--- title: "Dense Slides: Self-Training Agents: Hermes Agent, HF Traces, Skills, MCP & Finetuning — Merve Noyan, Hugging Face" category: "slides" video_id: "OV56RddyFuU" sourceLabels: ["Captured video frames", "Local OpenCV slide-region detection"] --- # Dense Slides: Self-Training Agents: Hermes Agent, HF Traces, Skills, MCP & Finetuning — Merve Noyan, Hugging Face ## Source Video [Self-Training Agents: Hermes Agent, HF Traces, Skills, MCP & Finetuning — Merve Noyan, Hugging Face](https://www.youtube.com/watch?v=OV56RddyFuU) ## Method This deck is slide-only. The existing captured video frame set supplies candidate frames, then local OpenCV rejects sponsor/title/speaker-only frames, crops visible slide surfaces, deduplicates, and saves the cropped slide images. ## Cropped Visible Slides ![[assets/dense-slides/OV56RddyFuU/slide-001.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-001.html) - AI slide classifier: `content_slide` confidence `0.95` - Text source: agent_vision. - OCR decision: ready — Product/UI screenshot with small embedded text and dense interface elements. Slide text: > Open-source ![[assets/dense-slides/OV56RddyFuU/slide-002.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-002.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. Slide text: > Why does open-source matter? > Absolute control over models > Cost reduction in multipliers > Further customize, shrink models depending on your needs > Guaranteed privacy for the end-user, enable on-device/in-browser uses, data doesn’t go to another server ![[assets/dense-slides/OV56RddyFuU/slide-003.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-003.html) - AI slide classifier: `content_slide` confidence `0.96` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Dense chart with many small labels and score annotations. Slide text: > Artificial Analysis Index > Artificial Analysis Intelligence Index by Open Weights / Proprietary 2 B 28 of 472 modeks > Artiflclal Analysls Intelligence Indcx v4.0 incorporates 10 evaluatlors: GDpval-AA, rr-Bench Telecom, Terminal- Barch Hard, SclCode, AA-LCR, AA-Omnlsclerca, IFBench, Humanity's Lest Exam, GPQA Dlamond, CritPt + Add modet from specifk provlder > Proprletary Open Welghts 8080808 A Artlflclw Autytts > 5.0 49 49 Br ![[assets/dense-slides/OV56RddyFuU/slide-004.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-004.html) - AI slide classifier: `content_slide` confidence `0.97` - Text source: agent_vision. Slide text: > Hugging Face Hub > Home for the open-source machine learning community: share & discover models, datasets, apps, connect with the community and more! ![[assets/dense-slides/OV56RddyFuU/slide-005.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-005.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Text-heavy slide with code block and multiple model names is best handled by OCR. Slide text: > models → agents, serve locally > Agentic LLMs (thinking + tool calling): gpt-oss, Gemma-4, Minimax M2.7, GLM-5, Nemotron3-Super > Agentic vision models (thinking + CUA): Qwen3.5 > (Alibaba), Kimi-K2.5 > mlx_ lm.generate --prompt *How tall ts Mt Everest?" > vllm serve Qwen/Qwen3-8B # then query with QpenAI Completion > llama-server -n model.gguf --port 8080 ![[assets/dense-slides/OV56RddyFuU/slide-006.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-006.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Leaderboard table and model scores are dense and OCR-suitable. Slide text: > compare open models > Datasets: @ScaleAl/SWE-bench_Pro like: 78 Follow @ ScaleA: 284 > Benchmark Modalities: θ Text Formats: + parquet Size:<1K Libraries: & Datasets pandas. Polars. +1 > t Dataset card: 田 Data Studio Flles and versions 1 K xot: O Community 0 > . Leaderboard ioficisl Benchmrk ① Leam more Epedmentsl > Taslc SWE Bench Pro > HODEL SCORE > Φ Oza1-org/GLH-5.1:0 58.4 > 2 HiniMaxAI/niniMax-H2.5 T 55.4 > 3. Q coonshotai/KimI-K2.S n' 50.7 > 4 s Quen/Qnen3-Coder-Hext n: source 44.3 > 5 1 Qon/Qwan3-Coder-4888-A35B-Instruct n! tource 38.7 > V Show all 14 modcls ![[assets/dense-slides/OV56RddyFuU/slide-007.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-007.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: agent_vision. - OCR decision: ready — Dense comparison table with small text and metrics; OCR is more suitable than manual transcription. Slide text: > compare providers across models ![[assets/dense-slides/OV56RddyFuU/slide-008.