Taming Long-form Text-to-Speech
Paper โข 2609.16989 โข Published โข 1
AI & ML interests
Building interactive demos to scikit-learn examples ๐งก
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EduardoPachecoย
authored a
paper 10 days ago
Post
2317
Anthropic gave me six months of Claude Max 20x through the Claude for Open Source program, granted based on my Hugging Face work. Thank you 
Anthropic for supporting open source.
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
https://github.com/Nymbo/Markdown-Minimap โ issues and PRs welcome.
So far I've been pointing it at Markdown Minimap, an Obsidian plugin that adds a scrollable IDE-style minimap to your notes. This week I've been clearing a backlog of user-reported issues on it, with Claude often handling them end to end.
https://github.com/Nymbo/Markdown-Minimap โ issues and PRs welcome.
Q-bertย
authored a
paper about 2 months ago
suayptalhaย
submitted a
paper to Daily Papers about 2 months ago
suayptalhaย
authored a
paper about 2 months ago
Post
6028
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
- 6 replies
suayptalhaย
authored a
paper 3 months ago
pankajpandey-devย
posted an update 3 months ago
Post
4149
๐ฎ๐ณ Qwen3.5-9B Hindi Instruct โ 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 โ then code-switches in the answer. I fine-tuned it to close the think block instantly and reply in pure, native Hindi.
โ Model (16-bit): pankajpandey-dev/qwen3.5-9b-hindi-instruct
โ GGUF (Q4/Q5/Q8): pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF
โ 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 โ 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 โ this one keeps answers detailed and native.
Part of my weekly ๐ฎ๐ณ Hindi LLM Series. Feedback welcome ๐
#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 โ then code-switches in the answer. I fine-tuned it to close the think block instantly and reply in pure, native Hindi.
โ Model (16-bit): pankajpandey-dev/qwen3.5-9b-hindi-instruct
โ GGUF (Q4/Q5/Q8): pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF
โ 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 โ 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 โ this one keeps answers detailed and native.
Part of my weekly ๐ฎ๐ณ Hindi LLM Series. Feedback welcome ๐
#Hindi #IndicNLP #Qwen #GGUF #LocalLLM #Unsloth
- 4 replies
Shrijanagainย
posted an update 3 months ago
Post
310
Welcome Researcher and Developers!
SKT AI Labs, we are pushing the boundaries of AI architecture and researchโand today, we are thrilled to open our doors to the global research community!
โWe warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.
โ๐งช What You Can Explore:
We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.
---------- CHECK OUT:
SPACE : SKT-NRS/RD
EXPERIMENT : https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1
โ๐ค Your Feedback Shapes the Future :
โIf it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.
โIf it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.
โWe firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!
You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.
โLet's innovate and build together! ๐ก
SKT AI Labs, we are pushing the boundaries of AI architecture and researchโand today, we are thrilled to open our doors to the global research community!
โWe warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.
โ๐งช What You Can Explore:
We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.
---------- CHECK OUT:
SPACE : SKT-NRS/RD
EXPERIMENT : https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1
โ๐ค Your Feedback Shapes the Future :
โIf it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.
โIf it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.
โWe firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!
You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.
โLet's innovate and build together! ๐ก
pankajpandey-devย
posted an update 3 months ago
Post
7860
๐ฎ๐ณ New in my Hindi LLM Series: Gemma-4 E4B, fine-tuned for Hindi โ and it runs on your laptop's CPU.
I fine-tuned Google's new Gemma-4 E4B on ~10k Hindi instruction pairs (AI4Bharat: anudesh + dolly) using Unsloth + LoRA, on a single L4 GPU.
Then I ran an honest side-by-side eval: base Gemma-4 vs my fine-tune, across 25 Hindi prompts. The results were interesting ๐
โ My fine-tune is more concise โ ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay.
โ Pure native Hindi โ base keeps slipping into English ("เคธเคเคคเฅเคฒเคฟเคค เคเคนเคพเคฐ (Eat a Balanced Diet)", "เคคเคพเคฐเคพ (Star)"). My fine-tune stays in clean Hindi.
โ Tighter instruction-following โ ask for a "short message" and it gives one, not a menu of options.
โ๏ธ And to be honest: base Gemma-4 is more detailed and comprehensive. I didn't build a "smarter" model โ I built a focused, Hindi-native, edge-friendly one that runs as a 5GB GGUF (Q4) on CPU.
๐ Try it:
Live demo (CPU): pankajpandey-dev/gemma-4-e4b-hindi-demo
GGUF (Ollama/llama.cpp): pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF
16-bit model: pankajpandey-dev/gemma-4-e4b-hindi-instruct
Built with @unsloth ยท Data by @ai4bharat ๐
#Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
I fine-tuned Google's new Gemma-4 E4B on ~10k Hindi instruction pairs (AI4Bharat: anudesh + dolly) using Unsloth + LoRA, on a single L4 GPU.
