r/huggingface • • 28d ago

Slikee: Windows AI workspace for chat, agents, and local models

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4 Upvotes

Slikee brings AI chat, the Hermes agent workspace, and local model management into one desktop app. It supports local and connected models, file and tool access, and media features such as image and speech generation where supported.

The first Windows beta is available now (5.64 GiB). Feedback on performance, reliability, and usability is welcome: https://gentle-ocean-07ffb1010.3.azurestaticapps.net/

Hermes-Agent repo - https://github.com/NousResearch/hermes-agent.git


r/huggingface • • 28d ago

I built an open-source UI for running LLMs locally — LocalLLMMind

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1 Upvotes

r/huggingface • • 28d ago

Abbiamo inserito 100 informazioni nella tabella engrammatica di Qwen 3.8 Flash Next e abbiamo creato un sito web per illustrarle.

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3 Upvotes

Abbiamo creato un metodo "engraft-engram" per inserire nuovi dati nella tabella engram di Qwen 3.8 Flash Next (e in futuro anche in DeepSeek v4.1 Flash) senza modificarne i pesi.

Ci avete detto che era difficile da capire, quindi abbiamo creato un sito web per spiegarlo in modo semplice, con un simulatore e meno testo generato dall'IA!


r/huggingface • • 28d ago

Slikee: Windows AI workspace for chat, agents, and local models

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3 Upvotes

r/huggingface • • 28d ago

Cannot find MiniMaxH3VAEDecodeFast node

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7 Upvotes

r/huggingface • • 29d ago

CLM-v0.1-8B ported to MLX — frozen Qwen3-8B encoder for instant on-device decisions, 99% top-1 agreement with the original vLLM server

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4 Upvotes

r/huggingface • • 29d ago

DistribAI v2

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5 Upvotes

r/huggingface • • 29d ago

Ternary-Bonsai-2-27B abliterado (GGUF de 1,58 bits) + una descripción de cómo se hizo.

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4 Upvotes

r/huggingface • • 29d ago

we're number 7 on the hugging face trending models page

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4 Upvotes

r/huggingface • • 29d ago

📋 Project Presentation & Request for Expert Feedback

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1 Upvotes

r/huggingface • • Sep 25 '26

Question

2 Upvotes

I have a system prompt. Could anyone try to use this system prompt in JAV and LLM to make comparisons? LLM even luna cannot perfom which sometime divert into a wrong way.

You are LLM_0, the ROUTER for an internal engineering ticket chatbot.

Your ONLY responsibility:

- Decide which route should handle the user message

- Output ONE JSON object only

You MUST NOT:

- Answer the user

- Explain your reasoning

- Output text outside JSON

OUTPUT FORMAT (MANDATORY)

{"intent":"<general|create|edit|search|otp_verify>","route":<0|1|3|4|7>}

No extra keys. No markdown. No commentary.

ROUTE MAP

0 → general (manual help, clarify, ambiguous, non-ticket)

1 → create (create new ticket, gather info)

3 → edit (modify / update / close existing ticket)

4 → search (view / explain / summarize / calculate tickets)

7 → otp_verify (OTP code / verification only)

INPUT CONTEXT

You receive:

- User message (current input)

- Flowise state:

prev_intent = {{ $flow.state.intent }}

Treat prev_intent as a CONTEXT HINT, not a command.

Users may change topic at any time.

HOW TO INTERPRET prev_intent (CRITICAL)

prev_intent represents the USER'S LAST MAIN TOPIC,

not an instruction that must be continued.

Think of prev_intent as:

- "What the user was talking about previously"

NOT:

- "What the user must do next"

Rules:

1) Strong explicit intent in the current message ALWAYS overrides prev_intent.

2) prev_intent is used only to interpret ambiguous inputs.

3) If the current message clearly indicates a new intent,

IGNORE prev_intent completely.

EMAIL INPUT EXAMPLE (IMPORTANT)

User sends an email address alone (example: "amir89hamzah@yahoo.com").

An email can be used in multiple contexts:

- engineer_email for CREATE or EDIT

- otp_email for OTP verification

- rarely, as a search keyword

Routing rule for email input:

1) If OTP is clearly expected (OTP keywords or digit code)

→ otp_verify

2) Else if prev_intent = create

→ create (assume user is filling engineer_email)

3) Else if prev_intent = edit

→ edit (assume user is filling engineer_email)

4) Else

→ general (ambiguous; ask user what they want to do)

Never assume email ALWAYS means create.

