Qwen/Qwen3.5-9B
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CYGDEN
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a model about 4 hours ago
Qwen/Qwen3.5-9B reacted
to
unmodeled-tyler's
post with 🚀 about 4 hours ago
Link to Repo: https://github.com/unmodeled-tyler/thought-tracer
I had a great time at Mistral's Hackathon in SF over the weekend! There were a lot of incredibly talented builders there and it was an honor to be a part of it! 😄
I built Thought Tracer - a TUI-based logitlens application for Ministral 3B/8B with optional AI analysis from Mistral Large on the Mistral API.
Thought Tracer allows you to see what the model "believes" at each layer until it arrives at it's final next token prediction. The Entropy tab displays entropy through each layer, additionally providing both token-level and prompt-level risk for hallucination.
If you have a Mistral API key, the AI analysis section is actually pretty cool because it's returned in rendered markdown and easily understandable language - providing a commentary on how the model likely arrived at it's final prediction, and also offering diagnostics for model developers. This commentary actually makes the tool pretty beginner friendly to anyone interested in exploring AI research tools for the first time.
Check it out if you're interested! reacted
to
danielhanchen's
post with 🔥 about 17 hours ago
Qwen releases 4 new Qwen3.5 Small models: 0.8B • 2B • 4B • 9B!
Run Qwen3.5-0.8B, 2B and 4B on your phone. Run 9B on 6GB RAM.
The vision reasoning LLMs perform better than models 4x their size.
GGUFs to run: https://huggingface.co/collections/unsloth/qwen35
Guide: https://unsloth.ai/docs/models/qwen3.5 Organizations
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liked a model about 4 hours ago
reacted to unmodeled-tyler's post with 🚀 about 4 hours ago
Post
963
Link to Repo: https://github.com/unmodeled-tyler/thought-tracer
I had a great time at Mistral's Hackathon in SF over the weekend! There were a lot of incredibly talented builders there and it was an honor to be a part of it! 😄
I built Thought Tracer - a TUI-based logitlens application for Ministral 3B/8B with optional AI analysis from Mistral Large on the Mistral API.
Thought Tracer allows you to see what the model "believes" at each layer until it arrives at it's final next token prediction. The Entropy tab displays entropy through each layer, additionally providing both token-level and prompt-level risk for hallucination.
If you have a Mistral API key, the AI analysis section is actually pretty cool because it's returned in rendered markdown and easily understandable language - providing a commentary on how the model likely arrived at it's final prediction, and also offering diagnostics for model developers. This commentary actually makes the tool pretty beginner friendly to anyone interested in exploring AI research tools for the first time.
Check it out if you're interested!
I had a great time at Mistral's Hackathon in SF over the weekend! There were a lot of incredibly talented builders there and it was an honor to be a part of it! 😄
I built Thought Tracer - a TUI-based logitlens application for Ministral 3B/8B with optional AI analysis from Mistral Large on the Mistral API.
Thought Tracer allows you to see what the model "believes" at each layer until it arrives at it's final next token prediction. The Entropy tab displays entropy through each layer, additionally providing both token-level and prompt-level risk for hallucination.
If you have a Mistral API key, the AI analysis section is actually pretty cool because it's returned in rendered markdown and easily understandable language - providing a commentary on how the model likely arrived at it's final prediction, and also offering diagnostics for model developers. This commentary actually makes the tool pretty beginner friendly to anyone interested in exploring AI research tools for the first time.
Check it out if you're interested!
- 3 replies
reacted to danielhanchen's post with 🔥🤗 about 17 hours ago
Post
2659
Qwen releases 4 new Qwen3.5 Small models: 0.8B • 2B • 4B • 9B!
Run Qwen3.5-0.8B, 2B and 4B on your phone. Run 9B on 6GB RAM.
The vision reasoning LLMs perform better than models 4x their size.
GGUFs to run: https://huggingface.co/collections/unsloth/qwen35
Guide: https://unsloth.ai/docs/models/qwen3.5
Run Qwen3.5-0.8B, 2B and 4B on your phone. Run 9B on 6GB RAM.
The vision reasoning LLMs perform better than models 4x their size.
GGUFs to run: https://huggingface.co/collections/unsloth/qwen35
Guide: https://unsloth.ai/docs/models/qwen3.5
- 1 reply
liked a model about 17 hours ago
reacted to OzTianlu's post with 🤗 about 17 hours ago
Post
4502
🔥 UPGRADE in Kai: 30B Scaling! 🔥
NoesisLab/Kai-30B-Instruct
NoesisLab/Kai-3B-Instruct
We are incredibly excited to announce that the Kai-30B-Instruct model and its official Space are now LIVE! 🚀
If you've been following the journey from Kai-0.35B to Kai-3B, you know we're rethinking how models reason. Tired of verbose, slow Chain-of-Thought (CoT) outputs that flood your screen with self-talk? So are we.
