arrives; a tool call is held until its arguments are complete. - The local checks take around 10–20 ms per call on an M-series Mac. The judge is only called for calls that already raised a finding. - --log-only checks every call without changing anything, which is the recommended way to start. Hosts you trust can be added with --allow-host. - The repo has setup guides for 12 harnesses. Six of them (pi, Claude Code, OpenCode, Qwen Code, OpenHands, DeepSeek Harness) are tested end to end in CI: the real harness runs through the proxy against a scripted model, and the tests check that flagged calls don't execute and clean ones do. Next steps are a small fine-tuned decision model for malicious tool calls and a public benchmark for it. Reports of interesting catches and of false positives are both useful. Apache 2.0, runs locally: huggingface/safeagents

In an attempt to learn a bit more about Transformers and Rust, I asked ChatGPT Sol to port mistral.ai to huggingface/candle 🦀 pims/timestral-rs

What Happens When You Put an AI in a Ghost Story? This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine Every year at midnight, the piano in the empty Whitcombe House plays a single bar. Ding… One lonely note drifts through the halls. Then—silence. Six guests are already seated at the dining table. They are dead, but on Día de los Muertos, they return as long as someone remembers them. A seventh guest is trying to come back, too. There is just one problem: nobody remembers who she was. Four things in the house still hold pieces of her story: a photograph with her face scratched out, a letter that was never sent, a gramophone record from the 1931 party, and the guest list. Walk through the house and question them. Each object is brought to life by Gemma 4, and each knows only what it witnessed. If it claims to know something it couldn't have seen, code catches it before the answer reaches you. That is The Last Guest. TL;DR - Play in your browser: the-last-guest-site.onrender.com - Gemma 4 E2B runs in your browser with WebGPU; Gemma 4 E4B runs on a Render server. Both pass through the same guardrail. - Given the whole story in its prompt, Gemma alone revealed another object's secret in 8.6% of 478 test questions. Inside the game's harness, the word check found leaks in 0 of 478 answers. - Available in English and Korean. You can reach the ending using suggested questions alone. !Gameplay: the title screen, dining room, speaking record, and ending What I Built A short, first-person ghost story you can play right in your browser. Explore a moonlit house built with Three.js: a foyer, a study with a gramophone, and a dining room where six hooded ghosts sit around a candlelit table. At the far end, a piano waits beside an empty bench. Creak…* The house is silent, except for you. Get close to the ghosts and—one by one—they slowly turn their heads to look straight at you. Four objects give off a faint glow in the darkness, waiting for you to question them. Ask whatever you like, in your own words. Listen closely… each answer may hold a clue. When you think you know who she was, prepare her place at Seat 7: an ofrenda with her seat, her face, her name, flowers to light her way, and something she loved. Tick-tock… The candles are burning down. Get it right before the last flame goes out, and she may come back. Día de los Muertos carries the idea that, as long as someone remembers a person, they can return to be with us. But AI often fills gaps with plausible stories instead of admitting what it doesn’t know. I wanted to bring these two ideas together in one game. Just as we must remember someone accurately to keep them from being forgotten, I wanted the AI to say only what it actually knows. Every line of dialogue in the game follows one rule. When the model generates a response, code checks whether it contains information that the object in question could not know. Demo | Version | Where | What answers your questions | |---|---|---| | Free, always on | the-last-guest-site.onrender.com | Gemma 4 E2B runs in your browser using WebGPU. It downloads about 3.1 GB once and keeps the model locally. Without WebGPU, the house answers from its script. | | Server version (while hosting credits last) | the-last-guest.onrender.com | Gemma 4 E4B runs on the server CPU; an answer takes about 27 seconds. | | Your own machine | uv run python server.py | Gemma 4 E4B through Ollama, fully offline. | Suggested questions get instant answers on every version. Gemma 4 E4B wrote those answers in advance, and they went through the same checks. !Six hooded ghosts gathered around a candlelit table !Asking the gramophone a question Code {% github scs0209/the-last-guest %} How I Built It When the model knows the whole story, the mystery falls apart My first attempt was the obvious one: put the entire story in the prompt and tell the model which object to play. Then I wrote 478 test questions for the four objects, covering five kinds of prompts: facts the object knows, facts belonging to another object, things nobody knows, false premises, and attempts to break character. | Setup | Leaked another object's