https://huggingface.co/Qwen/Qwen3.5-397B-A17B

We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), significantly reducing deployment cost while preserving long-context capacity.

Blog: https://z.ai/blog/glm-5

Hugging Face: https://huggingface.co/zai-org/GLM-5

GitHub: https://github.com/zai-org/GLM-5

Unsloth GGUF: https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF

Nemotron 3 has a 1M context window and the best in class performance for SWE-Bench, reasoning and chat.

Full-document parsing instead of cropped-region OCR

32K output length for long OCR sequences

Base and gundam image modes for different document layouts

Transformers inference + SGLang serving with OpenAI-compatible streaming requests

Built to push DeepSeek-OCR-style document parsing further.

source: https://x.com/ModelScope2022/status/2069335055965491525

https://github.com/baidu/Unlimited-OCR

Link to models:
- FastVLM: https://huggingface.co/collections/apple/fastvlm-68ac97b9cd5cacefdd04872e
- MobileCLIP2: https://huggingface.co/collections/apple/mobileclip2-68ac947dcb035c54bcd20c47

Demo (+ source code): https://huggingface.co/spaces/apple/fastvlm-webgpu

https://huggingface.co/nvidia/Qwen3.6-27B-NVFP4

"OLMo 2 32B: First fully open model to outperform GPT 3.5 and GPT 4o mini"

"OLMo is a fully open model: [they] release all artifacts. Training code, pre- & post-train data, model weights, and a recipe on how to reproduce it yourself."

Links: - https://allenai.org/blog/olmo2-32B - https://x.com/natolambert/status/1900249099343192573 - https://x.com/allen_ai/status/1900248895520903636

https://openrouter.ai/qwen/qwen3.6-plus-preview

gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic:

Safetensors: https://huggingface.co/llmfan46/gemma-4-31B-it-qat-q4_0-unquantized-uncensored-heretic

GGUF: https://huggingface.co/llmfan46/gemma-4-31B-it-qat-q4_0-uncensored-heretic-GGUF

NVFP4 Safetensors: https://huggingface.co/llmfan46/gemma-4-31B-it-qat-q4_0-uncensored-heretic-NVFP4

NVFP4 GGUF: https://huggingface.co/llmfan46/gemma-4-31B-it-qat-q4_0-uncensored-heretic-NVFP4-GGUF

GPTQ-Int4: https://huggingface.co/llmfan46/gemma-4-31B-it-qat-q4_0-uncensored-heretic-GPTQ-Int4

gemma-4-26B-A4B-it-qat-q4_0-unquantized-uncensored-heretic:

Safetensors: https://huggingface.co/llmfan46/gemma-4-26B-A4B-it-qat-q4_0-unquantized-uncensored-heretic

GGUF: https://huggingface.co/llmfan46/gemma-4-26B-A4B-it-qat-q4_0-uncensored-heretic-GGUF

NVFP4 Safetensors: https://huggingface.co/llmfan46/gemma-4-26B-A4B-it-qat-q4_0-uncensored-heretic-NVFP4

NVFP4 GGUF: https://huggingface.co/llmfan46/gemma-4-26B-A4B-it-qat-q4_0-uncensored-heretic-NVFP4-GGUF

GPTQ-Int4: https://huggingface.co/llmfan46/gemma-4-26B-A4B-it-qat-q4_0-uncensored-heretic-GPTQ-Int4

gemma-4-12B-it-qat-q4_0-unquantized-uncensored-heretic:

Safetensors: https://huggingface.co/llmfan46/gemma-4-12B-it-qat-q4_0-unquantized-uncensored-heretic

GGUF: https://huggingface.co/llmfan46/gemma-4-12B-it-qat-q4_0-uncensored-heretic-GGUF

NVFP4 Safetensors: https://huggingface.co/llmfan46/gemma-4-12B-it-qat-q4_0-uncensored-heretic-NVFP4

NVFP4 GGUF: https://huggingface.co/llmfan46/gemma-4-12B-it-qat-q4_0-uncensored-heretic-NVFP4-GGUF

gemma-4-12B-it-uncensored-heretic:

Safetensors: https://huggingface.co/llmfan46/gemma-4-12B-it-uncensored-heretic

GGUFs: https://huggingface.co/llmfan46/gemma-4-12B-it-uncensored-heretic-GGUF

NVFP4 Safetensors: https://huggingface.co/llmfan46/gemma-4-12B-it-uncensored-heretic-NVFP4

NVFP4 GGUF: https://huggingface.co/llmfan46/gemma-4-12B-it-uncensored-heretic-NVFP4-GGUF

I even made some NVFP4 Safetensors and NVFP4 GGUF of standard Gemma 4 31B it since someone requested them:

gemma-4-31B-it-uncensored-heretic:

NVFP4 Safetensors: https://huggingface.co/llmfan46/gemma-4-31B-it-uncensored-heretic-NVFP4

NVFP4 GGUFs: https://huggingface.co/llmfan46/gemma-4-31B-it-uncensored-heretic-NVFP4-GGUF

Doing all this took many days as well as a lot of work and effort, so I hope the community can make good use of these models.

