im still trying to build mutli model but i cant make it work, then combine to mobile Ai...
im missing a way to build, and to teach my Ai... Im Beginner
i have online source for old books and pdf, i want to digitalize.
im using LmStudio
lfm2-2.6b-uncensored-x64-i1
gemma-3-4b-it-heretic-uncensored-abliterated-extreme-i1
huihui-ai.deepseek-r1-distill-qwen-7b-abliterated
(Downloading) mradermacher : Qwen2.5 Coder Scholar 7B Abliterated MFANN Slerp Unretrained GGUF Q4_K_M
abbdd
A0bd
·
AI & ML interests
i got inspired by J.A.R.V.I.S.
Recent Activity
replied to
Parveshiiii's
post
about 10 hours ago
Hey everyone!
We’re excited to introduce our new Telegram group: https://t.me/XenArcAI
This space is built for **model builders, tech enthusiasts, and developers** who want to learn, share, and grow together. Whether you’re just starting out or already deep into AI/ML, you’ll find a supportive community ready to help with knowledge, ideas, and collaboration.
💡 Join us to:
- Connect with fellow developers and AI enthusiasts
- Share your projects, insights, and questions
- Learn from others and contribute to a growing knowledge base
👉 If you’re interested, hop in and be part of the conversation: https://t.me/XenArcAI
replied to
Parveshiiii's
post
about 10 hours ago
Hey everyone!
We’re excited to introduce our new Telegram group: https://t.me/XenArcAI
This space is built for **model builders, tech enthusiasts, and developers** who want to learn, share, and grow together. Whether you’re just starting out or already deep into AI/ML, you’ll find a supportive community ready to help with knowledge, ideas, and collaboration.
💡 Join us to:
- Connect with fellow developers and AI enthusiasts
- Share your projects, insights, and questions
- Learn from others and contribute to a growing knowledge base
👉 If you’re interested, hop in and be part of the conversation: https://t.me/XenArcAI
replied to
Parveshiiii's
post
about 10 hours ago
Hey everyone!
We’re excited to introduce our new Telegram group: https://t.me/XenArcAI
This space is built for **model builders, tech enthusiasts, and developers** who want to learn, share, and grow together. Whether you’re just starting out or already deep into AI/ML, you’ll find a supportive community ready to help with knowledge, ideas, and collaboration.
💡 Join us to:
- Connect with fellow developers and AI enthusiasts
- Share your projects, insights, and questions
- Learn from others and contribute to a growing knowledge base
👉 If you’re interested, hop in and be part of the conversation: https://t.me/XenArcAI
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replied to
Parveshiiii's
post
about 10 hours ago
replied to
Parveshiiii's
post
about 10 hours ago
if you interested we can set video call meeting
replied to
Parveshiiii's
post
about 10 hours ago
im trying to teach my ai about topics, only ChatGpt can discuss properly...
i got may books pdf i tried uploading 1 by 1 to local model, but its not a fix, Ai go hallucinate
its new topic, new logic, ...
replied to
unmodeled-tyler's
post
about 11 hours ago
can you help me in my dataset
A0bd/Forbidden_and_Obscure
reacted to
Parveshiiii's
post with ➕
about 11 hours ago
Post
3396
Hey everyone!
We’re excited to introduce our new Telegram group: https://t.me/XenArcAI
This space is built for **model builders, tech enthusiasts, and developers** who want to learn, share, and grow together. Whether you’re just starting out or already deep into AI/ML, you’ll find a supportive community ready to help with knowledge, ideas, and collaboration.
💡 Join us to:
- Connect with fellow developers and AI enthusiasts
- Share your projects, insights, and questions
- Learn from others and contribute to a growing knowledge base
👉 If you’re interested, hop in and be part of the conversation: https://t.me/XenArcAI
We’re excited to introduce our new Telegram group: https://t.me/XenArcAI
This space is built for **model builders, tech enthusiasts, and developers** who want to learn, share, and grow together. Whether you’re just starting out or already deep into AI/ML, you’ll find a supportive community ready to help with knowledge, ideas, and collaboration.