Then I ran an honest side-by-side eval: base Gemma-4 vs my fine-tune, across 25 Hindi prompts. The results were interesting ๐
โ My fine-tune is more concise โ ask for "3 tips" and it gives exactly 3. Base writes a 1,200-character essay.
โ Pure native Hindi โ base keeps slipping into English ("เคธเคเคคเฅเคฒเคฟเคค เคเคนเคพเคฐ (Eat a Balanced Diet)", "เคคเคพเคฐเคพ (Star)"). My fine-tune stays in clean Hindi.
โ Tighter instruction-following โ ask for a "short message" and it gives one, not a menu of options.
โ๏ธ And to be honest: base Gemma-4 is more detailed and comprehensive. I didn't build a "smarter" model โ I built a focused, Hindi-native, edge-friendly one that runs as a 5GB GGUF (Q4) on CPU.
๐ Try it:
Live demo (CPU): pankajpandey-dev/gemma-4-e4b-hindi-demo
GGUF (Ollama/llama.cpp): pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF
16-bit model: pankajpandey-dev/gemma-4-e4b-hindi-instruct
Built with @unsloth ยท Data by @ai4bharat ๐
#Hindi #LLM #Gemma #Unsloth #IndicNLP #GGUF
- 12 replies
Shrijanagainย
posted an update 3 months ago
Post
295
๐ Big News for the AI Community! ๐ฅ
Weโre excited to release NRS_QWEN_MYTHOS_1M โ a powerful reasoning model built on Qwen 3.5 9B!
At SKT AI LABS, weโve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.
๐ฅ Why This Model is a Game-Changer:
โ 100x Reasoning Capacity โ Exceptional deep logical thinking and complex problem-solving
โ 1 Million Token Context โ Perfect for massive codebases, long documents, and multi-turn agentic workflows
โ Advanced Thinking Mode โ Native <think> tags for true step-by-step Chain-of-Thought reasoning
โ Tool-Use Ready โ Optimized for Python execution, Web Search, and self-correction
โ Blazing Fast โ Runs smoothly on consumer GPUs like RTX 3090/4090
Technical Highlights:
Base: Qwen 3.5 9B
Tuning: NRS-specific high-quality reasoning data
Context: 1M Tokens (YaRN Scaling)
License: NRS DOCS
Whether youโre a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning โ this model is built for you.
๐ Try it now on Hugging Face:
SKT-NRS/NRS_QWEN_MYTHOS_1M
Drop a comment: What will you build with it first? ๐
#AI #OpenSource #LLM #Qwen #ReasoningModel #HuggingFace #NewModel #AICommunity
Weโre excited to release NRS_QWEN_MYTHOS_1M โ a powerful reasoning model built on Qwen 3.5 9B!
At SKT AI LABS, weโve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.
๐ฅ Why This Model is a Game-Changer:
โ 100x Reasoning Capacity โ Exceptional deep logical thinking and complex problem-solving
โ 1 Million Token Context โ Perfect for massive codebases, long documents, and multi-turn agentic workflows
โ Advanced Thinking Mode โ Native <think> tags for true step-by-step Chain-of-Thought reasoning
โ Tool-Use Ready โ Optimized for Python execution, Web Search, and self-correction
โ Blazing Fast โ Runs smoothly on consumer GPUs like RTX 3090/4090
Technical Highlights:
Base: Qwen 3.5 9B
Tuning: NRS-specific high-quality reasoning data
Context: 1M Tokens (YaRN Scaling)
License: NRS DOCS
Whether youโre a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning โ this model is built for you.
๐ Try it now on Hugging Face:
SKT-NRS/NRS_QWEN_MYTHOS_1M
Drop a comment: What will you build with it first? ๐
#AI #OpenSource #LLM #Qwen #ReasoningModel #HuggingFace #NewModel #AICommunity
eienmojikiย
posted an update 3 months ago
Post
311
Hi everyone,
I've created a Gradio space for embedding and extracting invisible watermarks in images:
๐ eienmojiki/blind-watermark-studio
It supports hiding text, images, and bit arrays using the DWT-DCT-SVD algorithm.
Credits:
- Original library: https://github.com/guofei9987/blind_watermark
- Author: Guo Fei
:).
I've created a Gradio space for embedding and extracting invisible watermarks in images:
๐ eienmojiki/blind-watermark-studio
It supports hiding text, images, and bit arrays using the DWT-DCT-SVD algorithm.
