GGUF My Repo
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Create GGUF quantized model from a Hugging Face repo
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nvidia/Nemotron-Cascade-2-SFT-DataOrganizations
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🏛️ 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 🔥 2 months ago
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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
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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
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RedNote 小红书 just released their first LLM 🔥
dots.llm1.base 🪐 a 142B MoE model with only 14B active params.
rednote-hilab/dotsllm1-68246aaaaba3363374a8aa7c
✨ Base & Instruct - MIT license
✨ Trained on 11.2T non-synthetic high-quality data
✨ Competitive with Qwen2.5/3 on reasoning, code, alignment
dots.llm1.base 🪐 a 142B MoE model with only 14B active params.
rednote-hilab/dotsllm1-68246aaaaba3363374a8aa7c
✨ Base & Instruct - MIT license
✨ Trained on 11.2T non-synthetic high-quality data
✨ Competitive with Qwen2.5/3 on reasoning, code, alignment
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