Post
27
dropping something intresting
sheet to score convert audio clips to instrumental
Reubencf/Score-Studio
sheet to score convert audio clips to instrumental
Reubencf/Score-Studio
Reuben fernandes PRO
Reubencf
AI & ML interests
LLM
Recent Activity
updated a Space 2 days ago
Reubencf/Portfolio reacted to theirpost with 🔥 4 days ago
dropping something intresting
sheet to score convert audio clips to instrumental
https://huggingface.co/spaces/Reubencf/Score-StudioOrganizations
reacted to their post with 🔥 4 days ago
posted an update 4 days ago
Post
27
dropping something intresting
sheet to score convert audio clips to instrumental
Reubencf/Score-Studio
sheet to score convert audio clips to instrumental
Reubencf/Score-Studio
reacted to ProCreations's post with 🧠 17 days ago
Post
2138
what should i make next?
- 10 replies
reacted to KlondikeDev's post with 🔥 17 days ago
Post
2411
Boris-1.7-D60M-n30M out NOW!
opencerebral/Boris-1.7-D60M-n30M
Following this will be Boris-1.8-D60M-n30M, which will test both the n-gram embeddings AND a new architecture.
Then, Boris-2 will begin training!
opencerebral/Boris-1.7-D60M-n30M
Following this will be Boris-1.8-D60M-n30M, which will test both the n-gram embeddings AND a new architecture.
Then, Boris-2 will begin training!
replied to their post 25 days ago
please do check it out
reacted to their post with 🔥 about 2 months ago
Post
229
Open Video Craft v1.0.2 is live 🎬
I built an open-source, local-first screen recorder and timeline editor for macOS and Windows. Record your screen, camera and audio; trim clips, add subtitles and export without an account.
GitHub + downloads: https://github.com/Reubencfernandes/Open-Video-Craft/releases/tag/v1.0.2
I’d love feedback from the Hugging Face community—especially ideas for useful captioning and AI-assisted editing workflows.
I built an open-source, local-first screen recorder and timeline editor for macOS and Windows. Record your screen, camera and audio; trim clips, add subtitles and export without an account.
GitHub + downloads: https://github.com/Reubencfernandes/Open-Video-Craft/releases/tag/v1.0.2
I’d love feedback from the Hugging Face community—especially ideas for useful captioning and AI-assisted editing workflows.
- 1 reply
posted an update about 2 months ago
Post
229
Open Video Craft v1.0.2 is live 🎬
I built an open-source, local-first screen recorder and timeline editor for macOS and Windows. Record your screen, camera and audio; trim clips, add subtitles and export without an account.
GitHub + downloads: https://github.com/Reubencfernandes/Open-Video-Craft/releases/tag/v1.0.2
I’d love feedback from the Hugging Face community—especially ideas for useful captioning and AI-assisted editing workflows.
I built an open-source, local-first screen recorder and timeline editor for macOS and Windows. Record your screen, camera and audio; trim clips, add subtitles and export without an account.
GitHub + downloads: https://github.com/Reubencfernandes/Open-Video-Craft/releases/tag/v1.0.2
I’d love feedback from the Hugging Face community—especially ideas for useful captioning and AI-assisted editing workflows.
- 1 reply
reacted to their post with 🤗 2 months ago
Post
962
- 2 replies
replied to their post 2 months ago
I would have improved it but the usage gets over fast
reacted to their post with 🔥 2 months ago
reacted to RDTvlokip's post with 👍 3 months ago
Post
2007
I finally changed the architecture of my 15M French LLM. It worked. Then I almost fooled myself about how much and catching that was the real win.
After proving last time that architecture is a threshold, not a lever, I got stubborn: could I change how the model learns? Four honest attempts, Lion, a sharper AdamW β2, multi-token prediction, LayerScale. Four failures. The bottleneck wasn't the learning rule either.
So I changed the shape of the computation instead: loop the same transformer blocks 4×, deeper reasoning, zero added parameters. It beat the baseline on perplexity, the first thing in the whole project to move that number. Then I added my own twist: let each token decide how deep to think, halting on its own entropy.
My first evaluation was spectacular. Coherence up 65%. Hallucinated names down 62%.
It was noise.
Eight prompts, one seed. I re-ran on 50 prompts × 200 tokens and watched the gains shrink to "modest" and on out-of-domain prompts, recurrence actually made things worse. No universal winner. And none of it is new: it's Adaptive Computation Time (2016), the Universal Transformer (2018), and LoopViT (2026), recombined and measured honestly.
