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
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

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content_slideconfidence0.95 - Text source: agent_vision.
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Slide text:
Open-source

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content_slideconfidence0.98 - Text source: agent_vision.
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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

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content_slideconfidence0.96 - 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

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content_slideconfidence0.97 - Text source: agent_vision.
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Hugging Face Hub
Home for the open-source machine learning community: share & discover models, datasets, apps, connect with the community and more!

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content_slideconfidence0.98 - 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

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content_slideconfidence0.98 - 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

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content_slideconfidence0.98 - Text source: agent_vision.
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Slide text:
compare providers across models

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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

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rapidocr-live/bright-screen/contrast. - OCR decision: ready — Code and configuration content is small and better suited to OCR than direct transcription.
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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

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content_slideconfidence0.98 - Text source: advanced OCR
rapidocr-live/bright-screen/contrast. - OCR decision: ready — Dense mixed text and code block are OCR-suitable.
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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

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content_slideconfidence0.95 - Text source: advanced OCR
rapidocr-live/bright-screen/opencv-adaptive. - OCR decision: ready — Product UI screenshot contains dense small text; OCR is appropriate.
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New! Hub hosts your agent traces
oher Talu Uonres Untute Uamsts: Datasets 21 a Fker by rume Ful-tet search,:, 1l Sort: Trendng
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》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?

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Slide text:
Just upload your sessions from:
~/.claude/projects
~/.codex/sessions
~/.pi/agent/sessions
Nothing else needed!

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content_slideconfidence0.98 - Text source: advanced OCR
rapidocr-live/bright-screen/opencv-adaptive. - OCR decision: ready — product UI screenshot with many small labels and model rows
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tip: find models supported by local apps
Hsin Isks Litrarirs Urpuges Lloemit (ther 0 Models 474 Tul et verch. y oun+ '+1 Sort, Trending.
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tertmce Prerlders Select alt α unsloth/GLH-5.1-GGUF T Tta Gmrmtion - Tb. Updsted I dsy ao · a i1.y - O t unsloth/Q=en3.S-358-A3n-GGUF F tratrTento-Tent · 4 Jso - Updrted Hes· ±L4cy. O Ta
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hf.co/models → other → apps

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content_slideconfidence0.98 - 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

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content_slideconfidence0.99 - 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

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content_slideconfidence0.99 - Text source: none.
- OCR decision: ready — Dense small chat screenshot, code block, and multi-column annotations.

- Recreated text/layout view: open HTML recreation
- AI slide classifier:
content_slideconfidence0.99 - 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
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+ $ltg-1srge.: s1.s0/bour. osch, so $24/bour. for 16
+ Estimated wall-clock: about 56.4 hours. + Total: sbout s1,353
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Hidden Non-Slide Evidence
- `slide-009.jpg` —
speaker_stageconfidence0.96; Camera shot of speaker on stage with the slide only partially visible behind. - `slide-017.jpg` —
speaker_stageconfidence0.98; speaker on stage with partial slide and sponsor logos - `slide-018.jpg` —
speaker_stageconfidence0.98; speaker on stage with partial slide and sponsor logos
Classification audit: raw/sources/slide-ai-classification/dense/OV56RddyFuU/audit.json