💡 Join us to:
- Connect with fellow developers and AI enthusiasts
- Share your projects, insights, and questions
- Learn from others and contribute to a growing knowledge base
👉 If you’re interested, hop in and be part of the conversation: https://t.me/XenArcAI
- 11 replies
replied to
Parveshiiii's
post
about 11 hours ago
can you help me in my dataset
A0bd/Forbidden_and_Obscure
reacted to
davidmezzetti's
post with 🧠
about 11 hours ago
Post
2917
🧬⚕️🔬 Encoding the World's Medical Knowledge into 970K! We're excited to release this new series of vector embeddings models for medical literature based on our recent BERT Hash work.
And you read it right, we're talking 970,000 parameters for a surprisingly strong performing model. Enjoy!
https://huggingface.co/blog/neuml/biomedbert-hash-nano
And you read it right, we're talking 970,000 parameters for a surprisingly strong performing model. Enjoy!
https://huggingface.co/blog/neuml/biomedbert-hash-nano
reacted to
ibragim-bad's
post with 🔥
about 11 hours ago
Post
290
🎄 67,074 Qwen3-Coder OpenHands trajectories + 2 RFT checkpoints.
We release: 67,000+ trajectories from 3,800 resolved issues in 1,800+ Python repos.
About 3x more successful trajectories and 1.5x more repos than our previous dataset.
Trajectories are long: on average 64 turns, up to 100 turns and 131k context length.
> RFT on this data, SWE-bench Verified:
Qwen3-30B-Instruct: 25.7% → 50.3% Pass@1.
Qwen3-235B-Instruct: 46.2% → 61.7% Pass@1.
Also strong gains on SWE-rebench September.
> We also did massive evals.
We run OpenHands with 100 and 500 turns.
We compare models under both limits.
We run on SWE-bench Verified and several months of SWE-rebench.
!!! We also check tests written by the models.
We measure how often tests are correct.
We check how often the final patch passes its own tests.
This gives a pool of tests for verifiers and auto graders.
> Fully permissive licenses
Dataset and models: https://huggingface.co/collections/nebius/openhands-trajectories
Blog post: https://nebius.ai/blog/posts/openhands-trajectories-with-qwen3-instruct
We release: 67,000+ trajectories from 3,800 resolved issues in 1,800+ Python repos.
About 3x more successful trajectories and 1.5x more repos than our previous dataset.
Trajectories are long: on average 64 turns, up to 100 turns and 131k context length.
> RFT on this data, SWE-bench Verified:
Qwen3-30B-Instruct: 25.7% → 50.3% Pass@1.
Qwen3-235B-Instruct: 46.2% → 61.7% Pass@1.
Also strong gains on SWE-rebench September.
> We also did massive evals.
We run OpenHands with 100 and 500 turns.
We compare models under both limits.
We run on SWE-bench Verified and several months of SWE-rebench.
!!! We also check tests written by the models.
We measure how often tests are correct.
We check how often the final patch passes its own tests.
This gives a pool of tests for verifiers and auto graders.
> Fully permissive licenses
Dataset and models: https://huggingface.co/collections/nebius/openhands-trajectories
Blog post: https://nebius.ai/blog/posts/openhands-trajectories-with-qwen3-instruct
-
- 2 replies
replied to
ibragim-bad's
post
about 11 hours ago
can you help me in my dataset
A0bd/Forbidden_and_Obscure
replied to
ronantakizawa's
post
about 11 hours ago
can you help me in my dataset
A0bd/Forbidden_and_Obscure
reacted to
ronantakizawa's
post with 😎
about 11 hours ago
Post
5280
Thank you
@clem
(Co-Founder & CEO of Hugging Face) for sharing my dataset on X / Twitter!
