Good work nd nice thing you doing. Lets make AI more capable on somelanguages that sometimes lacks on quality. I made some good Hindi friends on my path on AI.
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liked a model 1 day ago
deepreinforce-ai/Ornith-1.0-9B commentedon an article about 1 month ago
Agentic RL: Token-In, Token-Out Done Right upvoted an article about 1 month ago
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replied to pankajpandey-dev's post about 1 month ago
reacted to pankajpandey-dev's post with ๐ฅ about 1 month ago
Post
14993
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
replied to their post about 2 months ago
Sent 2 more emails to deepseek support today.
reacted to salma-remyx's post with ๐ฅ about 2 months ago
Post
11605
The space of possible improvements for your AI model is large while evaluation is costly.
So I was excited to discover the ICML 2026 paper from Kobalczyk, Lin, Letham, Zhao, Balandat, and Bakshy titled "LILO: Bayesian Optimization with Natural Language Feedback."
The method learns efficiently from expert preferences, balancing exploration and exploitation in a principled way with Bayesian Optimization for expensive-to-evaluate black-box objectives.
Experimenting with the technique, I trained a Gaussian Process proxy model on the implicit preferences in my code repo's commit history at VQASynth.
The result: I used the model's preference scores to re-rank candidate papers recommended based on my interests in spatial reasoning and multimodal data synthesis.
Semantic relevance is a high-recall method for finding arXiv papers personalized to your interests. Adding contributor preferences, extracted from the merge history of your code offers a high-precision filter.
So what's next? I'm using the model to synthesize a larger volume of preference data to finetune an open-weight coding model with DPO and LoRA. Tuning Coding Agents via Implicit Preference Distillation
arXiv: https://arxiv.org/pdf/2510.17671
Substack: https://remyxai.substack.com/p/lilo-and-myx
VQASynth: https://github.com/remyxai/VQASynth
So I was excited to discover the ICML 2026 paper from Kobalczyk, Lin, Letham, Zhao, Balandat, and Bakshy titled "LILO: Bayesian Optimization with Natural Language Feedback."
The method learns efficiently from expert preferences, balancing exploration and exploitation in a principled way with Bayesian Optimization for expensive-to-evaluate black-box objectives.
Experimenting with the technique, I trained a Gaussian Process proxy model on the implicit preferences in my code repo's commit history at VQASynth.
The result: I used the model's preference scores to re-rank candidate papers recommended based on my interests in spatial reasoning and multimodal data synthesis.
Semantic relevance is a high-recall method for finding arXiv papers personalized to your interests. Adding contributor preferences, extracted from the merge history of your code offers a high-precision filter.
So what's next? I'm using the model to synthesize a larger volume of preference data to finetune an open-weight coding model with DPO and LoRA. Tuning Coding Agents via Implicit Preference Distillation
arXiv: https://arxiv.org/pdf/2510.17671
Substack: https://remyxai.substack.com/p/lilo-and-myx
VQASynth: https://github.com/remyxai/VQASynth
- 1 reply
reacted to espejelomar's post with โค๏ธ about 2 months ago
Post
4740
Sharing WorldForge with @abdelstark
It's an open-source Python project for evaluating and replaying robotics and world-model workflows.
The useful part is not only calling a model. WorldForge records the run, validates action shapes, translates outputs into actions, and keeps replay artifacts you can inspect later.
The current demo uses LeRobot + LeWorldModel on PushT through the official loader:
The harness also has replay-only paths for Cosmos-Policy and GR00T-style outputs, so you can inspect the provider contract from saved artifacts without keeping a GPU server online.
Try it:
Repo: https://github.com/AbdelStark/worldforge
Docs: https://abdelstark.github.io/worldforge/
Pre-1.0, MIT, and actively looking for contributors. Good areas:
- robotics provider adapters
- replay artifacts
- eval flows
- docs & first-run demos
Good first issues: https://github.com/AbdelStark/worldforge/contribute
If you're building robot policy evals or model adapters, would love a PR โ or an issue describing what's missing.
It's an open-source Python project for evaluating and replaying robotics and world-model workflows.
The useful part is not only calling a model. WorldForge records the run, validates action shapes, translates outputs into actions, and keeps replay artifacts you can inspect later.
