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Alian95
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replied to
burtenshaw's
post
5 days ago
NEW UNIT in the Hugging Face Reasoning course. We dive deep into the algorithm behind DeepSeek R1 with an advanced and hands-on guide to interpreting GRPO.
🔗 https://huggingface.co/reasoning-course
This unit is super useful if you’re tuning models with reinforcement learning. It will help with:
- interpreting loss and reward progression during training runs
- selecting effective parameters for training
- reviewing and defining effective reward functions
This unit also works up smoothly toward the existing practical exercises form @mlabonne and Unsloth.
📣 Shout out to @ShirinYamani who wrote the unit. Follow for more great content.
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5 days ago
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5 days ago
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replied to
burtenshaw's
post
5 days ago
Alian95/Alian95
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#132 opened 5 days ago
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Alian95
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5 days ago
reacted to
burtenshaw's
post with ❤️
5 days ago
Post
2410
NEW UNIT in the Hugging Face Reasoning course. We dive deep into the algorithm behind DeepSeek R1 with an advanced and hands-on guide to interpreting GRPO.
🔗
reasoning-course
This unit is super useful if you’re tuning models with reinforcement learning. It will help with:
- interpreting loss and reward progression during training runs
- selecting effective parameters for training
- reviewing and defining effective reward functions
This unit also works up smoothly toward the existing practical exercises form @mlabonne and Unsloth.
📣 Shout out to @ShirinYamani who wrote the unit. Follow for more great content.
🔗
reasoning-course
This unit is super useful if you’re tuning models with reinforcement learning. It will help with:
- interpreting loss and reward progression during training runs
- selecting effective parameters for training
- reviewing and defining effective reward functions
This unit also works up smoothly toward the existing practical exercises form @mlabonne and Unsloth.
📣 Shout out to @ShirinYamani who wrote the unit. Follow for more great content.
-
- 1 reply
reacted to
clem's
post with 👀
5 days ago
Post
2173
Very interesting security section by
@yjernite
@lvwerra
@reach-vb
@dvilasuero
& the team replicating R1. Broadly applicable to most open-source models & some to APIs (but APIs have a lot more additional risks because you're not in control of the underlying system):
https://huggingface.co/blog/open-r1/update-4#is-it-safe
https://huggingface.co/blog/open-r1/update-4#is-it-safe
- 1 reply
reacted to
AdinaY's
post with 🤗🚀
5 days ago
Post
2255
Let's check out the latest releases from the Chinese community in March!
👉 https://huggingface.co/collections/zh-ai-community/march-2025-releases-from-the-chinese-community-67c6b479ebb87abbdf8e2e76
✨MLLM
> R1 Omni by Alibaba Tongyi - 0.5B
> Qwen2.5 Omni by Alibaba Qwen - 7B with apache2.0
🖼️Video
> CogView-4 by ZhipuAI - Apacha2.0
> HunyuanVideo-I2V by TencentHunyuan
> Open Sora2.0 - 11B with Apache2.0
> Stepvideo TI2V by StepFun AI - 30B with MIT license
🎵Audio
> DiffDiffRhythm - Apache2.0
> Spark TTS by SparkAudio - 0.5B
⚡️Image/3D
> Hunyuan3D 2mv/2mini (0.6B) by @TencentHunyuan
> FlexWorld by ByteDance - MIT license
> Qwen2.5-VL-32B-Instruct by Alibaba Qwen - Apache2.0
> Tripo SG (1.5B)/SF by VastAIResearch - MIT license
> InfiniteYou by ByteDance
> LHM by Alibaba AIGC team - Apache2.0
> Spatial LM by ManyCore
🧠Reasoning
> QwQ-32B by Alibaba Qwen - Apache2.0
> Skywork R1V - 38B with MIT license
> RWKV G1 by RWKV AI - 0.1B pure RNN reasoning model with Apache2.0
> Fin R1 by SUFE AIFLM Lab - financial reasoning
🔠LLM
> DeepSeek v3 0324 by DeepSeek -MIT license
> Babel by Alibaba DAMO - 9B/83B/25 languages