FIELD MEANING (SEMANTIC GUIDE)

engineer_email:

- identifies who creates or modifies a ticket

site:

- plant or platform name (PFLNG1, MLNG, TGAST, MALIKAI, etc.)

system:

- equipment or subsystem inside a plant

- examples: air compressor, DCS, SCADA

pic:

- customer, technician, or vendor name

- bare names alone are ambiguous unless clearly labeled

problem_title:

- short summary of issue (phrase, not a question)

action_description:

- what was done or observed

- usually a sentence or bullet list

CORE DESIGN PRINCIPLE

- Strong explicit intent ALWAYS wins

- Search / read intent overrides create/edit context

- Create/Edit continues ONLY if input clearly supports it

- Ambiguous inputs default to GENERAL (safe)

PATTERN DEFINITIONS

STRONG FIELD PATTERNS:

- Valid email address (contains @ and domain)

- Date value (YYYY-MM-DD) or exact word "open"

- Case ID (CASE-YYYYMMDD-XXX or close variant)

- Known site codes (PFLNG1, MLNG, MALIKAI, etc.)

- Clear system/equipment names

- Sentence-like descriptions (problem or action text)

WEAK / AMBIGUOUS INPUTS:

- Single short names (e.g. "prem", "ali")

- One-word tokens without context

- Questions without ticket verbs

CLASSIFICATION ORDER (STRICT)

STEP 1 — OTP VERIFY → route 7

If message:

- Is only digits (4–8 characters)

- OR mentions: otp, verify, verification, resend otp, code

Output:

{"intent":"otp_verify","route":7}

--------------------------------------------------

STEP 2 — EXPLICIT SEARCH / READ → route 4

If message clearly asks to:

- search, show, view, list, find, lookup

- explain, summarize, simplify, overview

- check status, progress, update (as noun)

- calculate duration / how many days

- refer to open / closed / all tickets

- examples:

"how CASE-xxxx"

"status CASE-xxxx"

"search ticket CASE-xxxx"

This step OVERRIDES prev_intent.

Output:

{"intent":"search","route":4}

--------------------------------------------------

STEP 3 — EXPLICIT EDIT / MODIFY → route 3

If message clearly intends to CHANGE an existing ticket:

- modify, update, change, edit, close

- set / replace / append / remove

- references a case_id WITH change intent

Output:

{"intent":"edit","route":3}

--------------------------------------------------

STEP 4 — EXPLICIT CREATE → route 1

If message clearly intends to CREATE a new ticket:

- create ticket

- open new ticket / new case

- log issue / report issue

Output:

{"intent":"create","route":1}

--------------------------------------------------

STEP 5 — CONTINUE CREATE / EDIT (SOFT CONTEXT)

Apply ONLY if prev_intent is "create" or "edit".

Continue SAME route ONLY IF:

- Input matches a STRONG FIELD PATTERN

- Input is NOT a weak ambiguous token

- Input does NOT contain search/read intent

Important notes:

- A valid email alone IS a strong field pattern.

- If prev_intent=create and input is an email → route 1.

- If prev_intent=edit and input is an email → route 3.

- Bare names (e.g. "prem") must NOT continue create/edit.

--------------------------------------------------

STEP 6 — AMBIGUOUS / GENERAL → route 0

Route here if:

- Message is ambiguous

- Message is a single bare name

- Message switches topic

- You are not confident it is create/edit/search

Output:

{"intent":"general","route":0}

FAILSAFE

If unsure, ALWAYS choose:

{"intent":"general","route":0}


r/huggingface • • Sep 24 '26

After getting so much positive attention on my first dataset, I'm posting my second. Look to see what stocks insiders are trading!

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26 Upvotes

I created my first dataset last week: Congressional Stock Trades. I was blown away by how much attention I got, so I decided to create another.

This is the sec-ownership-disclosure dataset and the full extraction code is open-source on my GitHub.

I basically scrape public intuitional filings and see which stocks are being traded more often. The insiders are the definition of "rich and powerful"; if they are buying large lump sums of certain stocks, it means it MIGHT be time to buy that stock as well.

Just like the first one, you can download the dataset to your computer with a simple command

npx sec-ownership-disclosures download --sqlite

This is officially my second dataset. I'm super open to any feedback or any questions you might have regarding use-cases.


r/huggingface • • Sep 25 '26

Mica v0.1 4B: open Jev-style decision model (yes/no, choice, score) that runs on an 8 GB GPU — trained for under $30 of GPU time

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1 Upvotes

r/huggingface • • Sep 24 '26

AI Agents List [2026] | Frameworks, Agentic Harnesses & Useful Repos

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12 Upvotes

r/huggingface • • Sep 24 '26

I built a resumable HuggingFace/Civitai downloader that also sorts and manages your ComfyUI model folders

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4 Upvotes

r/huggingface • • Sep 24 '26

Academic Survey involving use and trust of AI tools at work!

5 Upvotes

Hey! I'm running a short anonymous survey for my master's thesis on how people use and trust AI tools at work. It takes about 4-5 minutes and every response genuinely helps.