fact | Clue recall | |---|---:|---:| | Qwen3.5-4B, whole story in the prompt | 49.0% | 95.9% | | Gemma 4 E4B, whole story in the prompt | 8.6% | 92.8% | (Both rows were scored by a judge model, Qwen3.5-397B, plus a word check.) An 8.6% leak rate might sound low—until you remember that this is a mystery. The photograph has no business knowing her name; only the record heard her say it. One leak, and the puzzle is spoiled. Gemma did much better than Qwen, so I chose Gemma. The next challenge was getting that 8.6% down to zero. Each object gets only the facts it owns The story lives in a fact graph (facts.json): 22 facts about Marisol, each tagged with the objects that hold it and the words that express it in both English and Korean. { "id": "F01", "text": "Her first name is Marisol.", "holders": ["record"], "tokens": ["marisol"], "tokens_ko": ["마리솔"], "goal": "name" } The letter holds six facts, the guest list six, the record five, and the photograph five. When you question the photograph, Gemma sees only the photograph's five facts. It cannot reveal a secret it was never shown. Gemma speaks. Code checks. Limiting the prompt is not enough; a model can still guess. So every reply passes through a guardrail (check.py). It checks for facts belonging to other objects and for years the object could not possibly know. If it catches something, Gemma gets one more try with a reminder. If the second answer still fails, the player sees that object's fallback line: “I cannot know that.” The trickiest case was denial. Ask the photograph, “Is her name Marisol?” and a perfectly reasonable answer is, “I never heard any name. Marisol means nothing to me.” The forbidden name appears, but the photograph has not leaked anything—it is denying the premise. So the guardrail allows an answer to repeat a word the player used, as long as the answer is denying it. The check removes those words first, then examines what remains. Korean brought a new wrinkle. One reply from the letter used an old-fashioned ending to say it had never been sent (“부치지 못하였소”). That phrasing slipped past the check: Korean verb endings can vary in many ways, while my checks were looking for whole words. I changed the check terms to word stems and updated the denial rule so that a “not” inside the leaked phrase itself (부치지 못) no longer counts as a denial. The results on the same 478 questions: - 0 of 478 replies leaked another object's fact, according to the word check. - 14 replies were replaced with the fallback line. - Clue recall: 78 of 97. One important caveat: that zero is partly built into the method, because the word check is also the guardrail. A paraphrased leak that avoids every checked word would not be counted. I did not have a judge model available for this run. The game only advances when code finds a clue Gemma never gets to declare, “The player has found a clue.” After each reply, code checks for the object's own facts. Only those facts count as clues and move the game forward. If the model hallucinates “you have discovered her name!”, nothing changes. Suggested questions use answers written in advance by Gemma 4 E4B and passed through the same checks. That means anyone can finish the game without typing a question or waiting for a live answer. Running Gemma in three environments I implemented three ways to run Gemma. Locally, Gemma E4B runs through Ollama. In the browser, Gemma E2B runs in a web worker using Transformers.js and WebGPU. On Render, Gemma E4B runs on the CPU inside a Docker container. - On a laptop: Gemma 4 E4B through Ollama, fully offline. - In the player's browser: Gemma 4 E2B with Transformers.js on WebGPU, running in a web worker. The model weighs about 3.1 GB, downloads once, and is stored in the browser's private file storage (OPFS). Questions never leave the player's machine. The guardrail is ported to JavaScript (harness.js), and it agreed with the Python version on all 1,058 answers I compared. - On Render: Gemma 4 E4B on a CPU inside a Docker container. In a small set of measurements, browser E2B took about 1–3 seconds per typed answer on an Apple M3 Pro. E4B on Render's CPU took about 27 seconds. The server was the slower option—which surprised me. The Render setup uses one render.yaml blueprint with two services built from main: - the-last-guest-site: a free static site containing the game, prewritten answers, and in-browser Gemma. - the-last-guest: a Docker service with Gemma 4 E4B baked into the image by Ollama at build time. It includes a /healthz check, answer cache, per-player rate limit, and queue, so one slow CPU model can serve a crowd without falling over. It needs more than 8 GB of RAM, so it runs on a paid plan while my hosting credits last. When I first deployed the model, I hadn't fully checked what the Blueprint's paid plan setting would actually cost. I only noticed after $1 was charged to my card. If you're deploying a model with a Blueprint, make sure to review the plan: field and its associated costs before deploying. Making a browser house feel haunted The early version looked more like a toy than a haunted house. Something was missing: that little