Credits:
- Original library: https://github.com/guofei9987/blind_watermark
- Author: Guo Fei
:).
pankajpandey-devย
posted an update 4 months ago
Post
922
๐ฎ๐ณ Gemma-3-1B Hindi Instruct โ a Hindi LLM that runs fully offline, anywhere.
Last week I shipped Qwen3-4B Hindi. This week I went the other direction: how tiny can a useful Hindi model get? So I fine-tuned Gemma-3-1B on quality-filtered Hindi instruction data and shipped the full GGUF ladder.
โ Fine-tune (16-bit): pankajpandey-dev/gemma-3-1b-hindi-instruct
โ GGUF (Q4/Q5/Q8): pankajpandey-dev/gemma-3-1b-hindi-instruct-GGUF
Runs in Ollama, llama.cpp, and LM Studio. The Q4_K_M is just 806 MB โ runs on CPU, a cheap laptop, even a Raspberry Pi.
What I tried this round: chrF-filtered the training data to drop weak translations, and used response-only loss so the model learns how to answer, not how to repeat prompts.
Honest note: at 1B, Hindi fluency is strong but coherence is bounded by size โ it's a lightweight/edge experiment, not a 4B replacement. Gemma-3-4B Hindi is next.
Part of my Hindi LLM Series โ openly-licensed Indic models for local & edge use. Feedback welcome ๐
#Hindi #IndicNLP #GGUF #LocalLLM #Gemma #EdgeAI
Last week I shipped Qwen3-4B Hindi. This week I went the other direction: how tiny can a useful Hindi model get? So I fine-tuned Gemma-3-1B on quality-filtered Hindi instruction data and shipped the full GGUF ladder.
โ Fine-tune (16-bit): pankajpandey-dev/gemma-3-1b-hindi-instruct
โ GGUF (Q4/Q5/Q8): pankajpandey-dev/gemma-3-1b-hindi-instruct-GGUF
Runs in Ollama, llama.cpp, and LM Studio. The Q4_K_M is just 806 MB โ runs on CPU, a cheap laptop, even a Raspberry Pi.
What I tried this round: chrF-filtered the training data to drop weak translations, and used response-only loss so the model learns how to answer, not how to repeat prompts.
Honest note: at 1B, Hindi fluency is strong but coherence is bounded by size โ it's a lightweight/edge experiment, not a 4B replacement. Gemma-3-4B Hindi is next.
Part of my Hindi LLM Series โ openly-licensed Indic models for local & edge use. Feedback welcome ๐
#Hindi #IndicNLP #GGUF #LocalLLM #Gemma #EdgeAI
EduardoPachecoย
authored 3
papers 4 months ago
Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild
Paper โข 2604.07354 โข Published โข 1
WhisperKit: On-device Real-time ASR with Billion-Scale Transformers
Paper โข 2507.10860 โข Published โข 2
SDBench: A Comprehensive Benchmark Suite for Speaker Diarization
Paper โข 2507.16136 โข Published โข 1
pankajpandey-devย
posted an update 4 months ago
Post
15011
๐ฎ๐ณ Qwen3-4B Hindi Instruct v2 โ a Hindi LLM that runs on your own machine
Most strong Hindi-capable models are either huge or cloud-only. I wanted one that's small enough to run locally but actually follows instructions in Hindi โ so I fine-tuned Qwen3-4B on 10K Hindi instruction pairs and shipped it with a full GGUF quant ladder.
โ Fine-tune (16-bit): huggingface.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2
โ GGUF (Q4/Q5/Q8): huggingface.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF
Runs in Ollama, llama.cpp, and LM Studio. The Q4_K_M is just 2.5 GB โ fits comfortably on a laptop, CPU or GPU.
Part of my Hindi LLM Series โ building openly-licensed Indic models for local and edge use. More coming (Gemma next). Feedback welcome ๐
#Hindi #IndicNLP #GGUF #LocalLLM #Qwen
Most strong Hindi-capable models are either huge or cloud-only. I wanted one that's small enough to run locally but actually follows instructions in Hindi โ so I fine-tuned Qwen3-4B on 10K Hindi instruction pairs and shipped it with a full GGUF quant ladder.
โ Fine-tune (16-bit): huggingface.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2
โ GGUF (Q4/Q5/Q8): huggingface.co/pankajpandey-dev/Qwen3-4B-Hindi-Instruct-v2-GGUF
Runs in Ollama, llama.cpp, and LM Studio. The Q4_K_M is just 2.5 GB โ fits comfortably on a laptop, CPU or GPU.