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-008.html) - AI slide classifier: `content_slide` confidence `0.97` - Text source: agent_vision. Slide text: > The Hub meets your agent > MCP Server > plug Hub in to your favorite LLM > HF CLI > search models, manage datasets & buckets, launch Spaces, run jobs. > Skills > empower your agent with Skills of HF ecosystem > Local Agents > Run full coding agents with llama.cpp, Pi & more ![[assets/dense-slides/OV56RddyFuU/slide-010.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-010.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: advanced OCR `rapidocr-live/bright-screen/contrast`. - OCR decision: ready — Code and configuration content is small and better suited to OCR than direct transcription. Slide text: > Local coding agents Pi consumes llama.cpp Ilama-agent: agent loop baked into llama.cpp as binary 000 #add your local model $npm install -g @mariozechner/pi-coding-agent "providers":{ "llama-cpp":{ "baseUrl":"http://localhost:8080/v1", install pi configure > /build/bin/llama-agent -hf [HUB_ID] "models":[ "apikey":"none", "api":"openai-completions”, > (agent) pi API llama.cpp (server) "id":"Qwen3.5-122B-A10B-GGUF" > I your files, terminal, etc. > $pi #start pi start coding ![[assets/dense-slides/OV56RddyFuU/slide-011.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-011.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: advanced OCR `rapidocr-live/bright-screen/contrast`. - OCR decision: ready — Dense mixed text and code block are OCR-suitable. Slide text: > Local self-improving agents > Hermes Agent > self-improvement is baked in: the approach as a reusable across sessions after the task, the agent saves 'skill" and persists memory hermes chat --provider hf #use with inference providers HF_TOKEN=hf_... #usewith local served endpoint hermes config Set OPENAI BASE URL http://localhost:8080/v1 (llama.cpp & friends) > Providers or serve LM locally Integrated with Inference hermes config set OPENAI API KEY dummy name hermes config set LLM_MoDEL your-model- > #start chatting > hermes chat ![[assets/dense-slides/OV56RddyFuU/slide-012.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-012.html) - AI slide classifier: `content_slide` confidence `0.95` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Product UI screenshot contains dense small text; OCR is appropriate. Slide text: > New! Hub hosts your agent traces > oher Talu Uonres Untute Uamsts: Datasets 21 a Fker by rume Ful-tet search,:, 1l Sort: Trendng > Hodalles 08 Audo: Documnt t + n6ttT - Lt9 a + or yrp eptpdn: el #: m badlogicganos/pi-aono + 0xSoro/pi-sossions +in· LpLta3dyxo· b%·±17. 5 > D Geosptiul 9 Tet Tmeseries. a voto + Tnct· Updattdadp ato. a7- ±5a. 04 m badlogicganos/pi-diff-review jediscti/agent-traces-smival + Trca: Updaed about ghour ao · D T · A ll: 01 > Sue(rour) 0 lhoestq/ag+nt-trac+s-+xaaple + Yaces: Updrid&day. sgo. D4· ±loe· 1 virhnx9e/vtcade-sassion± t o · rst + - alr tnou re noqr ptirpdn > Fo.k: + Trce · Updsttd sbout 1 boar sgo - a 1i · ± 1o · 1 noikapy/fxKobolds 食 tnoe- Updrted 6 dys ago: u 12· 主H cfahlgreni/egent-sessicns-list > Boe! = ov + prqut optimired parquet:. Bb tragetolder + Tnkes · Updned J dsy ago · D&· L39 mcfahlgron1/pi-diff-review + Tncgs + Updsted 1 day ago: a 15 + ±44 LaisEckart/appioraltostt-lava-sessions > 》MTOH B soundfoldr: ■tit + Trces + Updated 1dsys hro - ct 42? + t(o1 m cfahlgrenl/pi-nono-fresh + Tract4 - Updsted 1 days sgo - B 1 - ± 3s M dongxx11e4/Basoline_featboach > Type Benchmark 1( ( Irxes x:± Trces·Updsted2 dnysato·02·上32 I davanstrien/pi-tracos M davanstrien/pi-traco-parsor-stssions ± Trx++Updt*d2dyt +go: aI+ ±2? ![[assets/dense-slides/OV56RddyFuU/slide-013.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-013.html) - AI slide classifier: `content_slide` confidence `0.99` - Text source: agent_vision. Slide text: > Just upload your sessions from: > ~/.claude/projects > ~/.codex/sessions > ~/.pi/agent/sessions > > Nothing else needed! ![[assets/dense-slides/OV56RddyFuU/slide-014.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-014.