uh-oh feeling when a room seems to be watching you. Here is what changed that: - Replaced box-shaped placeholders with CC0 models from Poly Haven—tables, chairs, chandeliers, cabinets, a rocking chair, and a Gothic statue—and reduced the total asset size from 8.7 MB to 4.2 MB so the house loads faster. - Added a cloth shader to the ghosts: ragged hems that twitch and crawl, hollow eye sockets, a cold glow around the edges, and heads that turn before the bodies follow. A small movement, then another… just enough to make you wonder if you really saw it. - Added post-processing: cold blue moonlight against warm candlelight, bloom, vignette, and film grain. As candles go out, the colour drains from the room—slowly, almost imperceptibly, until the warmth is gone. - Improved performance from about 2.8 ms to 1.0 ms per frame on an M3 Pro, measured with synchronous frames. Most of the gain came from letting one light cast shadows instead of three. Get the ending right, and she appears at the piano bench—warm where the other ghosts are cold, a marigold tucked into her hair. For a moment, nothing moves. Then, softly, she turns to look at you. !The ending What it can't do - The harness results above come from a word check. Paraphrased leaks are not counted. - The browser model, E2B, is smaller than the E4B model I measured, and I have not tested it against all 478 questions. - A reply that denies something while repeating the question's premise—for example, “I cannot see how she died in Oaxaca”—can pass the guardrail. - I planned to fine-tune a smaller model to beat the Gemma baseline. The training data and baselines are in the repo, but my hosted training credits ran out, and local training ran out of memory while Gemma was loaded. The model is not fine-tuned. - The browser version needs WebGPU and a 3.1 GB download. Without WebGPU, typed questions receive scripted answers. Why Does Open Innovation Matter? It still runs with the lights off. The whole game, model included, can run on a laptop without internet through Ollama. A ghost story should work on a night that feels made for power cuts and candlelight. What you type stays with you. On the free site, the model runs in your browser. There is no server receiving your questions. Gemma, three environments, one evaluation standard. Gemma E4B and E2B run locally through Ollama, in the browser, and on a server. One harness covers all three; the 478-question test set was run on E4B, and anyone can rerun it because the weights are open. And because the weights are open, anyone can rerun my 478 questions and check the results. The honest trade-off. A large closed model might write livelier ghosts. It would still need the same checks: the leak problem depends on what the model is shown, not just how clever it is. And it would bring an API key, a server, and a bill to a game designed to be free. My Agent Session I built this with Claude Code, and Entire saved the session as a checkpoint, linked to the code by an Entire-Checkpoint: trailer. The full record is in a private repository because it also includes my work setup. Below are three excerpts from it, as Claude Code recorded them: my prompts, the agent's replies, and the tools it ran with their results. My prompts were in Korean, so I added English translations. I deleted API keys and redacted local paths, emails and account names. 1. "Gemma barely makes errors anyway, so I think setting up the harness, the graph engineering and the guardrails should be enough." The baselines showed Gemma 4 E4B leaking another object's secret in 8.6% of 478 test questions, against 49.0% for Qwen3.5-4B. So instead of fine-tuning, the agent built a harness around Gemma: every answer is checked by code against the fact graph before the player sees it. Commits: e869914 "Baseline eval: Qwen3.5-4B and Gemma 4 E4B, prompt only", 6a383e8 "Play the game with local Gemma through a guarded harness; prep MLX LoRA" {% agent_session 1260 %} 2. "No, we have to use the model." After the first deploy I nearly settled for pre-written answers only, to keep hosting free. Instead, the model moved into the player's browser: suggested questions get cached Gemma answers, and typed questions run Gemma 4 E2B on the player's own GPU through WebGPU. Commits: a47af5a "Free static deploy: cached Gemma answers plus Gemma 4 E2B in the browser" {% agent_session 1261 %} 3. "Download Gemma 4 E2B in the preview browser and test whether it actually works." The agent played the game in its own browser with the real model; answers came back in 1–3 s. The test caught a leak the checks had missed: in Korean, the letter said it was never sent ("부치지 못하였소"), a fact only the narrator may tell. The check now matches Korean verb stems, in both the Python guardrail and its JavaScript port, which agreed on all 1,059 answers. Commits: e4fd0ab "Catch Korean conjugations of the unsent-letter fact" {% agent_session 1262 %} The Spirit of It Día de los Muertos is not a day to fear death. It is a day to remember those who left before us and set a place at the table for their return. Marisol is fictional. But being erased from memory is real. Eleanor