Kai-30B-Instruct scales up our Adaptive Dual-Search Distillation (ADS) framework. By bridging classical A* heuristic search with continuous gradient descent , we use an information-theoretic log-barrier to physically prune high-entropy reasoning paths during training.
The result? Pure implicit reasoning. The model executes structured logic, arithmetic carries, and branch selections as a reflex in a single forward pass—no external scaffolding required.
At 3B, we observed a phase transition where the model achieved "logical crystallization". Now, at 30B, we are giving the ADS regularizer the massive representational capacity it needs to tackle higher-order symbolic abstractions and complex reasoning tasks.
🧪 Test Kai yourself in our new Space:
NoesisLab/Kai-3B-Instruct
📦 Model Weights:
NoesisLab/Kai-30B-Instruct
Bring your hardest math, logic, and coding benchmarks. We invite the community to stress-test the limits of the penalty wall! 🧱💥
NoesisLab/Kai-30B-Instruct
NoesisLab/Kai-3B-Instruct
We are incredibly excited to announce that the Kai-30B-Instruct model and its official Space are now LIVE! 🚀
If you've been following the journey from Kai-0.35B to Kai-3B, you know we're rethinking how models reason. Tired of verbose, slow Chain-of-Thought (CoT) outputs that flood your screen with self-talk? So are we.
Kai-30B-Instruct scales up our Adaptive Dual-Search Distillation (ADS) framework. By bridging classical A* heuristic search with continuous gradient descent , we use an information-theoretic log-barrier to physically prune high-entropy reasoning paths during training.
The result? Pure implicit reasoning. The model executes structured logic, arithmetic carries, and branch selections as a reflex in a single forward pass—no external scaffolding required.
At 3B, we observed a phase transition where the model achieved "logical crystallization". Now, at 30B, we are giving the ADS regularizer the massive representational capacity it needs to tackle higher-order symbolic abstractions and complex reasoning tasks.
🧪 Test Kai yourself in our new Space:
NoesisLab/Kai-3B-Instruct
📦 Model Weights:
NoesisLab/Kai-30B-Instruct
Bring your hardest math, logic, and coding benchmarks. We invite the community to stress-test the limits of the penalty wall! 🧱💥
- 1 reply
reacted to NJX-njx's post with 🚀👍🔥 about 17 hours ago
Post
7087
Recently, I have open-sourced an AI emotional companion product based on openclaw, called opensoul.
On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.).
You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.
On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.
Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.
This is the project introduction URL: https://opensoul-web.vercel.app
This is the GitHub project URL: https://github.com/NJX-njx/opensoul
@AdinaY @lilianweng@burtenshaw@clem
let's just do it
On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.).
You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.
On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.
Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.
This is the project introduction URL: https://opensoul-web.vercel.app
This is the GitHub project URL: https://github.com/NJX-njx/opensoul
@AdinaY @lilianweng@burtenshaw@clem
let's just do it
- 24 replies
liked 2 models about 23 hours ago
reacted to etemiz's post with 🔥🧠❤️ 2 days ago
Post
6102
AHA 2026 scores of Qwen3.5
27B
Huihui abliteration 65%
Heretic abliteration 55%
Normal 50%
35B
Huihui abliteration 64%
@jiaojjjjje abliteration 57%
@LeadFootThrottleCock abliteration 56%
Normal 49%
27B
Huihui abliteration 65%
Heretic abliteration 55%
Normal 50%
35B
Huihui abliteration 64%
@jiaojjjjje abliteration 57%
@LeadFootThrottleCock abliteration 56%
Normal 49%
- 6 replies
reacted to ajibawa-2023's post with 🚀🔥 2 days ago
Post
3337
Python-Code-Large
Dataset: ajibawa-2023/Python-Code-Large
Python-Code-Large is a large-scale corpus of Python source code comprising more than 2 million rows of Python code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the Python ecosystem.
By providing a high-volume, language-specific corpus, Python-Code-Large enables systematic experimentation in Python-focused model training, domain adaptation, and downstream code understanding tasks.
Python-Code-Large addresses the need for a dedicated Python-only dataset at substantial scale, enabling focused research across data science, backend systems, automation, scientific computing, and AI-driven Python environments.
Dataset: ajibawa-2023/Python-Code-Large
Python-Code-Large is a large-scale corpus of Python source code comprising more than 2 million rows of Python code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the Python ecosystem.
By providing a high-volume, language-specific corpus, Python-Code-Large enables systematic experimentation in Python-focused model training, domain adaptation, and downstream code understanding tasks.
Python-Code-Large addresses the need for a dedicated Python-only dataset at substantial scale, enabling focused research across data science, backend systems, automation, scientific computing, and AI-driven Python environments.
- 1 reply
liked 2 models 2 days ago
updated a collection 2 days ago
liked a model 2 days ago