ronantakizawa/github-top-developers
#github #dataset
ronantakizawa/github-top-developers
#github #dataset
-
-
- 4 replies
replied to
kanaria007's
post
about 11 hours ago
can you help me in my dataset
A0bd/Forbidden_and_Obscure
reacted to
kanaria007's
post with 🧠
about 11 hours ago
Post
233
✅ New Article: *Hardware Paths for Structured Intelligence* (Draft v0.1)
Title:
🧩 From CPUs to SI-GSPU: Hardware Paths for Structured Intelligence
🔗 https://huggingface.co/blog/kanaria007/hardware-paths-for-si
---
Summary:
Most “AI hardware” is built for dense matrix math. But real-world intelligence systems bottleneck elsewhere: **semantic parsing, structured memory, governance checks, auditability, and evaluation loops** — the parts that turn models into safe, resilient systems.
This article maps the gap clearly, and sketches how a future **SI-GSPU class accelerator** fits: not “a better GPU,” but a co-processor for **semantics + governance runtime**.
> GPUs carry the models.
> S
I-GSPU carries the rules that decide when models are allowed to act.
---
Why It Matters:
• Explains *why* “more GPU” doesn’t fix governance-heavy AI stacks
• Identifies what to accelerate: semantic transforms, memory ops, coverage/metrics, effect ledgers
• Shows how to build **SI-GSPU-ready** systems *today* on conventional clouds — without a rewrite later
• Keeps performance numbers explicitly **illustrative**, avoiding spec-washing
---
What’s Inside:
• Bottleneck taxonomy: where CPUs melt when you implement SI-Core properly
• Accelerator landscape (GPU/TPU/FPGA/DPU) vs. SI workloads
• What SI-GSPU would accelerate — and what it explicitly should *not*
• Determinism + audit chains + attestation requirements for governance-critical acceleration
• A staged roadmap: software-only → targeted offloads → semantic-fabric clusters
• A toy TCO intuition (shape, not pricing guidance)
---
📖 Structured Intelligence Engineering Series
A non-normative hardware guide: how to layer Structured Intelligence onto today’s compute, and where specialized silicon actually changes the economics.
Title:
🧩 From CPUs to SI-GSPU: Hardware Paths for Structured Intelligence
🔗 https://huggingface.co/blog/kanaria007/hardware-paths-for-si
---
Summary:
Most “AI hardware” is built for dense matrix math. But real-world intelligence systems bottleneck elsewhere: **semantic parsing, structured memory, governance checks, auditability, and evaluation loops** — the parts that turn models into safe, resilient systems.
This article maps the gap clearly, and sketches how a future **SI-GSPU class accelerator** fits: not “a better GPU,” but a co-processor for **semantics + governance runtime**.
> GPUs carry the models.
> S
I-GSPU carries the rules that decide when models are allowed to act.
---
Why It Matters:
• Explains *why* “more GPU” doesn’t fix governance-heavy AI stacks
• Identifies what to accelerate: semantic transforms, memory ops, coverage/metrics, effect ledgers
• Shows how to build **SI-GSPU-ready** systems *today* on conventional clouds — without a rewrite later
• Keeps performance numbers explicitly **illustrative**, avoiding spec-washing
---
What’s Inside:
• Bottleneck taxonomy: where CPUs melt when you implement SI-Core properly
• Accelerator landscape (GPU/TPU/FPGA/DPU) vs. SI workloads
• What SI-GSPU would accelerate — and what it explicitly should *not*
• Determinism + audit chains + attestation requirements for governance-critical acceleration
• A staged roadmap: software-only → targeted offloads → semantic-fabric clusters
• A toy TCO intuition (shape, not pricing guidance)
---
📖 Structured Intelligence Engineering Series
A non-normative hardware guide: how to layer Structured Intelligence onto today’s compute, and where specialized silicon actually changes the economics.
