Unofficial Mistral Community
community
AI & ML interests
Unofficial org for community upload of Mistral's Open Source models.
Recent Activity
mistral-community's activity
Post
3683
What are you using to evaluate models or AI systems? So far we're building lighteval & leaderboards on the hub but still feels early & a lot more to build. What would be useful to you?
- 6 replies
mrfakename
posted
an
update
6 days ago
Post
1918
Hi everyone,
I just launched TTS Arena V2 - a platform for benchmarking TTS models by blind A/B testing. The goal is to make it easy to compare quality between open-source and commercial models, including conversational ones.
What's new in V2:
- **Conversational Arena**: Evaluate models like CSM-1B, Dia 1.6B, and PlayDialog in multi-turn settings
- **Personal Leaderboard**: Optional login to see which models you tend to prefer
- **Multi-speaker TTS**: Random voices per generation to reduce speaker bias
- **Performance Upgrade**: Rebuilt from Gradio → Flask. Much faster with fewer failed generations.
- **Keyboard Shortcuts**: Vote entirely via keyboard
Also added models like MegaTTS 3, Cartesia Sonic, and ElevenLabs' full lineup.
I'd love any feedback, feature suggestions, or ideas for models to include.
TTS-AGI/TTS-Arena-V2
I just launched TTS Arena V2 - a platform for benchmarking TTS models by blind A/B testing. The goal is to make it easy to compare quality between open-source and commercial models, including conversational ones.
What's new in V2:
- **Conversational Arena**: Evaluate models like CSM-1B, Dia 1.6B, and PlayDialog in multi-turn settings
- **Personal Leaderboard**: Optional login to see which models you tend to prefer
- **Multi-speaker TTS**: Random voices per generation to reduce speaker bias
- **Performance Upgrade**: Rebuilt from Gradio → Flask. Much faster with fewer failed generations.
- **Keyboard Shortcuts**: Vote entirely via keyboard
Also added models like MegaTTS 3, Cartesia Sonic, and ElevenLabs' full lineup.
I'd love any feedback, feature suggestions, or ideas for models to include.
TTS-AGI/TTS-Arena-V2
- 1 reply
Post
1526
LeRobot-worldwide-hackathon
is already scheduled in 30 cities all over the world! Check if there's one in your city here: LeRobot-worldwide-hackathon/worldwide-map
Post
1434
The
meta-llama
org just crossed 40,000 followers on Hugging Face. Grateful for all their impact on the field sharing the Llama weights openly and much more!
We need more of this from all other big tech to make the AI more open, collaborative and beneficial to all!
meta-llama
org just crossed 40,000 followers on Hugging Face. Grateful for all their impact on the field sharing the Llama weights openly and much more! We need more of this from all other big tech to make the AI more open, collaborative and beneficial to all!
danielhanchen
posted
an
update
8 days ago
Post
1553
💜 Qwen3 128K Context Length: We've released Dynamic 2.0 GGUFs + 4-bit safetensors!
Fixed: Now works on any inference engine and fixed issues with the chat template.
Qwen3 GGUFs:
30B-A3B: unsloth/Qwen3-30B-A3B-GGUF
235-A22B: unsloth/Qwen3-235B-A22B-GGUF
32B: unsloth/Qwen3-32B-GGUF
Read our guide on running Qwen3 here: https://docs.unsloth.ai/basics/qwen3-how-to-run-and-finetune
128K Context Length:
30B-A3B: unsloth/Qwen3-30B-A3B-128K-GGUF
235-A22B: unsloth/Qwen3-235B-A22B-128K-GGUF
32B: unsloth/Qwen3-32B-128K-GGUF
All Qwen3 uploads: unsloth/qwen3-680edabfb790c8c34a242f95
Fixed: Now works on any inference engine and fixed issues with the chat template.
Qwen3 GGUFs:
30B-A3B: unsloth/Qwen3-30B-A3B-GGUF
235-A22B: unsloth/Qwen3-235B-A22B-GGUF
32B: unsloth/Qwen3-32B-GGUF
Read our guide on running Qwen3 here: https://docs.unsloth.ai/basics/qwen3-how-to-run-and-finetune
128K Context Length:
30B-A3B: unsloth/Qwen3-30B-A3B-128K-GGUF
235-A22B: unsloth/Qwen3-235B-A22B-128K-GGUF
32B: unsloth/Qwen3-32B-128K-GGUF
All Qwen3 uploads: unsloth/qwen3-680edabfb790c8c34a242f95
danielhanchen
posted
an
update
13 days ago
Post
5701
🦥 Introducing Unsloth Dynamic v2.0 GGUFs!
Our v2.0 quants set new benchmarks on 5-shot MMLU and KL Divergence, meaning you can now run & fine-tune quantized LLMs while preserving as much accuracy as possible.
Llama 4: unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
DeepSeek-R1: unsloth/DeepSeek-R1-GGUF-UD
Gemma 3: unsloth/gemma-3-27b-it-GGUF
We made selective layer quantization much smarter. Instead of modifying only a subset of layers, we now dynamically quantize all layers so every layer has a different bit. Now, our dynamic method can be applied to all LLM architectures, not just MoE's.
Blog with Details: https://docs.unsloth.ai/basics/dynamic-v2.0
All our future GGUF uploads will leverage Dynamic 2.0 and our hand curated 300K–1.5M token calibration dataset to improve conversational chat performance.
For accurate benchmarking, we built an evaluation framework to match the reported 5-shot MMLU scores of Llama 4 and Gemma 3. This allowed apples-to-apples comparisons between full-precision vs. Dynamic v2.0, QAT and standard iMatrix quants.