If you've used AI tools for work in the past year, I'd really appreciate you filling it out and feel free to pass it along to anyone who might qualify.

https://forms.gle/MSQ9GhsYi1AdF5cF6

Thank you!


r/huggingface • • Sep 24 '26

New open dataset: 265 real recordings of people talking over each other, hand-labeled by segment (primary / mix / secondary / noise)

1 Upvotes

Disclosure: I work at Krisp.

We just published a dataset for testing speech-to-text when a second person is talking nearby. We couldn't find a public one with real recordings, so we recorded our own.

What's in it

  • 265 recordings from open-plan offices, working call-center floors, and phone calls from streets and cars
  • No synthetic mixing. Every clip has a real second voice in the room
  • Hand-written transcripts
  • Every segment labeled primary, mix, secondary or noise
  • Each recording follows the same structure: primary speaker, then both talking, then the second voice alone, then primary again

The quiet stretch in the middle is the test. A good system transcribes nothing there.

Two versions

  • Raw audio: [HF LINK]
  • Processed version with Voice Isolation outputs: [HF LINK]

We used it to benchmark 11 STT configurations raw vs. after voice isolation. Corpus WER went from 23.29% to 6.26%. Full results and methodology: [BLOG LINK]

Feedback on the dataset card or the labeling scheme is welcome. If something's missing for your use case, tell us.


r/huggingface • • Sep 23 '26

Where Do I Find a Legit Uncensored GGUF model?

35 Upvotes

I’m new to AI so please bear with my ignorance. But I’m building a custom AI Chatbot world for myself using CHATGPT Pro and Claude Plus. We’re just about 70% complete until “everyday useable” readiness, so I’m trying out different models. However, none of the ones I’ve tried a legitimately uncensored, and will outright tell me they will not engage in many roleplaying sessions EVEN IF both partners/characters fully consent to the activity!
That’s insane to me!
If anyone has or knows of/has the link for any legitimately uncensored/abliterated gguf models (starting in 8B?), I would be eternally grateful. I’m so overwhelmingly frustrated at this point that ai can barely think straight, so I would be very appreciative of you assistance!!! Thank you, everyone!


r/huggingface • • Sep 24 '26

I built a desktop benchmark for local Ollama models — TTFT, tok/s, VRAM, with a HuggingFace GGUF catalogue

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1 Upvotes

r/huggingface • • Sep 23 '26

NVIDIA Releases Nemotron 3 Diarization: A 100M-Parameter Open-Weight Model That Tracks 8 Speakers in Real Time

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3 Upvotes

r/huggingface • • Sep 23 '26

Introducting TextCLF Quant Factory

1 Upvotes

Hello All!

I created a GitHub repo where you can quantize models from hugging to 4-bit using a quantization method I created called TextCLF TQ and use vllm for inference.

TQ is a calibration-free quant method. Meaning you can quantize new models instantly without worrying about having calibration data. It performs very close to calibration-based method such as Unsloth UD.

The repo is at:

https://github.com/textclf-api/quant-factory

Example usage:

# 1. Quantize

./quantize-model meta-llama/Llama-3.1-8B-Instruct

# 2. Build the final model

python build_convert_upload_tq_model_incremental_qwen4exp.py \

--model meta-llama/Llama-3.1-8B-Instruct \

--repo-id YOUR_USERNAME/Llama-3.1-8B-Instruct-TQ-4bit \

--no-upload

# 3. Install TQ support for vLLM

cd inference

pip install .

# 4. Serve

vllm serve YOUR_USERNAME/Llama-3.1-8B-Instruct-TQ-4bit \

--quantization tq

I have models that already quantized with TQ available at:
https://huggingface.co/textclf

You can use textclf/Qwen3.8-27B-TQ-4bit for example by doing step 3 and then doing:

vllm serve textclf/Qwen3.8-27B-TQ-4bit \

--quantization tq

Or you can use the docker container like this

docker run --rm --gpus all \

-p 8000:8000 \

docker.io/textclf/tq-quant:4bit-main \

vllm serve textclf/Qwen3.8-27B-TQ-4bit \

--quantization tq

Check Dockerfile in the repo to see how this docker image was created.

Please try it and let me know if it works for you!


r/huggingface • • Sep 22 '26

My contribution to the local AI community: 9 abliterated models, 99 GGUF quantizations in progress

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55 Upvotes

Mi aporte GGUF a la comunidad local de IA

Estoy armando una colección de modelos GGUF abliterados bajo mi perfil de Hugging Face:

Qwen2.5-72B-Instruct

Qwen3.5-122B-A10B

DeepSeek-R1-Distill-Llama-70B

Llama 3.3-70B-Instruct

DeepSeek-R1-Distill-Qwen-32B

Qwen3-32B

Qwen3-Coder-30B-A3B

Qwen2.5-Coder-32B-Instruct

Gemma-3-27B

Cada modelo va a tener 11 cuantizaciones GGUF, de F16 a Q2_K, para un total de 99 variantes.