-
- 1 reply
reacted to
dhruv3006's
post with 😎
about 11 hours ago
Post
1637
Hey folks 👋
We’re experimenting with a new response panel layout and would love your feedback.We’re testing a more focused experience:
- Only one response section open at a time (instead of multiple)
- The response body now takes up most of the vertical space, making it easier to read and inspect
The goal is simple: reduce clutter and keep the response as the main focus.
That said, we know many developers are comfortable with the classic layout (Postman / Bruno-style), where multiple sections can stay open at once.What would you prefer?
- A new, focused single-section layout
- The classic multi-section layout
- A toggle that lets you choose between both?
Download Voiden here :https://voiden.md/download
We’re experimenting with a new response panel layout and would love your feedback.We’re testing a more focused experience:
- Only one response section open at a time (instead of multiple)
- The response body now takes up most of the vertical space, making it easier to read and inspect
The goal is simple: reduce clutter and keep the response as the main focus.
That said, we know many developers are comfortable with the classic layout (Postman / Bruno-style), where multiple sections can stay open at once.What would you prefer?
- A new, focused single-section layout
- The classic multi-section layout
- A toggle that lets you choose between both?
Download Voiden here :https://voiden.md/download
reacted to
MonsterMMORPG's
post with 🔥
about 11 hours ago
Post
3152
Qwen Image Edit 2511 Free and Open Source Crushes Qwen Image Edit 2509 and Challenges Nano Banana Pro : https://www.youtube.com/watch?v=YfuQuOk2sB0
Full tutorial link > https://www.youtube.com/watch?v=YfuQuOk2sB0
Full HF article here : https://huggingface.co/blog/MonsterMMORPG/qwen-image-edit-2511-free-and-open-source-crushes
Qwen Image Edit 2511 model just published and it is literally competing against Nano Banana Pro at image editing tasks. With native whopping 2560x2560 pixels image output capability and with only 12 steps it is next level. With our installers and specially made Quant FP8 Scaled model, you can run this amazing beast even as low as 6 GB GPUs. In this tutorial, I have compared Qwen Image Edit 2511 with previous successor model Qwen Image 2509 with 12 different unique and hard prompts and cases. Everything is step by step explained and provided.
Here check some comparison images
Full tutorial link > https://www.youtube.com/watch?v=YfuQuOk2sB0
Full HF article here : https://huggingface.co/blog/MonsterMMORPG/qwen-image-edit-2511-free-and-open-source-crushes
Qwen Image Edit 2511 model just published and it is literally competing against Nano Banana Pro at image editing tasks. With native whopping 2560x2560 pixels image output capability and with only 12 steps it is next level. With our installers and specially made Quant FP8 Scaled model, you can run this amazing beast even as low as 6 GB GPUs. In this tutorial, I have compared Qwen Image Edit 2511 with previous successor model Qwen Image 2509 with 12 different unique and hard prompts and cases. Everything is step by step explained and provided.
Here check some comparison images
- 5 replies
replied to
danielhanchen's
post
about 11 hours ago
can you help me in my dataset
A0bd/Forbidden_and_Obscure
reacted to
danielhanchen's
post with ❤️
about 11 hours ago
Post
3394
You can now run GLM-4.7, the new 355B parameter SOTA model on your local device (128GB RAM).✨
The model achieves SOTA performance on coding, agentic and chat benchmarks.
GGUF: unsloth/GLM-4.7-GGUF
Guide: https://docs.unsloth.ai/models/glm-4.7
The model achieves SOTA performance on coding, agentic and chat benchmarks.
GGUF: unsloth/GLM-4.7-GGUF
Guide: https://docs.unsloth.ai/models/glm-4.7
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- 2 replies
Testing LFM2-2.6B-Exp on Differential Equations
❤️
1
2
#1 opened 1 day ago
by
Javedalam
Can support using huihui-ai/Huihui-GLM-4.6V-Flash-abliterated in ollama?
3
#1 opened about 17 hours ago
by
zhaoxiaozhao07
can you help me in my dataset
A0bd/Forbidden_and_Obscure