Dynamic v2.0 aims to minimize the performance gap between full-precision models and their quantized counterparts.
Our v2.0 quants set new benchmarks on 5-shot MMLU and KL Divergence, meaning you can now run & fine-tune quantized LLMs while preserving as much accuracy as possible.
Llama 4: unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
DeepSeek-R1: unsloth/DeepSeek-R1-GGUF-UD
Gemma 3: unsloth/gemma-3-27b-it-GGUF
We made selective layer quantization much smarter. Instead of modifying only a subset of layers, we now dynamically quantize all layers so every layer has a different bit. Now, our dynamic method can be applied to all LLM architectures, not just MoE's.
Blog with Details: https://docs.unsloth.ai/basics/dynamic-v2.0
All our future GGUF uploads will leverage Dynamic 2.0 and our hand curated 300K–1.5M token calibration dataset to improve conversational chat performance.
For accurate benchmarking, we built an evaluation framework to match the reported 5-shot MMLU scores of Llama 4 and Gemma 3. This allowed apples-to-apples comparisons between full-precision vs. Dynamic v2.0, QAT and standard iMatrix quants.
Dynamic v2.0 aims to minimize the performance gap between full-precision models and their quantized counterparts.
Post
3970
Energy is a massive constraint for AI but do you even know what energy your chatGPT convos are using?
We're trying to change this by releasing ChatUI-energy, the first interface where you see in real-time what energy your AI conversations consume. Great work from @jdelavande powered by spaces & TGI, available for a dozen of open-source models like Llama, Mistral, Qwen, Gemma and more.
jdelavande/chat-ui-energy
Should all chat interfaces have this? Just like ingredients have to be shown on products you buy, we need more transparency in AI for users!
We're trying to change this by releasing ChatUI-energy, the first interface where you see in real-time what energy your AI conversations consume. Great work from @jdelavande powered by spaces & TGI, available for a dozen of open-source models like Llama, Mistral, Qwen, Gemma and more.
jdelavande/chat-ui-energy
Should all chat interfaces have this? Just like ingredients have to be shown on products you buy, we need more transparency in AI for users!
-
- 3 replies
Post
2928
Just crossed half a million public apps on Hugging Face. A new public app is created every minute these days 🤯🤯🤯
What's your favorite? http://hf.co/spaces
What's your favorite? http://hf.co/spaces
- 3 replies
Post
1466
You can now bill your inference costs from all our inference partners (together, fireworks, fal, sambanova, cerebras, hyperbolic,...) to your Hugging Face organization.
Useful to drive more company-wide usage of AI without the billing headaches!
Useful to drive more company-wide usage of AI without the billing headaches!
- 1 reply
How do I load the model quantized?
4
#33 opened 29 days ago
by
treehugg3
How do I load the model quantized?
4
#33 opened 29 days ago
by
treehugg3
reach-vb
authored
a
paper
about 1 month ago
danielhanchen
posted
an
update
about 1 month ago
Post
4865
You can now run Llama 4 on your own local device! 🦙
Run our Dynamic 1.78-bit and 2.71-bit Llama 4 GGUFs:
unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4
Run our Dynamic 1.78-bit and 2.71-bit Llama 4 GGUFs:
unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4
- 1 reply
Post
2655
Llama 4 is in transformers!
Fun example using the instruction-tuned Maverick model responding about two images, using tensor parallel for maximum speed.
From https://huggingface.co/blog/llama4-release
Fun example using the instruction-tuned Maverick model responding about two images, using tensor parallel for maximum speed.
From https://huggingface.co/blog/llama4-release
- 1 reply
Post
1994
Llama models (arguably the most successful open AI models of all times) just represented 3% of total model downloads on Hugging Face in March.
People and media like stories of winner takes all & one model/company to rule them all but the reality is much more nuanced than this!
Kudos to all the small AI builders out there!
People and media like stories of winner takes all & one model/company to rule them all but the reality is much more nuanced than this!
Kudos to all the small AI builders out there!
- 2 replies
mrfakename
posted
an
update
about 1 month ago
Post
2771
Papla P1 from Papla Media is now available on the TTS Arena!
Try out Papla's new ultra-realistic TTS model + compare it with other leading models on the TTS Arena: TTS-AGI/TTS-Arena
Try out Papla's new ultra-realistic TTS model + compare it with other leading models on the TTS Arena: TTS-AGI/TTS-Arena
Post
1355
Now in Enterprise Hub organizations, you can centralize your billing not only for HF usage but also inference through our inference partners.
Will prevent some headaches for your finance & accounting teams haha (so feel free to share that with them).
Will prevent some headaches for your finance & accounting teams haha (so feel free to share that with them).
- 3 replies
Post
4029
Before 2020, most of the AI field was open and collaborative. For me, that was the key factor that accelerated scientific progress and made the impossible possible—just look at the “T” in ChatGPT, which comes from the Transformer architecture openly shared by Google.
Then came the myth that AI was too dangerous to share, and companies started optimizing for short-term revenue. That led many major AI labs and researchers to stop sharing and collaborating.
With OAI and sama now saying they're willing to share open weights again, we have a real chance to return to a golden age of AI progress and democratization—powered by openness and collaboration, in the US and around the world.
This is incredibly exciting. Let’s go, open science and open-source AI!
Then came the myth that AI was too dangerous to share, and companies started optimizing for short-term revenue. That led many major AI labs and researchers to stop sharing and collaborating.
With OAI and sama now saying they're willing to share open weights again, we have a real chance to return to a golden age of AI progress and democratization—powered by openness and collaboration, in the US and around the world.
This is incredibly exciting. Let’s go, open science and open-source AI!
-
- 5 replies