Post
118
gg-tt
company
AI & ML interests
None defined yet.
Recent Activity
View all activity
danielhanchen
posted
an
update
about 14 hours ago
Post
3222
LiquidAI
open-sources a new generation of edge LLMs! 🥳Based on a new hybrid architecture, these 350M, 700M, and 1.2B models are both fast and performant, ideal for on-device deployment.
I recommend fine-tuning them to power your next edge application. We already provide Colab notebooks to guide you. More to come soon!
📝 Blog post: https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models
🤗 Models: LiquidAI/lfm2-686d721927015b2ad73eaa38
- 1 reply
danielhanchen
posted
an
update
12 days ago
Post
2890
We fixed more issues! Use
* Fixed Nanonets OCR-s unsloth/Nanonets-OCR-s-GGUF
* Fixed THUDM GLM-4 unsloth/GLM-4-32B-0414-GGUF
* DeepSeek Chimera v2 is uploading! unsloth/DeepSeek-TNG-R1T2-Chimera-GGUF
--jinja for all!* Fixed Nanonets OCR-s unsloth/Nanonets-OCR-s-GGUF
* Fixed THUDM GLM-4 unsloth/GLM-4-32B-0414-GGUF
* DeepSeek Chimera v2 is uploading! unsloth/DeepSeek-TNG-R1T2-Chimera-GGUF
danielhanchen
posted
an
update
14 days ago
Post
2361
Gemma 3n finetuning is now 1.5x faster and uses 50% less VRAM in Unsloth!
Click "Use this model" and click "Google Colab"!
unsloth/gemma-3n-E4B-it
unsloth/gemma-3n-E2B-it
https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Conversational.ipynb
Click "Use this model" and click "Google Colab"!
unsloth/gemma-3n-E4B-it
unsloth/gemma-3n-E2B-it
https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Conversational.ipynb
-
- 2 replies
Post
2456
‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module for asymmetric models & much more. Sparse + Dense = 🔥 hybrid search performance! Details:
1️⃣ Sparse Encoder Models
Brand new support for sparse embedding models that generate high-dimensional embeddings (30,000+ dims) where <1% are non-zero:
- Full SPLADE, Inference-free SPLADE, and CSR architecture support
- 4 new modules, 12 new losses, 9 new evaluators
- Integration with @elastic-co , @opensearch-project , @NAVER LABS Europe, @qdrant , @IBM , etc.
- Decode interpretable embeddings to understand token importance
- Hybrid search integration to get the best of both worlds
2️⃣ Enhanced Encode Methods & Multi-Processing
- Introduce encode_query & encode_document automatically use predefined prompts
- No more manual pool management - just pass device list directly to encode()
- Much cleaner and easier to use than the old multi-process approach
3️⃣ Router Module & Advanced Training
- Router module with different processing paths for queries vs documents
- Custom learning rates for different parameter groups
- Composite loss logging - see individual loss components
- Perfect for two-tower architectures
4️⃣ Comprehensive Documentation & Training
- New Training Overview, Loss Overview, API Reference docs
- 6 new training example documentation pages
- Full integration examples with major search engines
- Extensive blogpost on training sparse models
Read the comprehensive blogpost about training sparse embedding models: https://huggingface.co/blog/train-sparse-encoder
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v5.0.0
What's next? We would love to hear from the community! What sparse encoder models would you like to see? And what new capabilities should Sentence Transformers handle - multimodal embeddings, late interaction models, or something else? Your feedback shapes our roadmap!
1️⃣ Sparse Encoder Models
Brand new support for sparse embedding models that generate high-dimensional embeddings (30,000+ dims) where <1% are non-zero:
- Full SPLADE, Inference-free SPLADE, and CSR architecture support
- 4 new modules, 12 new losses, 9 new evaluators
- Integration with @elastic-co , @opensearch-project , @NAVER LABS Europe, @qdrant , @IBM , etc.
- Decode interpretable embeddings to understand token importance
- Hybrid search integration to get the best of both worlds
2️⃣ Enhanced Encode Methods & Multi-Processing
- Introduce encode_query & encode_document automatically use predefined prompts
- No more manual pool management - just pass device list directly to encode()
- Much cleaner and easier to use than the old multi-process approach
3️⃣ Router Module & Advanced Training
- Router module with different processing paths for queries vs documents
- Custom learning rates for different parameter groups
- Composite loss logging - see individual loss components
- Perfect for two-tower architectures
4️⃣ Comprehensive Documentation & Training
- New Training Overview, Loss Overview, API Reference docs
- 6 new training example documentation pages
- Full integration examples with major search engines
- Extensive blogpost on training sparse models
Read the comprehensive blogpost about training sparse embedding models: https://huggingface.co/blog/train-sparse-encoder
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v5.0.0
What's next? We would love to hear from the community! What sparse encoder models would you like to see? And what new capabilities should Sentence Transformers handle - multimodal embeddings, late interaction models, or something else? Your feedback shapes our roadmap!
danielhanchen
posted
an
update
28 days ago
Post
727
We updated lots of our GGUFs and uploaded many new ones!