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — product UI screenshot with many small labels and model rows Slide text: > tip: find models supported by local apps > Hsin Isks Litrarirs Urpuges Lloemit (ther 0 Models 474 Tul et verch. y oun+ '+1 Sort, Trending. > Liu.coo G L Sudoe LnM ow Thinp O Hayr lop t9c 0 - ht1 F: otr kp t pvrpdn · 9st r + ju1-orl+lrul unsloth/geana-4-268-A48-it-GGuf D unsloth/gema-4-318-1t-GGUF P muItrl·to-Tit + d 1la - Updted sbout 18 hout ato · ± Ael. 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O 10 unsloth/Quon3.5-1228-A1e8-GGUF > hf.co/models → other → apps ![[assets/dense-slides/OV56RddyFuU/slide-015.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-015.html) - AI slide classifier: `content_slide` confidence `0.98` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — dense product screenshot with model card, code, and compatibility details Slide text: > ggml-org/gemma-4-26B-A4B-it-GGUF uke 40 Folow ceml-org 168k > B GGUF: converatlonal > V Modelcard Ie Files and versions i& mt Community I:: Deploy * eUse this model] > Edit model card > gemma-4-26B-A4B-it-GGUF 67,075 Downlosds Iast monh > Recommended way to run this model:1lana-scrvor -hf ggal-org/gcnma-4-26B-A4B-it-GGUF GGUF ① Model size 25B params Architecture: gemma4 () Chat template > Then, access http://ocalhost:8080 Hardware compatibility P L4 (24 GB) x1: > 4-bit Q4_K_M:16.8 GB > 8-bit α Q8_0 126.9 G8: > 16-bit β F16 150.5 GB ![[assets/dense-slides/OV56RddyFuU/slide-016.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-016.html) - AI slide classifier: `content_slide` confidence `0.99` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — slide contains code snippets and multi-column text Slide text: > lets any coding agent HF CLI Skill # Add CLI skill globally hf skills add --claude --global > search models, manage datasets, # 0r per-project hf skills add --claude > run jobs (and more!) launch Spaces # Works with other agents too hf skills add --codex hf skills add hf skills add --opencode --cursor ![[assets/dense-slides/OV56RddyFuU/slide-019.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-019.html) - AI slide classifier: `content_slide` confidence `0.99` - Text source: none. - OCR decision: ready — Dense small chat screenshot, code block, and multi-column annotations. ![[assets/dense-slides/OV56RddyFuU/slide-020.jpg]] - Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/dense/OV56RddyFuU/slide-020.html) - AI slide classifier: `content_slide` confidence `0.99` - Text source: advanced OCR `rapidocr-live/bright-screen/opencv-adaptive`. - OCR decision: ready — Dense pricing calculations and screenshot text in small type. Slide text: > Grest Do you hive sn ides of the costs foc runaing 16 GpUs in parstei tor thst long? Use @ Hugglng Face Jobt: > nq Uslng the latest rerifed HF Jobs pricing frorn our erlronmant, l4tsxl ks s1.8e/hour pe GPU. So 16x I4esx1 3 previous mosssges ) → LLM even does napkin calculates running cost for math for the instance and > Aep/169s noqy · +. $2s.te/hour tots the Job > Lyn yyap-its sinou oeioz hoqt ey pmoyt uru tded- ras'iz mm, out 'ehtd yyewlpuea sotl ueuro eu betn + -29.3h case: about s843 + 3+.th cso: sbout $565 16 jobs in paralle, to the rw GpU cost is about: ran you can use Buckets → these Jobs will be re- > For compsrson, 16x sieg-large: b ch+aotr par hour but slomt ottral: rd budget a bit abore rw compute ior shurd skew, slrtup overhead, retrles, trd possble Hub hlceupt + Practicel budgtl sbout s9eo-si,054 fast read-write with hf-mount that allows > + $ltg-1srge.: s1.s0/bour. osch, so $24/bour. for 16 > + Estimated wall-clock: about 56.4 hours. + Total: sbout s1,353 > Tthuo aodtoy> Aryoirw pue ostu, 4oq dq proyt 1' txhm xgt tsn:outt cn cirt uoepuauuoou Au os ### Hidden Non-Slide Evidence - [`slide-009.jpg`](/assets/dense-slides/OV56RddyFuU/slide-009.jpg) — `speaker_stage` confidence `0.96`; Camera shot of speaker on stage with the slide only partially visible behind. - [`slide-017.jpg`](/assets/dense-slides/OV56RddyFuU/slide-017.jpg) — `speaker_stage` confidence `0.98`; speaker on stage with partial slide and sponsor logos - [`slide-018.jpg`](/assets/dense-slides/OV56RddyFuU/slide-018.jpg) — `speaker_stage` confidence `0.98`; speaker on stage with partial slide and sponsor logos Classification audit: `raw/sources/slide-ai-classification/dense/OV56RddyFuU/audit.json`