Qwen/Qwen3-Embedding-4B-GGUF
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littlebird13
updated
2
models
about 1 hour ago
littlebird13
updated
a
model
about 4 hours ago
danielhanchen
posted
an
update
about 12 hours ago
Post
110
Post
147
past week had huuuge releases 💗
here's our picks 🔥 find more models, datasets, demos here merve/releases-july-11-68750452c358c98b0fa663f7
> moonshotai/Kimi-K2-Instruct is the new sota LLM with 1T total 32B active parameters 🤯
> HuggingFaceTB/SmolLM3-3B is the new best LM for it's size, offers thinking mode 💭 as well as the dataset HuggingFaceTB/smoltalk2
> Alibaba-NLP/WebSailor-3B is the new agentic LLM for complex browsing
> Google DeepMind released medical vision LMs with an agentic doctor-patient app google/medgemma-release-680aade845f90bec6a3f60c4
> fal released a LoRA to improve details on face images fal/Realism-Detailer-Kontext-Dev-LoRA
here's our picks 🔥 find more models, datasets, demos here merve/releases-july-11-68750452c358c98b0fa663f7
> moonshotai/Kimi-K2-Instruct is the new sota LLM with 1T total 32B active parameters 🤯
> HuggingFaceTB/SmolLM3-3B is the new best LM for it's size, offers thinking mode 💭 as well as the dataset HuggingFaceTB/smoltalk2
> Alibaba-NLP/WebSailor-3B is the new agentic LLM for complex browsing
> Google DeepMind released medical vision LMs with an agentic doctor-patient app google/medgemma-release-680aade845f90bec6a3f60c4
> fal released a LoRA to improve details on face images fal/Realism-Detailer-Kontext-Dev-LoRA
littlebird13
updated
a
Space
about 16 hours ago
littlebird13
published
a
Space
about 16 hours ago
Post
3114
Kimi-K2 is now available on the hub🔥🚀
This is a trillion-parameter MoE model focused on long context, code, reasoning, and agentic behavior.
moonshotai/kimi-k2-6871243b990f2af5ba60617d
✨ Base & Instruct
✨ 1T total / 32B active - Modified MIT License
✨ 128K context length
✨ Muon optimizer for stable trillion-scale training
This is a trillion-parameter MoE model focused on long context, code, reasoning, and agentic behavior.
moonshotai/kimi-k2-6871243b990f2af5ba60617d
✨ Base & Instruct
✨ 1T total / 32B active - Modified MIT License
✨ 128K context length
✨ Muon optimizer for stable trillion-scale training
- 1 reply
zsytony
authored
3
papers
5 days ago
PM4Bench: A Parallel Multilingual Multi-Modal Multi-task Benchmark for Large Vision Language Model
Paper
•
2503.18484
•
Published
Coding Triangle: How Does Large Language Model Understand Code?
Paper
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2507.06138
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Published
•
19
Rethinking Verification for LLM Code Generation: From Generation to Testing
Paper
•
2507.06920
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Published
•
27
louisbrulenaudet
posted
an
update
6 days ago
Post
2621
Because hackathons are often the starting point for many AI projects, I've created a Python-backend template incorporating my feedback to streamline collaboration and urgent deployments 🏎️
Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through:
- uv as a package manager, simplifying usage via a series of pre-configured make commands.
- FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network).
- Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects.
- A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.
This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️
In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️
Link to GitHub: https://github.com/louisbrulenaudet/hackathon-backend
Simply issue these commands and you can ship your code at the speed of light:
Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through:
- uv as a package manager, simplifying usage via a series of pre-configured make commands.
- FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network).
- Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects.
- A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.
This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️
In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️
Link to GitHub: https://github.com/louisbrulenaudet/hackathon-backend
Simply issue these commands and you can ship your code at the speed of light:
make init
make dev
chujiezheng
authored
a
paper
6 days ago
Post
2994
GitHub refuses to render notebooks for a long time now 💔
so smol-vision now lives in Hugging Face model repository 🤗 merve/smol-vision
so smol-vision now lives in Hugging Face model repository 🤗 merve/smol-vision
- 1 reply
Change to Apache 2.0 Licnse
❤️
1
#36 opened 6 days ago
by
ehartford
Post
443
The tech report of RoboBrain 2.0 is now available on the Daily Papers page🔥
It's an embedded brain model that sees, thinks, and plans for many robots.
Leave your insights or questions, the authors are happy to respond.
RoboBrain 2.0 Technical Report (2507.02029)
It's an embedded brain model that sees, thinks, and plans for many robots.
Leave your insights or questions, the authors are happy to respond.
RoboBrain 2.0 Technical Report (2507.02029)
Post
310
Skywork-Reward-V2🔥 Reward models by Skywork AI.
Skywork/skywork-reward-v2-685cc86ce5d9c9e4be500c84
✨ 0.6B - 8B
✨ Trained on 26M human-LLM preference pairs
✨ 0.6B > 27B in many tasks
Skywork/skywork-reward-v2-685cc86ce5d9c9e4be500c84
✨ 0.6B - 8B
✨ Trained on 26M human-LLM preference pairs
✨ 0.6B > 27B in many tasks
Post
257
POLAR🐻❄️ New reward modeling by Shanghai AI Lab
internlm/polar-68693f829d2e83ac5e6e124a
✨ 1.8B/7B - Apache 2.0
✨ Scalable policy discriminative pretraining
✨ Easy RLHF with minimal preference data
internlm/polar-68693f829d2e83ac5e6e124a
✨ 1.8B/7B - Apache 2.0
✨ Scalable policy discriminative pretraining
✨ Easy RLHF with minimal preference data
Post
3351
ByteDance released Tar 1.5B and 7B: image-text in image-text out models, fully open-source 👏
ByteDance-Seed/tar-6864cf0d9fe59a3b91cc4260
They have an image tokenizer unified with text, and they de-tokenize using either of two models (LLM and diffusion)
The model is actually a full LLM (Qwen2), the tokenizer converts image tokens 🤯
They have an image tokenizer unified with text, and they de-tokenize using either of two models (LLM and diffusion)
The model is actually a full LLM (Qwen2), the tokenizer converts image tokens 🤯