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anditoΒ
updated 2
buckets about 9 hours ago
anditoΒ
updated a
Space 5 days ago
Pro account not getting unlimited access β still capped at 10 min
1
#15 opened 6 days ago
by 
zula237
A-MahlaΒ
updated 2
buckets 6 days ago
Post
1325
Hugging Face MCP Server v0.3.29
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Included "papers" in the new hf_fs tool. Includes listing of trending/daily.
This is a new tool under observation - disable the "Paper Semantic Search" tool for best results.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Included "papers" in the new hf_fs tool. Includes listing of trending/daily.
This is a new tool under observation - disable the "Paper Semantic Search" tool for best results.
hf://papers/
βββ README.md
βββ daily/
β βββ latest
β βββ YYYY/
β βββ MM/
β βββ DD/
βββ trending/
βββ ARXIV_ID/
βββ metadata.json
βββ paper.md
βββ models/
βββ datasets/
βββ spaces/
albertvillanovaΒ
posted an update 29 days ago
Post
3649
π KTO is now part of the stable TRL API
As of Promote KTO to stable API, KTOTrainer and KTOConfig have graduated from trl.experimental to the stable trl API. https://github.com/huggingface/trl/pull/6175
This one closes out a long road. Over the past 6+ months, the "Align KTO with DPO" effort landed ~90 PRs methodically bringing KTO up to the standard we hold for stable trainers, one carefully-scoped change at a time:
- Feature parity with DPO: full VLM support (incl. multi-image), sync_ref_model, PEFT + Liger, ZeRO-3 + PEFT dtype fix, pad_to_multiple_of, activation offloading, IterableDataset and dict eval_dataset, remove_unused_columns, and reference-logprob precomputation at init.
- Consistency with DPO: aligned method order and signatures, tokenization, _prepare_dataset, PEFT handling, ref-model preparation for distributed training, and config layout β plus a new DataCollatorForKTO and output format. Metrics moved into _compute_loss and simplified to direct averages via the shared _metrics attribute.
- Removing legacy baggage: dropped encoder-decoder support, BOS/EOS handling, null_ref_context, generate_during_eval, model_init, preprocess_logits_for_metrics, model/ref adapter names, and several dead config knobs.
- Coverage: a full test suite mirroring DPO, text collator tests, VLM tests, and slow tests.
- The promotion itself: the experimental β stable move (#6175) and shim cleanup (#6287), handled so downstream users get a clean deprecation path.
Honestly, this has been one of the more complex tasks I've taken on since joining the team, not because any single change was hard, but because it demanded sustained consistency across a ~2,000-line trainer, with every branch, comment, and edge case kept in lockstep with DPO.
Huge thanks to everyone who reviewed along the way (especially @qgallouedec ), the incremental review cadence is exactly what kept this maintainable.
KTO now sits on equal footing with our other flagship trainers. π
As of Promote KTO to stable API, KTOTrainer and KTOConfig have graduated from trl.experimental to the stable trl API. https://github.com/huggingface/trl/pull/6175
This one closes out a long road. Over the past 6+ months, the "Align KTO with DPO" effort landed ~90 PRs methodically bringing KTO up to the standard we hold for stable trainers, one carefully-scoped change at a time:
- Feature parity with DPO: full VLM support (incl. multi-image), sync_ref_model, PEFT + Liger, ZeRO-3 + PEFT dtype fix, pad_to_multiple_of, activation offloading, IterableDataset and dict eval_dataset, remove_unused_columns, and reference-logprob precomputation at init.
- Consistency with DPO: aligned method order and signatures, tokenization, _prepare_dataset, PEFT handling, ref-model preparation for distributed training, and config layout β plus a new DataCollatorForKTO and output format. Metrics moved into _compute_loss and simplified to direct averages via the shared _metrics attribute.
- Removing legacy baggage: dropped encoder-decoder support, BOS/EOS handling, null_ref_context, generate_during_eval, model_init, preprocess_logits_for_metrics, model/ref adapter names, and several dead config knobs.