The current demo uses LeRobot + LeWorldModel on PushT through the official loader:
stable_worldmodel.policy.AutoCostModel("pusht/lewm")The harness also has replay-only paths for Cosmos-Policy and GR00T-style outputs, so you can inspect the provider contract from saved artifacts without keeping a GPU server online.
Try it:
pip install worldforge-aiuv run --extra harness worldforge-harness --flow robotics-compareRepo: https://github.com/AbdelStark/worldforge
Docs: https://abdelstark.github.io/worldforge/
Pre-1.0, MIT, and actively looking for contributors. Good areas:
- robotics provider adapters
- replay artifacts
- eval flows
- docs & first-run demos
Good first issues: https://github.com/AbdelStark/worldforge/contribute
If you're building robot policy evals or model adapters, would love a PR โ or an issue describing what's missing.
replied to their post 2 months ago
Another purchase of credits on my second accout. another email sent tocdeepseek support to recover my main account with no response.
replied to their post 2 months ago
no answer from deepseek on my emsails. Gonna try reddit and X.
replied to their post 3 months ago
My issue was the DNS. Google asks to fix but for that i have to access hostnger. I requested to remove my 2fa and chango email... After sending my while life in documents... They changed the email but forgot to remove the 2fa. I requested again and still no answer.
As for Deepseek,v i m using a secong account that i top up but my First account hรกs crรฉditos also... But i think they need that money ore than me.
posted an update 3 months ago
Post
211
To the Deepseek Team
I had some issues with my Google Workspace account (DNS got all messes up) and basically im not able to access my Gmail and im not able to fix my DNS because hostingeris not helping too much on that. I gave all my documents, IDs, payment receipts etc... But i still not able to access.
WHY im asking for help... Because Deepseek is one of the models that i PAY The API for long time and im not able to access my account. I have one API that i dont know if Will work with v4 and i would like tรด the Deepseek Team tรด help me. The e-mail registered is the same email i use on Huggingface. I have IDs payment receipts from top ups... Everything but i already sent like 5 e-mails tรด Deepseek support with no answer. Sorry for talking about that here but i had no other way tรด reach you up.
And for Huggingface...i iwe you about 2 USD that i Will PAY... But im waiting my New CC arrive. Im a long time user and i wont do any wrong thing like not paying but you could please reactivate some of my services. I go tรด Hospital every week for radiotherapy and sometimes i dont feel good and basically i forgot some hardware on and charged me those 2 USD. I promess all Will be resolved soon.
Thank you all!!!
I had some issues with my Google Workspace account (DNS got all messes up) and basically im not able to access my Gmail and im not able to fix my DNS because hostingeris not helping too much on that. I gave all my documents, IDs, payment receipts etc... But i still not able to access.
WHY im asking for help... Because Deepseek is one of the models that i PAY The API for long time and im not able to access my account. I have one API that i dont know if Will work with v4 and i would like tรด the Deepseek Team tรด help me. The e-mail registered is the same email i use on Huggingface. I have IDs payment receipts from top ups... Everything but i already sent like 5 e-mails tรด Deepseek support with no answer. Sorry for talking about that here but i had no other way tรด reach you up.
And for Huggingface...i iwe you about 2 USD that i Will PAY... But im waiting my New CC arrive. Im a long time user and i wont do any wrong thing like not paying but you could please reactivate some of my services. I go tรด Hospital every week for radiotherapy and sometimes i dont feel good and basically i forgot some hardware on and charged me those 2 USD. I promess all Will be resolved soon.
Thank you all!!!
- 5 replies
reacted to sergiopaniego's post with ๐ค 3 months ago
Post
2325
We just released TRL v0.26.0!
It comes packed with updates:
> Agent training with tools in GRPO
> New CISPO & SAPO losses + reasoning rewards
> vLLM quantization in colocate mode
> Dataset shuffling in SFT
> Lots of NEW examples
> Tons of fixes and documentation improvements
It comes packed with updates:
> Agent training with tools in GRPO
> New CISPO & SAPO losses + reasoning rewards
> vLLM quantization in colocate mode
> Dataset shuffling in SFT
> Lots of NEW examples
> Tons of fixes and documentation improvements
- 3 replies
reacted to melvindave's post with ๐ 7 months ago
Post
2591
Currently having a blast learning the transformers library.