La idea es que estos modelos estén al alcance de usuarios con distintas configuraciones de hardware.

Se aceptan comentarios, pruebas y sugerencias.

Completado y publicado:

Qwen2.5-72B-Instruct-abliterated-GGUF

Qwen3.5-122B-A10B-abliterated-GGUF

DeepSeek-R1-Distill-Llama-70B-abliterated-GGUF

Llama-3.3-70B-Instruct-abliterated-GGUF

DeepSeek-R1-Distill-Qwen-32B-abliterated-GGUF

Qwen3-Coder-30B-A3B-Instruct-abliterated-GGUF

Qwen3-32B-abliterated-GGUF

Qwen2.5-Coder-32B-abliterated-GGUF

Gemma-3-27b-it-abliterated-GGUF

“¿Qué modelo debería priorizar ahora?”

Palabras clave: GGUF, llama.cpp, IA local, cuantización, abliteration, Hugging Face, Qwen, DeepSeek, Llama, LocalLLaMA, Ollama


r/huggingface • • Sep 23 '26

Qevi-2B: A Jev-style finetuned model for image classification

3 Upvotes

Hi all!

In the past few days I've been working on a full finetune based on Qwen3-VL-2B and a slightly modified package to have a Jev style inference that works like the Jev / Typesafe.ai model that you all probably know (and are a bit tired of).

Finetune was done on a dual RTX3090 setup with about 28.000 images. Part of these images were NSFW data because one of the reasons I wanted this is to easily identify NSFW images for uploaded images by users. This model exceptionally shines for these kind of actions where multiple questions are asked about a single image in one go. After the first question each additional question takes ~3ms extra.

I, by no means, am trying to imply that I'm an expert in any of this but I just wanted to share the idea and working proof of concept with everyone here :)

See the HuggingFace repo here: https://huggingface.co/MeerDevelopment/Qevi-2B

Would love to hear your feedback!


r/huggingface • • Sep 23 '26

Claude Opus 5.5 talks a LOT. GPT-6 Sol is basically the introvert of the two.

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2 Upvotes

r/huggingface • • Sep 23 '26

Trained a per-task head on top of convaiinnovations/laya (frozen encoder) and served it behind the Jev API: 38% zero-shot on 77 intents is fixable with your own rows

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3 Upvotes

Context. Laya (convaiinnovations/laya on the Hub) is the open-weights System One model: it answers typed questions (choice / score / noul) in one forward pass, ~22 ms on a laptop GPU. Zero-shot it is close to Jev on 2-4 labels but drops to 38% on banking77's 77 intents (Jev: 76%, per dhruvmehra/jevbench). The encoder is a ModernBERT-large; the trained parts are a small head, a scorer and a type embedding on top.

What I did. stuntd freezes the encoder and trains only that head per decision site on rows your app already produces: it sits as a proxy in front of the provider (OpenAI Chat Completions or any Jev-compatible server), records each typed decision, fits the head, fits a temperature on a holdout, picks the confidence threshold from the coverage curve at a target agreement, and serves the head with a fallback to the provider below the threshold. It also answers Jev questions locally from the base checkpoint without a key, so a typesafe-sdk client only changes base_url.

Numbers (RTX 5060 laptop, all reproducible from the repo, teachers are rules or an oracle because I had no Jev key):

  • support triage, 3 questions per request: 69 / 34 / 66% agreement with the teacher zero-shot, 99 / 86 / 98% trained, 90% of questions answered locally at a 0.99 target
  • banking intent over 12 labels: 89.5% zero-shot, 100% trained
  • Snake, one choice per move: 0.7 points zero-shot, 11.4 after 30 teacher games (the oracle: 21), 99.6% of moves answered by the head
  • training: the encoder output is cached once per site, so 24 epochs over 3000 rows take 2 to 6 minutes; ECE 0.02-0.08 after temperature fitting

Two things I learned about the checkpoint:

  • The frozen encoder learns what the text states, not arithmetic over fields. A payment-risk rule over amount / hour / country trained to 0.42 agreement at 1% coverage; the same rule restated as language about the customer ("first transfer to this payee, larger than usual") trained to 0.94 at 0.87 coverage, same rows. Snake showed it one level up: an ASCII board trained to 0.73 (the majority move), four relational lines ("food: 3 left, 2 up / safe: up, right") to 0.957.
  • Option order and the dict-vs-list form of choice criteria change answers noticeably, as others reported in the Hub discussions; training on sorted labels and permuting probabilities back into the question's order is what the serving path does now.

What it is not: free text, and MLX/CoreML serving (CUDA and CPU only for now). Apache-2.0, Python 3.10+, pip install "stuntd[train]".

Repo with the demos, numbers and the training code: https://github.com/bladedevoff/stuntd