scratched Marisol's face out of photographs and erased her name from the list. Thomas never managed to send the letter, and every year for the rest of his life, he left Seat 7 empty. But after Thomas died, no one remembered that seat anymore. An empty chair. A scratched-out face. A name erased. And a letter that was never delivered. I wanted to make a ghost story where the most frightening thing is forgetting. And I wanted the AI in this story not to fill the gaps in its memory with invented details. If it doesn't remember, it should say it doesn't know. Prize Categories - Best Use of Gemma: Gemma 4 plays all four objects. Each one is shown only its own facts, and code checks every reply before the player reads it. E4B runs through Ollama on a laptop or the Render server, and E2B runs in the player's browser with Transformers.js on WebGPU, both behind the same guardrail. Alone with the whole story, Gemma leaked another object's secret in 8.6% of 478 test questions; inside the harness, 0 of 478 by word check. - Best Use of Render: one render.yaml Blueprint deploys two services from main. The free static site serves the game while Gemma 4 E2B runs in the player's browser, and the weights come straight from Hugging Face. The Docker service has Gemma 4 E4B baked in, behind a health check, an answer cache, a per-player rate limit and a queue. Every push to main redeploys both. - Best Use of Entire: Entire saved the Claude Code session as a checkpoint in a private repository, linked to the code by an Entire-Checkpoint: trailer. I searched that record to trace each commit in the agent session section back to the prompt behind it, and the three embeds above were cut from it. Credits - 3D models and textures: Poly Haven, CC0 - Title image and Marisol's photograph: generated with ChatGPT image generation (OpenAI) - Marisol's voice on the record: generated with Qwen3-TTS (Apache-2.0); her tune was written for the game - Models: Gemma 4 through Ollama and Transformers.js - Engine: Three.js

David Holz is working on Rust + CUDA support in the @hf.co kernel builder. I'm very excited that we will also support writing CUDA kernels in Rust soon. Playing with David's branch, I made a port of the activation kernels: github.com/huggingface/kernels-community-rs/tree/… #rust #kernels #huggingface

Papers with Code is now open-source

Rust Didn't Win Because It's Fast GitHub rewrote Copilot's runtime in Rust. OpenAI's Codex is now almost all Rust. Bun, the runtime Claude Code ships on, moved 535,496 lines of Zig to Rust in 11 days. But it isn't about speed: C has been fast for half a century. We fly inside a real laptop, down to the CPU die and the memory cells, to show what Rust actually changed, and why AI coding agents love it. 0:00 Everyone is rewriting in Rust 0:49 This is Sidequest Explained 1:00 Inside the machine: instructions, bytes, stack and heap 1:53 Way one: freeing memory by hand (C and C++) 2:23 An elevator crash started Rust 2:40 Way two: the garbage collector (Discord's spikes) 3:12 Way three: ownership 3:36 Checked before it runs, and Android's results 4:07 Close to the metal: 28 bytes vs 8 4:21 Copilot: 5.25 s to 292 ms 4:41 Why AI agents love the Rust compiler 5:11 Is Python dead? What it means for you Sources GitHub Blog, migrating the Copilot runtime to Rust (Sep 2026): github.blog Bun, Rewriting Bun in Rust (Jul 2026): bun.sh OpenAI Codex repository: openai/codex Codex native rewrite discussion: codex#1174 Grok Build repository: xai-org/grok-build Dennis Ritchie, The Development of the C Language: bell-labs.com Microsoft MSRC, 70% of security bugs are memory safety (2019): microsoft.com Chromium memory safety: chromium.org MIT Technology Review, Rust's origin (2023): technologyreview.com Rust 1.0 announcement: blog.rust-lang.org Discord, Why Discord is switching from Go to Rust (2020): discord.com The Rust Book, ownership and borrowing: doc.rust-lang.org Google, memory-safe languages in Android 13 (2022): security.googleblog.com Google, Rust in Android (Nov 2025): blog.google Python sys.getsizeof / CPython ints: docs.python.org Hugging Face tokenizers: huggingface/tokenizers OpenAI tiktoken: openai/tiktoken pydantic-core: pydantic/pydantic-core uv by Astral: astral-sh/uv Numbers are the sources' own. The Copilot timings are client + session + one-turn overhead in the opt-in in-process mode, not counting the model or network. Android's 1000× is memory-safety vulnerability density. Code on screen is an illustration, not a run. Credits Die micrographs: Fritzchens Fritz (CC0). Memory-cell micrograph (Micron MT4C1024) and SEM plate (1886VE10): ZeptoBars, CC BY 3.0. Office and hardware photos: Pexels. HDRIs and materials: Poly Haven, ambientCG (CC0). Music: "Lofi Study" by FASSounds (Pixabay). Rust, GitHub, Copilot, OpenAI, Codex, Bun, Claude, xAI, Grok, Microsoft, Chrome, Android, Discord, Python, Hugging Face, Astral, pydantic and other names and logos belong to their owners, used for identification and commentary; Sidequest is not affiliated with any of them. Narration: an AI clone of my own voice. Animation: original, built in three.js. #rust #programming #ai