I noticed that model cards usually have Transformers code as usage examples.
So I tried to figure out how to load a model just using the transformers library without using ollama, lmstudio, or llamacpp.
Learned how to install dependencies required to make it work like pytorch and CUDA. I also used Conda for python environment dependencies.
Once I got the model loaded and sample inference working, I made an API to serve it.
I know it's very basic stuff for machine learning experts here in HF but I'm completely new to this so I'm happy to get it working!
Model used: Qwen/Qwen3-VL-8B-Instruct
GPU: NVIDIA GeForce RTX 3090
Here's the result of my experimentation
I noticed that model cards usually have Transformers code as usage examples.
So I tried to figure out how to load a model just using the transformers library without using ollama, lmstudio, or llamacpp.
Learned how to install dependencies required to make it work like pytorch and CUDA. I also used Conda for python environment dependencies.
Once I got the model loaded and sample inference working, I made an API to serve it.
I know it's very basic stuff for machine learning experts here in HF but I'm completely new to this so I'm happy to get it working!
Model used: Qwen/Qwen3-VL-8B-Instruct
GPU: NVIDIA GeForce RTX 3090
Here's the result of my experimentation
- 6 replies
reacted to sergiopaniego's post with ๐ฅ 7 months ago
Post
2899
ICYMI, you can fine-tune open LLMs using Claude Code
just tell it:
โFine-tune Qwen3-0.6B on open-r1/codeforces-cotsโ
and Claude submits a real training job on HF GPUs using TRL.
it handles everything:
> dataset validation
> GPU selection
> training + Trackio monitoring
> job submission + cost estimation
when itโs done, your model is on the Hub, ready to use
read more about the process: https://huggingface.co/blog/hf-skills-training
just tell it:
โFine-tune Qwen3-0.6B on open-r1/codeforces-cotsโ
and Claude submits a real training job on HF GPUs using TRL.
it handles everything:
> dataset validation
> GPU selection
> training + Trackio monitoring
> job submission + cost estimation
when itโs done, your model is on the Hub, ready to use
read more about the process: https://huggingface.co/blog/hf-skills-training
- 1 reply
reacted to evijit's post with โค๏ธ 7 months ago
Post
2893
AI for Scientific Discovery Won't Work Without Fixing How We Collaborate.
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
๐จ The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
๐ Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
โ ๏ธ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
๐ Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join: hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
๐จ The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
๐ Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
โ ๏ธ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
๐ Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
Discord: https://discord.com/invite/VYkdEVjJ5J
reacted to lewtun's post with ๐ฅ over 1 year ago
Post
5180
Introducing OlympicCoder: a series of open reasoning models that can solve olympiad-level programming problems ๐งโ๐ป
- 7B open-r1/OlympicCoder-7B
- 32B open-r1/OlympicCoder-32B
We find that OlympicCoder models outperform Claude 3.7 Sonnet, as well as others over 100x larger ๐ช
Together with the models, we are releasing:
๐CodeForces-CoTs: new dataset of code problems from the most popular competitive coding platform, with R1 traces in C++ and Python open-r1/codeforces-cots
๐ IOI'2024: a new benchmark of VERY hard programming problems where even frontier models struggle to match human performance open-r1/ioi
For links to the models and datasets, check out our latest progress report from Open R1: https://huggingface.co/blog/open-r1/update-3
- 7B open-r1/OlympicCoder-7B
- 32B open-r1/OlympicCoder-32B
We find that OlympicCoder models outperform Claude 3.7 Sonnet, as well as others over 100x larger ๐ช
Together with the models, we are releasing:
๐CodeForces-CoTs: new dataset of code problems from the most popular competitive coding platform, with R1 traces in C++ and Python open-r1/codeforces-cots
๐ IOI'2024: a new benchmark of VERY hard programming problems where even frontier models struggle to match human performance open-r1/ioi
For links to the models and datasets, check out our latest progress report from Open R1: https://huggingface.co/blog/open-r1/update-3
- 1 reply