Part of my Hindi LLM Series โ building openly-licensed Indic models for local and edge use. More coming (Gemma next). Feedback welcome ๐
#Hindi #IndicNLP #GGUF #LocalLLM #Qwen
- 4 replies
pankajpandey-devย
posted an update 4 months ago
Post
702
๐ฎ๐ณ Just shipped: MiniCPM5-1B-Hindi-Instruct (+ GGUF quants)
First Hindi instruction-tuned fine-tune of OpenBMB's brand-new MiniCPM5-1B (released this week).
Trained with Unsloth + LoRA (r=32) on AI4Bharat's anudesh + dolly Hindi splits โ ~4k high-quality examples, 2 epochs on a single T4 in 60 minutes.
๐ Model (16-bit + LoRA adapter):
pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct
๐ฆ GGUF quants for llama.cpp / Ollama / LM Studio:
pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct-v1-GGUF
5 quant levels โ from Q3_K_M (~560 MB, runs on a Raspberry Pi) to Q8_0 (~1.2 GB, near-lossless). Q4_K_M is the recommended default.
Part of my ongoing ๐ฎ๐ณ Hindi LLM Series โ bringing strong open-source LLMs to Indian languages.
#Hindi #IndicNLP #MiniCPM5 #LoRA #Unsloth #GGUF #llamacpp #Ollama #LocalLLM
First Hindi instruction-tuned fine-tune of OpenBMB's brand-new MiniCPM5-1B (released this week).
Trained with Unsloth + LoRA (r=32) on AI4Bharat's anudesh + dolly Hindi splits โ ~4k high-quality examples, 2 epochs on a single T4 in 60 minutes.
๐ Model (16-bit + LoRA adapter):
pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct
๐ฆ GGUF quants for llama.cpp / Ollama / LM Studio:
pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct-v1-GGUF
5 quant levels โ from Q3_K_M (~560 MB, runs on a Raspberry Pi) to Q8_0 (~1.2 GB, near-lossless). Q4_K_M is the recommended default.
Part of my ongoing ๐ฎ๐ณ Hindi LLM Series โ bringing strong open-source LLMs to Indian languages.
#Hindi #IndicNLP #MiniCPM5 #LoRA #Unsloth #GGUF #llamacpp #Ollama #LocalLLM
pankajpandey-devย
posted an update 4 months ago
Post
2706
๐งฌ Just uploaded K-quants of Carbon-3B for llama.cpp users!
@HuggingFaceBio released the original GGUF in bf16 only โ so I added the full quant ladder for CPU/edge inference:
โข Q2_K โ 1.4 GB
โข Q3_K_M โ 1.8 GB
โข Q4_K_M โ 2.1 GB โญ
โข Q5_K_M โ 2.4 GB
โข Q6_K โ 2.7 GB
โข Q8_0 โ 3.5 GB
๐ pankajpandey-dev/Carbon-3B-GGUF
Now you can generate DNA sequences on your laptop. Needs a llama.cpp build with PR #23410 (HybridDNATokenizer support).
Huge thanks to the HuggingFaceBio team for the original model ๐
#GGUF #llamacpp #genomics #DNA
@HuggingFaceBio released the original GGUF in bf16 only โ so I added the full quant ladder for CPU/edge inference:
โข Q2_K โ 1.4 GB
โข Q3_K_M โ 1.8 GB
โข Q4_K_M โ 2.1 GB โญ
โข Q5_K_M โ 2.4 GB
โข Q6_K โ 2.7 GB
โข Q8_0 โ 3.5 GB
๐ pankajpandey-dev/Carbon-3B-GGUF
Now you can generate DNA sequences on your laptop. Needs a llama.cpp build with PR #23410 (HybridDNATokenizer support).
Huge thanks to the HuggingFaceBio team for the original model ๐
#GGUF #llamacpp #genomics #DNA
pankajpandey-devย
posted an update 4 months ago
Post
281
Just released Qwen3-0.6B fine-tuned on Hindi instruction data ๐ฎ๐ณ
โ Full model: pankajpandey-dev/Qwen3-0.6B-Hindi-Instruct-v1
โ GGUF versions (Q2/Q4/Q5/Q8): pankajpandey-dev/Qwen3-0.6B-Hindi-Instruct-v1-GGUF
Smallest Hindi-capable GGUF โ runs on any laptop at 0.37GB.
Next: v2 with more data, better responses.
#Hindi #LLM #GGUF #OpenSource
โ Full model: pankajpandey-dev/Qwen3-0.6B-Hindi-Instruct-v1
โ GGUF versions (Q2/Q4/Q5/Q8): pankajpandey-dev/Qwen3-0.6B-Hindi-Instruct-v1-GGUF
Smallest Hindi-capable GGUF โ runs on any laptop at 0.37GB.
Next: v2 with more data, better responses.
#Hindi #LLM #GGUF #OpenSource