ibm-granite/granite-4.0-micro
Text Generation • Updated • 106k • 264
bndp
bndp
AI & ML interests
None yet
Recent Activity
liked a model 7 days ago
ibm-granite/granite-4.0-micro reacted to Shrijanagain's post with 🔥 12 days ago
We are thrilled to announce the launch of SKT-OMNI-CORPUS-146T-V1, a massive-scale, high-quality dataset designed to power the next generation of Foundation Models (LLMs) from scratch.
Developed at SKT AI LABS, this corpus is not just a collection of data; it’s a mission to decentralize high-grade AI training for regional languages and global knowledge.
💎 Key Highlights:
•• Massive Scale: Targeting a multi-terabyte architecture for 146T-level tokenization.
•• Pure Quality: Curated from 500+ Elite Sources
•• Structured for MoE: Perfectly sharded into 3.5GB standardized units (SKT-𝕻 series) for seamless distributed training.
🤝 Open for Collaboration!
We are looking for AI researchers, CUDA engineers, and data scientists to join us in this journey of building Project Surya and the ST-X Series models. Whether it's optimization, custom tokenization, or architecture design—let’s build the future together.
Explore the Dataset on Hugging Face:
🔗 https://huggingface.co/datasets/Shrijanagain/SKT-OMNI-CORPUS-146T-V1
DSR -- 🔗 https://huggingface.co/datasets/Shrijanagain/SKT-DSRx10000
#AI #MachineLearning #OpenSource #IndicAI #SKTAILABS #LLM #BigData #HuggingFace #InnovationIndia liked a Space 16 days ago
ggml-org/gguf-my-repoOrganizations
None yet
liked a
model 7 days ago
reactedto Shrijanagain's post with 🔥 12 days ago
Post
5553
We are thrilled to announce the launch of SKT-OMNI-CORPUS-146T-V1, a massive-scale, high-quality dataset designed to power the next generation of Foundation Models (LLMs) from scratch.
Developed at SKT AI LABS, this corpus is not just a collection of data; it’s a mission to decentralize high-grade AI training for regional languages and global knowledge.
💎 Key Highlights:
•• Massive Scale: Targeting a multi-terabyte architecture for 146T-level tokenization.
•• Pure Quality: Curated from 500+ Elite Sources
•• Structured for MoE: Perfectly sharded into 3.5GB standardized units (SKT-𝕻 series) for seamless distributed training.
🤝 Open for Collaboration!
We are looking for AI researchers, CUDA engineers, and data scientists to join us in this journey of building Project Surya and the ST-X Series models. Whether it's optimization, custom tokenization, or architecture design—let’s build the future together.
Explore the Dataset on Hugging Face:
🔗 Shrijanagain/SKT-OMNI-CORPUS-146T-V1
DSR -- 🔗 Shrijanagain/SKT-DSRx10000
#AI #MachineLearning #OpenSource #IndicAI #SKTAILABS #LLM #BigData #HuggingFace #InnovationIndia
liked a
Space 16 days ago
liked a
model 16 days ago
liked a
dataset 16 days ago
liked a
model about 1 month ago
liked a
model 2 months ago
reactedto nyuuzyou's post with 👍 2 months ago
Post
2736
🏛️ Microsoft CodePlex Archive Dataset - nyuuzyou/ms-codeplex-archive
Following the strong response to the Google Code Archive nyuuzyou/google-code-archive (thanks!), this release preserves another major historical repository: the Microsoft CodePlex Archive.
CodePlex served as Microsoft’s primary open-source hosting platform from 2006 to 2017. This dataset captures the distinct .NET and Windows-centric development ecosystem that flourished before the industry standardizing on GitHub.
Key Stats:
- 5,043,730 files from 38,087 repositories
- 3.6 GB compressed Parquet
- 91 programming languages (Heavily featuring C#, ASP.NET, and C++)
- Cleaned of binaries, build artifacts, and vendor directories (node_modules, packages)
- Includes platform-specific license metadata (Ms-PL, Ms-RL)
Following the strong response to the Google Code Archive nyuuzyou/google-code-archive (thanks!), this release preserves another major historical repository: the Microsoft CodePlex Archive.
CodePlex served as Microsoft’s primary open-source hosting platform from 2006 to 2017. This dataset captures the distinct .NET and Windows-centric development ecosystem that flourished before the industry standardizing on GitHub.
Key Stats:
- 5,043,730 files from 38,087 repositories
- 3.6 GB compressed Parquet
- 91 programming languages (Heavily featuring C#, ASP.NET, and C++)
- Cleaned of binaries, build artifacts, and vendor directories (node_modules, packages)
- Includes platform-specific license metadata (Ms-PL, Ms-RL)
reactedto AdinaY's post with 🔥 3 months ago
Post
2400
Z.ai just released a powerful lightweight option of GLM 4.7
✨ 30B total/3B active - MoE
zai-org/GLM-4.7-Flash
✨ 30B total/3B active - MoE
zai-org/GLM-4.7-Flash
- 1 reply
liked a
model 3 months ago
liked a
Space 3 months ago
upvoted a paper 7 months ago
upvoted a collection 7 months ago
liked 2
models 8 months ago
liked a
Space 9 months ago
liked a
model 9 months ago
reactedto alandao's post with 🔥 10 months ago
Post
1461
Don’t give up 🔥
Do you know what I was planning to do this time last week?
I was preparing to write a report declaring that Jan Nano was a failed project because the benchmark results didn’t meet expectations.
But I thought — it can’t be. When loading the model into the app, the performance clearly felt better. So why were the benchmark results worse?
That’s when I reviewed the entire benchmark codebase and realized something fundamental: agentic or workflow-based approaches introduce a huge gap and variation when benchmarking. Jan-nano was trained with an agentic setup — it simply can’t be benchmarked using a rigid workflow-based method.
I made the necessary changes, and the model ended up performing even better than before the issues arose. Turns out the previous benchmarking method conflicted with the way the model was trained.
What if I had given up? That would’ve meant 1.5 months of training and a huge amount of company resources wasted.
But now, this is officially the most successful and biggest release for the whole team — all thanks to Jan-nano.
Menlo/Jan-nano
Do you know what I was planning to do this time last week?
I was preparing to write a report declaring that Jan Nano was a failed project because the benchmark results didn’t meet expectations.
But I thought — it can’t be. When loading the model into the app, the performance clearly felt better. So why were the benchmark results worse?
That’s when I reviewed the entire benchmark codebase and realized something fundamental: agentic or workflow-based approaches introduce a huge gap and variation when benchmarking. Jan-nano was trained with an agentic setup — it simply can’t be benchmarked using a rigid workflow-based method.
I made the necessary changes, and the model ended up performing even better than before the issues arose. Turns out the previous benchmarking method conflicted with the way the model was trained.
What if I had given up? That would’ve meant 1.5 months of training and a huge amount of company resources wasted.
But now, this is officially the most successful and biggest release for the whole team — all thanks to Jan-nano.
Menlo/Jan-nano
published a
model 10 months ago
updated a
model 10 months ago