LiquidAI/LFM2.5-2.6B
Text Generation • 3B • Updated • 117k • 596
🏗️ Building on HF
almer
almernzh
AI & ML interests
None yet
Recent Activity
liked a model 4 days ago
LiquidAI/LFM2.5-2.6B liked a model 16 days ago
unsloth/Kimi-K3-GGUF liked a model 17 days ago
moonshotai/Kimi-K3Organizations
liked a
model 4 days ago
liked a
model 16 days ago
liked a
model 17 days ago
liked a
model 20 days ago
reacted to SeaWolf-AI's post with 👍 20 days ago
Post
4051
📱 POCKET — a 35-billion-parameter model that runs on your iPhone, and on your PC with no GPU
We're releasing POCKET, VIDRAFT's flagship Darwin-36B-Opus compressed for on-device use. No fork, no CUDA, no cloud — it runs on stock llama.cpp. It's a sparse Mixture-of-Experts model (256 experts, only 8 active per token), so the file can be large while the work per token stays small. That's what lets a 35B model run on a phone, and generate fast on a CPU with no graphics card.
Measured (POCKET-35B IQ1_M vs Bonsai-27B Q1_0):
• CPU generate (Xeon, 16 threads): 27.0 vs 10.1 tok/s → 2.69× faster
• GPU generate (H100): 197 vs 89 tok/s → 2.22× faster
• GPU prompt processing (H100): 753 vs 1816 → 0.41× (Bonsai wins this one — MoE prefill wakes every expert, so sparsity stops helping there. We say so.)
• Quality (HellaSwag, 400 q): 61.0% vs 60.0% → a tie (confidence intervals overlap)
On a real consumer laptop — MacBook M3 Pro (18 GB) — POCKET wins every axis, prompt processing included:
• Metal generate: 25.4 vs 12.8 → 1.99×
• CPU generate: 13.8 vs 4.4 → 3.13×
• Metal prompt: 240.7 vs 73.4 → 3.28×
One more quiet fact: the same-size, quality-oriented rival Ternary-Bonsai-27B (7.2 GB) fails to load in upstream llama.cpp at all — it needs the PrismML fork. POCKET runs on the tools you already have: LM Studio, Ollama, PocketPal, MLX.
📖 Full story (tech, measurements, recipes): https://huggingface.co/blog/FINAL-Bench/pocket
Models:
📦 POCKET-35B-GGUF (PC / server, no GPU): FINAL-Bench/POCKET-35B-GGUF
🇰🇷 POCKET-KR-GGUF (Android): FINAL-Bench/POCKET-KR-GGUF
🍎 POCKET-KR-MLX (iPhone / Mac): FINAL-Bench/POCKET-KR-MLX
🌍 POCKET-EN-GGUF (English phone / PC): FINAL-Bench/POCKET-EN-GGUF
🖥️ Live demo (answering on a CPU, no GPU): FINAL-Bench/POCKET-35B-CPU
📚 Collection: FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6
We're releasing POCKET, VIDRAFT's flagship Darwin-36B-Opus compressed for on-device use. No fork, no CUDA, no cloud — it runs on stock llama.cpp. It's a sparse Mixture-of-Experts model (256 experts, only 8 active per token), so the file can be large while the work per token stays small. That's what lets a 35B model run on a phone, and generate fast on a CPU with no graphics card.
Measured (POCKET-35B IQ1_M vs Bonsai-27B Q1_0):
• CPU generate (Xeon, 16 threads): 27.0 vs 10.1 tok/s → 2.69× faster
• GPU generate (H100): 197 vs 89 tok/s → 2.22× faster
• GPU prompt processing (H100): 753 vs 1816 → 0.41× (Bonsai wins this one — MoE prefill wakes every expert, so sparsity stops helping there. We say so.)
• Quality (HellaSwag, 400 q): 61.0% vs 60.0% → a tie (confidence intervals overlap)
On a real consumer laptop — MacBook M3 Pro (18 GB) — POCKET wins every axis, prompt processing included:
• Metal generate: 25.4 vs 12.8 → 1.99×
• CPU generate: 13.8 vs 4.4 → 3.13×
• Metal prompt: 240.7 vs 73.4 → 3.28×
One more quiet fact: the same-size, quality-oriented rival Ternary-Bonsai-27B (7.2 GB) fails to load in upstream llama.cpp at all — it needs the PrismML fork. POCKET runs on the tools you already have: LM Studio, Ollama, PocketPal, MLX.
📖 Full story (tech, measurements, recipes): https://huggingface.co/blog/FINAL-Bench/pocket
Models:
📦 POCKET-35B-GGUF (PC / server, no GPU): FINAL-Bench/POCKET-35B-GGUF
🇰🇷 POCKET-KR-GGUF (Android): FINAL-Bench/POCKET-KR-GGUF
🍎 POCKET-KR-MLX (iPhone / Mac): FINAL-Bench/POCKET-KR-MLX
🌍 POCKET-EN-GGUF (English phone / PC): FINAL-Bench/POCKET-EN-GGUF
🖥️ Live demo (answering on a CPU, no GPU): FINAL-Bench/POCKET-35B-CPU
📚 Collection: FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6
- 5 replies
reacted to ovi054's post with ❤️ about 1 month ago
Post
4521
Turn any topic to animation explainer video
It is powered by Qwen3-14B + Manim LoRA and turns any concept into a Manim explainer video.