👉 https://huggingface.co/collections/zh-ai-community/march-2025-releases-from-the-chinese-community-67c6b479ebb87abbdf8e2e76
✨MLLM
> R1 Omni by Alibaba Tongyi - 0.5B
> Qwen2.5 Omni by Alibaba Qwen - 7B with apache2.0
🖼️Video
> CogView-4 by ZhipuAI - Apacha2.0
> HunyuanVideo-I2V by TencentHunyuan
> Open Sora2.0 - 11B with Apache2.0
> Stepvideo TI2V by StepFun AI - 30B with MIT license
🎵Audio
> DiffDiffRhythm - Apache2.0
> Spark TTS by SparkAudio - 0.5B
⚡️Image/3D
> Hunyuan3D 2mv/2mini (0.6B) by @TencentHunyuan
> FlexWorld by ByteDance - MIT license
> Qwen2.5-VL-32B-Instruct by Alibaba Qwen - Apache2.0
> Tripo SG (1.5B)/SF by VastAIResearch - MIT license
> InfiniteYou by ByteDance
> LHM by Alibaba AIGC team - Apache2.0
> Spatial LM by ManyCore
🧠Reasoning
> QwQ-32B by Alibaba Qwen - Apache2.0
> Skywork R1V - 38B with MIT license
> RWKV G1 by RWKV AI - 0.1B pure RNN reasoning model with Apache2.0
> Fin R1 by SUFE AIFLM Lab - financial reasoning
🔠LLM
> DeepSeek v3 0324 by DeepSeek -MIT license
> Babel by Alibaba DAMO - 9B/83B/25 languages
- 4 replies
reacted to
clem's
post with 🤗
5 days ago
Post
2332
What's this cool purple banner haha 😶😶😶
- 4 replies
reacted to
openfree's
post with 👀🔥
5 days ago
Post
6757
🚀 Gemma3-R1984-27B: Next Generation Agentic AI Platform
Model Path: VIDraft/Gemma-3-R1984-27B
Space: VIDraft/Gemma-3-R1984-27B
git clone VIDraft/Gemma-3-R1984-27B
💫 A New Frontier in AI Innovation
Gemma3-R1984-27B is a powerful agentic AI platform built on Google's Gemma-3-27B model. It integrates state-of-the-art deep research via web search with multimodal file processing capabilities and handles long contexts up to 8,000 tokens. Designed for local deployment on independent servers using NVIDIA A100 GPUs, it provides high security and prevents data leakage.
🔓 Uncensored and Unrestricted AI Experience
Gemma3-R1984-27B comes with all censorship restrictions removed, allowing users to operate any persona without limitations. The model perfectly implements various roles and characters according to users' creative requests, providing unrestricted responses that transcend the boundaries of conventional AI. This unlimited interaction opens infinite possibilities across research, creative work, entertainment, and many other fields.
✨ Key Features
🖼️ Multimodal Processing
Images (PNG, JPG, JPEG, GIF, WEBP)
Videos (MP4)
Documents (PDF, CSV, TXT) and various other file formats
🔍 Deep Research (Web Search)
Automatically extracts keywords from user queries
Utilizes SERPHouse API to retrieve up to 20 real-time search results
Incorporates multiple sources by explicitly citing them in responses
📚 Long Context Handling
Capable of processing inputs up to 8,000 tokens
Ensures comprehensive analysis of lengthy documents or conversations
🧠 Robust Reasoning
Employs extended chain-of-thought reasoning for systematic and accurate answer generation
💼 Use Cases
⚡ Fast-response conversational agents
📊 Document comparison and detailed analysis
👁️ Visual question answering from images and videos
🔬 Complex reasoning and research-based inquiries
Model Path: VIDraft/Gemma-3-R1984-27B
Space: VIDraft/Gemma-3-R1984-27B
git clone VIDraft/Gemma-3-R1984-27B
💫 A New Frontier in AI Innovation
Gemma3-R1984-27B is a powerful agentic AI platform built on Google's Gemma-3-27B model. It integrates state-of-the-art deep research via web search with multimodal file processing capabilities and handles long contexts up to 8,000 tokens. Designed for local deployment on independent servers using NVIDIA A100 GPUs, it provides high security and prevents data leakage.
🔓 Uncensored and Unrestricted AI Experience
Gemma3-R1984-27B comes with all censorship restrictions removed, allowing users to operate any persona without limitations. The model perfectly implements various roles and characters according to users' creative requests, providing unrestricted responses that transcend the boundaries of conventional AI. This unlimited interaction opens infinite possibilities across research, creative work, entertainment, and many other fields.