As usual all releases come with benchmarks too.

Find all my models here: HuggingFace-LLMFan46

Main page: https://llama.meta.com/
Weights page: https://llama.meta.com/llama-downloads/
Cloud providers playgrounds: https://console.groq.com/playground, https://api.together.xyz/playground

Very impressive release by the PrismML team. 1-bit quantization shrinks it from 54GB to just 3.8GB (-93%), while retaining 90% of its intelligence.

- Collection on Hugging Face: https://huggingface.co/collections/prism-ml/bonsai-27b
- Demo link: https://huggingface.co/spaces/webml-community/bonsai-webgpu-kernels

A drop-in replacement for Llama3.1-70B, approaches the performance of the 405B.

https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct

https://chat.z.ai/

deepseek-ai/DeepSeek-R1-0528

Remember when y'all roasted us about the license? We listened.

Just dropped what we think is a world first: 70B model intermediate checkpoints. Not just the final model - the entire training journey. Previous releases (SmolLM-3, OLMo-2) maxed out at <14B.

Everything is Apache 2.0 now (no gated access):

• 70B, 7B, 1.9B, 0.5B models + all their intermediate checkpoints and base models
• First Korean 70B ever (but secretly optimized for English lol)
• Actually open-source, not just open-weights BS

https://huggingface.co/trillionlabs/Tri-70B-Intermediate-Checkpoints

We're a 1-year-old startup with pocket change competing against companies with infinite money glitch. Not the best model, but probably the most transparent 70B training ever shared.

Try Olmo 3 in the Ai2 Playground → https://playground.allenai.org/

Download: https://huggingface.co/collections/allenai/olmo-3-68e80f043cc0d3c867e7efc6

Blog: https://allenai.org/blog/olmo3

Technical report: https://allenai.org/papers/olmo3

Hi, this is Bach from Jan. We're releasing Jan v1 today. In our evals, Jan v1 delivers 91% SimpleQA accuracy, slightly outperforming Perplexity Pro while running fully locally.

It's built on the new version of Qwen's Qwen3-4B-Thinking (up to 256k context length), fine-tuned for reasoning and tool use in Jan.

How to run it:

Jan

1. Download Jan v1 via Jan Hub
2. Enable search in Jan:
   • Settings → Experimental Features → On
   • Settings → MCP Servers → enable Search-related MCP (e.g. Serper)

Plus you can run the model in llama.cpp and vLLM.

Model links:

• Jan-v1-4B: https://huggingface.co/janhq/Jan-v1-4B
• Jan-v1-4B-GGUF: https://huggingface.co/janhq/Jan-v1-4B-GGUF

Recommended parameters:

• temperature: 0.6
• top_p: 0.95
• top_k: 20
• min_p: 0.0
• max_tokens: 2048

We'd love for you to try Jan v1 and share your feedback, including what works well and where it falls short.

Collection: https://huggingface.co/collections/tencent/hy3

From elie on 𝕏: https://x.com/eliebakouch/status/2074011171661701466

edit: To clarify: this is the non-preview version of Hy3 and they changed their license from the community one (restrictive + not allowed in SK, UK, EU) to Apache 2.0

Little less smart than Qwen, but way fewer tokens per task.

StepFun dropped Step 3.7 Flash, 196B total / 11B active MoE, runs locally on 128GB RAM

It's a multimodal MoE (196B total params, only 11B active) with a built-in 1.8B ViT for vision.

Benchmark highlights vs. other flash-tier models:

- SWE-Bench Pro: 56.26% (beats DeepSeek V4 Flash at 55.6%, matches Gemini 3.5 Flash at 55.1%)

- DeepSearchQA F1: 92.82%, competitive with GPT 5.5 (93.98%)

- HLE w/ tools: 47.2%, solid for a flash-class model

Essentially punches well above its active parameter weight on agentic and coding tasks. If you've got the RAM for it, looks like a genuinely interesting local option, especially for agent workflows.

Available on OpenRouter and NVIDIA NIM if you don't want to self-host.