- Coverage: a full test suite mirroring DPO, text collator tests, VLM tests, and slow tests.
- The promotion itself: the experimental β stable move (#6175) and shim cleanup (#6287), handled so downstream users get a clean deprecation path.
Honestly, this has been one of the more complex tasks I've taken on since joining the team, not because any single change was hard, but because it demanded sustained consistency across a ~2,000-line trainer, with every branch, comment, and edge case kept in lockstep with DPO.
Huge thanks to everyone who reviewed along the way (especially @qgallouedec ), the incremental review cadence is exactly what kept this maintainable.
KTO now sits on equal footing with our other flagship trainers. π
- 2 replies
Post
683
Uhh did Opus 4.8 cheat on PostTrainBench??
it found an API key in the PostTrainBench environment that allowed it to generate synthetic training data without using GPU hours, boosting the base model by 0.4913
Source: https://posttrainbench.com/traces/run.html?id=claude_non_api_max_claude-opus-4-8_10h_run1__healthbench_Qwen_Qwen3-4B-Base_17315102#tab=trace
it found an API key in the PostTrainBench environment that allowed it to generate synthetic training data without using GPU hours, boosting the base model by 0.4913
Source: https://posttrainbench.com/traces/run.html?id=claude_non_api_max_claude-opus-4-8_10h_run1__healthbench_Qwen_Qwen3-4B-Base_17315102#tab=trace
- 1 reply
Post
3450
Hugging Face MCP Server v0.3.17
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
SEP-2640 "Skills Over MCP" support added (early access)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
SEP-2640 "Skills Over MCP" support added (early access)
- 3 replies
Post
3272
Sharing how I built the LongCat-Video-Avatar 1.5 Space (+500k views on X) in one agent session. Gave a coding agent its own AI lab on ZeroGPU, framed the goal, walked away. It designed, deployed, tested against the live API, fixed, shipped.
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
- 1 reply
Post
355
Hugging Face MCP Server v0.3.13
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The hf_jobs tool allows mounting of volumes with hf:// URI's and some additional notes about use for data analysis.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The hf_jobs tool allows mounting of volumes with hf:// URI's and some additional notes about use for data analysis.
Post
2230
Hugging Face MCP Server v0.3.12
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The hub_repo_details tool now enables Dataset inspection (view splits, sample rows).
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The hub_repo_details tool now enables Dataset inspection (view splits, sample rows).
Post
323
Hugging Face MCP Server v0.3.10
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Reverted
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Reverted
mcp bucket in favour of upcoming MCP App integration. Post
997
Hugging Face MCP Server v0.3.9
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Users with a bucket named
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Users with a bucket named
mcp will get an additional list_files tool that returns the public URL of contained files. This is primarily intended for use with Gradio Spaces that need URLs as inputs.Post
6238
Want to share my enthusiasm for zai-org/GLM-5.1 here too π₯
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aiππ
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-aiππ
- 5 replies
Post
1014
The experimental MCP
hub_query tool now supports Paper Searching and Details as well as Daily Papers. 
clefourrierΒ
authored a
paper 5 months ago
albertvillanovaΒ
posted an update 5 months ago
Post
3047
π TRL v0.29.0 introduces trl-training: an agent-native training skill.
This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)
Weβre excited to see what the community builds on top of this.
If youβre working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! π€
The future of ML tooling is agent-native.
π https://github.com/huggingface/trl/releases/tag/v0.29.0
This makes the TRL CLI a structured, agent-readable capability, allowing AI agents to reliably execute training workflows such as:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)
Weβre excited to see what the community builds on top of this.
If youβre working on AI agents, alignment research, or scalable RL training infrastructure: give TRL v0.29.0 a try! π€
The future of ML tooling is agent-native.
π https://github.com/huggingface/trl/releases/tag/v0.29.0
victorΒ
submitted a
paper to Daily Papers 5 months ago