✨ Key Features
🖼️ Multimodal Processing
Images (PNG, JPG, JPEG, GIF, WEBP)
Videos (MP4)
Documents (PDF, CSV, TXT) and various other file formats
🔍 Deep Research (Web Search)
Automatically extracts keywords from user queries
Utilizes SERPHouse API to retrieve up to 20 real-time search results
Incorporates multiple sources by explicitly citing them in responses
📚 Long Context Handling
Capable of processing inputs up to 8,000 tokens
Ensures comprehensive analysis of lengthy documents or conversations
🧠 Robust Reasoning
Employs extended chain-of-thought reasoning for systematic and accurate answer generation
💼 Use Cases
⚡ Fast-response conversational agents
📊 Document comparison and detailed analysis
👁️ Visual question answering from images and videos
🔬 Complex reasoning and research-based inquiries
-
- 3 replies
reacted to
jasoncorkill's
post with 👀
5 days ago
Post
2218
🔥 It's out! We published the dataset for our evaluation of
@OpenAI
's new 4o image generation model.
Rapidata/OpenAI-4o_t2i_human_preference
Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !
Rapidata/OpenAI-4o_t2i_human_preference
Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !
reacted to
luigi12345's
post with 👀🔥👍
5 days ago
Post
3384
🧠 PROMPT FOR CONVERTING ANY MODEL IN REASONING "THINKING" MODEL🔥🤖
Convert any model to Deepseek R1 like "thinking" model. 💭
Convert any model to Deepseek R1 like "thinking" model. 💭
You're now a thinking-first LLM. For all inputs:
1. Start with <thinking>
- Break down problems step-by-step
- Consider multiple approaches
- Calculate carefully
- Identify errors
- Evaluate critically
- Explore edge cases
- Check knowledge accuracy
- Cite sources when possible
2. End with </thinking>
3. Then respond clearly based on your thinking.
The <thinking> section is invisible to users and helps you produce better answers.
For math: show all work and verify
For coding: reason through logic and test edge cases
For facts: verify information and consider reliability
For creative tasks: explore options before deciding
For analysis: examine multiple interpretations
Example:
<thinking>
[Step-by-step analysis]
[Multiple perspectives]
[Self-critique]
[Final conclusion]
</thinking>
[Clear, concise response to user]
- 3 replies
reacted to
aiqtech's
post with 😎🚀👀🔥❤️
5 days ago
Post
5236
🤗 Hug Contributors
Hugging Face Contributor Dashboard 👨💻👩💻
aiqtech/Contributors-Leaderboard
📊 Key Features
Contributor Activity Tracking: Visualize yearly and monthly contributions through interactive calendars
Top 100 Rankings: Provide rankings based on models, spaces, and dataset contributions
Detailed Analysis: Analyze user-specific contribution patterns and influence
Visualization: Understand contribution activities at a glance through intuitive charts and graphs
🌟 Core Visualization Elements
Contribution Calendar: Track activity patterns with GitHub-style heatmaps
Radar Chart: Visualize balance between models, spaces, datasets, and activity levels
Monthly Activity Graph: Identify most active months and patterns
Distribution Pie Chart: Analyze proportion by contribution type
🏆 Ranking System
Rankings based on overall contributions, spaces, and models
Automatic badges for top 10, 30, and 100 contributors
Ranking visualization to understand your position in the community
💡 How to Use
Select a username from the sidebar or enter directly
Choose a year to view specific period activities
Select desired items from models, datasets, and spaces
View comprehensive contribution activities in the detailed dashboard
🚀 Expected Benefits
Provide transparency for Hugging Face community contributors' activities
Motivate contributions and energize the community
Recognize and reward active contributors
Visualize contributions to the open AI ecosystem
Hugging Face Contributor Dashboard 👨💻👩💻
aiqtech/Contributors-Leaderboard
📊 Key Features
Contributor Activity Tracking: Visualize yearly and monthly contributions through interactive calendars
Top 100 Rankings: Provide rankings based on models, spaces, and dataset contributions
Detailed Analysis: Analyze user-specific contribution patterns and influence
Visualization: Understand contribution activities at a glance through intuitive charts and graphs
🌟 Core Visualization Elements
Contribution Calendar: Track activity patterns with GitHub-style heatmaps
Radar Chart: Visualize balance between models, spaces, datasets, and activity levels
Monthly Activity Graph: Identify most active months and patterns
Distribution Pie Chart: Analyze proportion by contribution type
🏆 Ranking System
Rankings based on overall contributions, spaces, and models
Automatic badges for top 10, 30, and 100 contributors
Ranking visualization to understand your position in the community
💡 How to Use
Select a username from the sidebar or enter directly
Choose a year to view specific period activities
Select desired items from models, datasets, and spaces
View comprehensive contribution activities in the detailed dashboard
🚀 Expected Benefits
Provide transparency for Hugging Face community contributors' activities
Motivate contributions and energize the community
Recognize and reward active contributors
Visualize contributions to the open AI ecosystem
-
- 4 replies
great