👉 Try it now:
build-small-hackathon/anim-vid-ai
It is powered by Qwen3-14B + Manim LoRA and turns any concept into a Manim explainer video.
👉 Try it now:
build-small-hackathon/anim-vid-ai
liked a
Space about 1 month ago
reacted to projectlosangeles's post with 🔥 about 2 months ago
Post
6620
🔥If you love multi-modal art🎨, please check out "A Million Little Fibers" project!!!🔥
https://github.com/asigalov61/A-Million-Little-Fibers-2026
https://soundcloud.com/aleksandr-sigalov-61/sets/a-million-little-fibers
This brand new 2026 edition covers three SOTA models:
zai-org/GLM-5.2
k2-fsa/OmniVoice
HeartMuLa/HeartMuLa-oss-3B-happy-new-year
The project aims to showcase what kind of multi-modal art is now possible to create with these amazing OSS resources!
If you enjoyed the project, please ⭐or 🔱 GitHub repo and❤️on SoundCloud and Hugging Face. It really helps!
Most sincerely,
Alex
Project Los Angeles
Tegridy Code 2026
P.S. Don't forget to bring a towel! 😂
@multimodalart
@victor
@John6666
https://github.com/asigalov61/A-Million-Little-Fibers-2026
https://soundcloud.com/aleksandr-sigalov-61/sets/a-million-little-fibers
This brand new 2026 edition covers three SOTA models:
zai-org/GLM-5.2
k2-fsa/OmniVoice
HeartMuLa/HeartMuLa-oss-3B-happy-new-year
The project aims to showcase what kind of multi-modal art is now possible to create with these amazing OSS resources!
If you enjoyed the project, please ⭐or 🔱 GitHub repo and❤️on SoundCloud and Hugging Face. It really helps!
Most sincerely,
Alex
Project Los Angeles
Tegridy Code 2026
P.S. Don't forget to bring a towel! 😂
@multimodalart
@victor
@John6666
- 3 replies
updated a
model about 2 months ago
published a
model about 2 months ago
liked a
model about 2 months ago
reacted to ST-x-Tony's post with ❤️ about 2 months ago
Post
7169
Hello AI Community! 👋
We are thrilled to announce the release of **NRS_QWEN_MYTHOS_1M**, a high-performance reasoning model built on the powerful **Qwen 3.5 9B** base. At **SKT AI LABS**, we’ve applied our proprietary **Neural Reasoning System (NRS)** to push the boundaries of what a 9B model can do.
🔥 **Why this model is a Game-Changer:**
✅ **100x High Reasoning Capacity:** Deep logical thinking and complex problem-solving via NRS Boosting.
✅ **1 Million Token Context:** Handle massive codebases, long documents, and multi-turn agentic tasks with ease (YaRN Scaling).
✅ **Advanced Thinking Mode:** Native
✅ **Tool-Use Ready:** Optimized for Python execution and Web Search with self-correction.
✅ **Blazing Fast:** Efficient 9B architecture that runs smoothly on consumer hardware (RTX 3090/4090).
🛠️ **Technical Highlights:**
* **Base:** Qwen 3.5 9B
* **Tuning:** NRS Specific Tuning high-quality samples.
* **License:** NRS DOCS
Whether you are a developer building coding agents, a researcher dealing with long-context data, or just someone who loves deep reasoning, this model is built for you.
👇 **Try it now on Hugging Face:**
SKT-NRS/NRS_QWEN_MYTHOS_1M
We are thrilled to announce the release of **NRS_QWEN_MYTHOS_1M**, a high-performance reasoning model built on the powerful **Qwen 3.5 9B** base. At **SKT AI LABS**, we’ve applied our proprietary **Neural Reasoning System (NRS)** to push the boundaries of what a 9B model can do.
🔥 **Why this model is a Game-Changer:**
✅ **100x High Reasoning Capacity:** Deep logical thinking and complex problem-solving via NRS Boosting.
✅ **1 Million Token Context:** Handle massive codebases, long documents, and multi-turn agentic tasks with ease (YaRN Scaling).
✅ **Advanced Thinking Mode:** Native
<think> tags for step-by-step Chain-of-Thought reasoning.✅ **Tool-Use Ready:** Optimized for Python execution and Web Search with self-correction.
✅ **Blazing Fast:** Efficient 9B architecture that runs smoothly on consumer hardware (RTX 3090/4090).
🛠️ **Technical Highlights:**
* **Base:** Qwen 3.5 9B
* **Tuning:** NRS Specific Tuning high-quality samples.
* **License:** NRS DOCS
Whether you are a developer building coding agents, a researcher dealing with long-context data, or just someone who loves deep reasoning, this model is built for you.
👇 **Try it now on Hugging Face:**
SKT-NRS/NRS_QWEN_MYTHOS_1M
liked a
Space about 2 months ago
updated a
model about 2 months ago
published a
model about 2 months ago
liked 4
models about 2 months ago
upvoted a collection about 2 months ago