* unsloth/dots.llm1.inst-GGUF
* unsloth/Jan-nano-GGUF
* unsloth/Nanonets-OCR-s-GGUF
* Updated and fixed Q8_0 upload for unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF
* Added Q2_K_XL for unsloth/DeepSeek-R1-0528-GGUF
* Updated and fixed Vision support for unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
* unsloth/dots.llm1.inst-GGUF
* unsloth/Jan-nano-GGUF
* unsloth/Nanonets-OCR-s-GGUF
* Updated and fixed Q8_0 upload for unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF
* Added Q2_K_XL for unsloth/DeepSeek-R1-0528-GGUF
* Updated and fixed Vision support for unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF
Post
2799
Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub 🤯
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
Post
1615
Me: This function is too slow. Find a faster algorithm.
Cursor: Hold my beer.
Me: *Slacking off with colleagues*
Cursor: Ping.
Me: 🤯
Cursor: Hold my beer.
Me: *Slacking off with colleagues*
Cursor: Ping.
Me: 🤯
danielhanchen
posted
an
update
about 1 month ago
Post
2027
Mistral releases Magistral, their new reasoning models! 🔥
GGUFs to run: unsloth/Magistral-Small-2506-GGUF
Magistral-Small-2506 excels at mathematics and coding.
You can run the 24B model locally with just 32GB RAM by using our Dynamic GGUFs.
GGUFs to run: unsloth/Magistral-Small-2506-GGUF
Magistral-Small-2506 excels at mathematics and coding.
You can run the 24B model locally with just 32GB RAM by using our Dynamic GGUFs.
Post
5941
NEW: Real-time conversational AI models can now run 100% locally in your browser! 🤯
🔐 Privacy by design (no data leaves your device)
💰 Completely free... forever
📦 Zero installation required, just visit a website
⚡️ Blazingly-fast WebGPU-accelerated inference
Try it out: webml-community/conversational-webgpu
For those interested, here's how it works:
- Silero VAD for voice activity detection
- Whisper for speech recognition
- SmolLM2-1.7B for text generation
- Kokoro for text to speech
Powered by Transformers.js and ONNX Runtime Web! 🤗 I hope you like it!
🔐 Privacy by design (no data leaves your device)
💰 Completely free... forever
📦 Zero installation required, just visit a website
⚡️ Blazingly-fast WebGPU-accelerated inference
Try it out: webml-community/conversational-webgpu
For those interested, here's how it works:
- Silero VAD for voice activity detection
- Whisper for speech recognition
- SmolLM2-1.7B for text generation
- Kokoro for text to speech
Powered by Transformers.js and ONNX Runtime Web! 🤗 I hope you like it!
- 4 replies
danielhanchen
posted
an
update
about 1 month ago
Post
3404
New DeepSeek-R1-0528 1.65-bit Dynamic GGUF!
Run the model locally even easier! Will fit on a 192GB Macbook and run at 7 tokens/s.
DeepSeek-R1-0528 GGUFs: unsloth/DeepSeek-R1-0528-GGUF
Qwen3-8B DeepSeek-R1-0528 GGUFs: unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF
And read our Guide: https://docs.unsloth.ai/basics/deepseek-r1-0528
Run the model locally even easier! Will fit on a 192GB Macbook and run at 7 tokens/s.
DeepSeek-R1-0528 GGUFs: unsloth/DeepSeek-R1-0528-GGUF
Qwen3-8B DeepSeek-R1-0528 GGUFs: unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF
And read our Guide: https://docs.unsloth.ai/basics/deepseek-r1-0528
PhilCulliton
authored
2
papers
about 2 months ago
Post
4084
hey hey
@mradermacher
- VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! 💥
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
- 16 replies
clefourrier
posted
an
update
about 2 months ago
Post
953
Always surprised that so few people actually read the FineTasks blog, on
✨how to select training evals with the highest signal✨
If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!
An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!
The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"👌
(to know on your use case how to select the best evals for you)
Blog: HuggingFaceFW/blogpost-fine-tasks
✨how to select training evals with the highest signal✨
If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!
An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!
The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"👌
(to know on your use case how to select the best evals for you)
Blog: HuggingFaceFW/blogpost-fine-tasks
-
- 2 replies
gneubig
authored
a
paper
about 2 months ago
paultimothymooney
authored
2
papers
2 months ago
PhilCulliton
authored
a
paper
2 months ago
danielhanchen
posted
an
update
3 months ago
Post
2053
💜 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