The real lesson:
A number from 8 prompts is a rumor. The eval harness that kills your own best result is worth more than the result it kills. Cite your lineage. Stay preliminary until multiple seeds say otherwise.
The three models are live. The write-up is honest about every caveat 👇
🔗 https://huggingface.co/blog/RDTvlokip/teaching-a-15m-french-llm-to-think-deeper
After proving last time that architecture is a threshold, not a lever, I got stubborn: could I change how the model learns? Four honest attempts, Lion, a sharper AdamW β2, multi-token prediction, LayerScale. Four failures. The bottleneck wasn't the learning rule either.
So I changed the shape of the computation instead: loop the same transformer blocks 4×, deeper reasoning, zero added parameters. It beat the baseline on perplexity, the first thing in the whole project to move that number. Then I added my own twist: let each token decide how deep to think, halting on its own entropy.
My first evaluation was spectacular. Coherence up 65%. Hallucinated names down 62%.
It was noise.
Eight prompts, one seed. I re-ran on 50 prompts × 200 tokens and watched the gains shrink to "modest" and on out-of-domain prompts, recurrence actually made things worse. No universal winner. And none of it is new: it's Adaptive Computation Time (2016), the Universal Transformer (2018), and LoopViT (2026), recombined and measured honestly.
The real lesson:
A number from 8 prompts is a rumor. The eval harness that kills your own best result is worth more than the result it kills. Cite your lineage. Stay preliminary until multiple seeds say otherwise.
The three models are live. The write-up is honest about every caveat 👇
🔗 https://huggingface.co/blog/RDTvlokip/teaching-a-15m-french-llm-to-think-deeper
- 6 replies
reacted to their post with 🧠🤯👀 3 months ago
Post
153
Introducing Reubencf/Document_Query
powered by CohereLabs/command-a-plus-05-2026-w4a4 by cohere
just open admin page -> drop in any document -> and ask questions
powered by CohereLabs/command-a-plus-05-2026-w4a4 by cohere
just open admin page -> drop in any document -> and ask questions
- 1 reply
reacted to pankajpandey-dev's post with ❤️🚀🔥 3 months ago
Post
4148
🇮🇳 Qwen3.5-9B Hindi Instruct — it stops thinking in English
Ask base Qwen3.5-9B a question in Hindi and it burns hundreds of tokens thinking in English inside its think block before a single Devanagari word appears — then code-switches in the answer. I fine-tuned it to close the think block instantly and reply in pure, native Hindi.
✅ Model (16-bit): pankajpandey-dev/qwen3.5-9b-hindi-instruct
✅ GGUF (Q4/Q5/Q8): pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF
✅ Try it in the browser: pankajpandey-dev/qwen3.5-9b-hindi-demo
Recipe: Unsloth + LoRA (r=16, response-only loss) on 12.9k Hindi pairs — AI4Bharat anudesh + dolly-hi + wikiHow-hi + Aya Hindi (human-written). The Q4_K_M is 5.4 GB and runs on a plain laptop CPU.
New in this run vs my earlier models: mixed in long-form native sources (wikiHow) after my last eval showed the fine-tune traded detail for conciseness — this one keeps answers detailed and native.
Part of my weekly 🇮🇳 Hindi LLM Series. Feedback welcome 🙏
#Hindi #IndicNLP #Qwen #GGUF #LocalLLM #Unsloth
Ask base Qwen3.5-9B a question in Hindi and it burns hundreds of tokens thinking in English inside its think block before a single Devanagari word appears — then code-switches in the answer. I fine-tuned it to close the think block instantly and reply in pure, native Hindi.
✅ Model (16-bit): pankajpandey-dev/qwen3.5-9b-hindi-instruct
✅ GGUF (Q4/Q5/Q8): pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF
✅ Try it in the browser: pankajpandey-dev/qwen3.5-9b-hindi-demo
Recipe: Unsloth + LoRA (r=16, response-only loss) on 12.9k Hindi pairs — AI4Bharat anudesh + dolly-hi + wikiHow-hi + Aya Hindi (human-written). The Q4_K_M is 5.4 GB and runs on a plain laptop CPU.
New in this run vs my earlier models: mixed in long-form native sources (wikiHow) after my last eval showed the fine-tune traded detail for conciseness — this one keeps answers detailed and native.
Part of my weekly 🇮🇳 Hindi LLM Series. Feedback welcome 🙏
#Hindi #IndicNLP #Qwen #GGUF #LocalLLM #Unsloth
- 4 replies
replied to their post 3 months ago
@CohereLabs