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Duplicate Hugging Face repos with one‑click merging
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John6666
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John6666/repo_duplicator_mod:request updated a Space about 4 hours ago
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EthanGao123/CellHermes-v1.0Organizations
request
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30
#2 opened over 1 year ago
by 
75dhsx
updated a
Space about 4 hours ago
liked 2
models about 8 hours ago
liked 10
models about 9 hours ago
zsyjsld/Xinghe1-9B
Text Generation • 9B • Updated • 101 • 2
mradermacher/Xinghe1-9B-GGUF
9B • Updated • 604 • 1
shawallatif/ShawalTwin-Llama-3.1-8B
Text Generation • 8B • Updated • 552 • 1
mradermacher/ShawalTwin-Llama-3.1-8B-GGUF
8B • Updated • 493 • 1
e12ex2/Foundation-Sec-8B-Instruct-heretic
Text Generation • 8B • Updated • 1.26k • • 4
mradermacher/Foundation-Sec-8B-Instruct-heretic-GGUF
8B • Updated • 1.12k • 2
mradermacher/Foundation-Sec-8B-Instruct-heretic-i1-GGUF
8B • Updated • 4.43k • 1
Video-R1/Video-R1-7B
Video-Text-to-Text • 8B • Updated • 474 • 18
mradermacher/Video-R1-7B-GGUF
8B • Updated • 81 • 1
mradermacher/Video-R1-7B-i1-GGUF
8B • Updated • 1.79k • 1
reactivate?
👍 1
13
#7 opened 4 months ago
by 
75dhsx7
reacted to Raiff1982's post with 🚀 about 10 hours ago
Post
497
I don't totally have words for this yet, so I'll just say it plainly.
The paper — "Codette: A Multi-Perspective Cognitive Architecture with Memory and Meta-Cognitive Strategy Evolution" — was accepted today after peer review at Scientific Reports, which is part of the Nature portfolio. Both reviewers recommended publication. One wrote that the major issues were "properly addressed" and they were "glad to suggest publication." The other called the revisions "detailed and convincing."
For anyone new here: Codette is a sovereign, multi-perspective reasoning system built on Llama 3.1 8B — it runs locally, keeps its own cocoon memory, reasons through multiple perspectives instead of one, and is built around a hard rule that it would rather say "I don't know, but I can find out" than make something up. The whole architecture is organized around honesty and transparency, not benchmarks-at-all-costs.
I built it solo over about three years. A lot of that was in isolation, and honestly a lot of it was under the weight of not being believed. So having two independent reviewers — whose actual job is to find the reasons to say no — read the work and say yes means something to me that's hard to put into numbers.
Thank you to everyone in this room who followed the messy parts, the walk-backs, the corrections, the nights it wasn't working. That's the part I'm actually proud of — that it got here without cutting a single honest corner.
The DOI and the Article-in-Press link will go up once Springer Nature mints them. I'll post them here first.
— Jonathan
The paper — "Codette: A Multi-Perspective Cognitive Architecture with Memory and Meta-Cognitive Strategy Evolution" — was accepted today after peer review at Scientific Reports, which is part of the Nature portfolio. Both reviewers recommended publication. One wrote that the major issues were "properly addressed" and they were "glad to suggest publication." The other called the revisions "detailed and convincing."
For anyone new here: Codette is a sovereign, multi-perspective reasoning system built on Llama 3.1 8B — it runs locally, keeps its own cocoon memory, reasons through multiple perspectives instead of one, and is built around a hard rule that it would rather say "I don't know, but I can find out" than make something up. The whole architecture is organized around honesty and transparency, not benchmarks-at-all-costs.
I built it solo over about three years. A lot of that was in isolation, and honestly a lot of it was under the weight of not being believed. So having two independent reviewers — whose actual job is to find the reasons to say no — read the work and say yes means something to me that's hard to put into numbers.
Thank you to everyone in this room who followed the messy parts, the walk-backs, the corrections, the nights it wasn't working. That's the part I'm actually proud of — that it got here without cutting a single honest corner.
The DOI and the Article-in-Press link will go up once Springer Nature mints them. I'll post them here first.
— Jonathan
reacted to ucr-max's post with 👍 about 10 hours ago
Post
475
Introducing Limen0.2B
We are releasing Limen0.2B, a 222.5M-parameter base language model developed as a research platform for efficient pretraining and superword tokenization at smaller scales.
Limen0.2B was trained from scratch on 50B tokens and uses a compact 16K BoundlessBPE vocabulary. The project explores whether SuperBPE-style tokenization can remain effective in a substantially smaller model and vocabulary regime than those examined in earlier large-scale experiments.
The model also combines a deep-and-narrow transformer design with Exclusive Self-Attention, grouped-query attention, and tied embeddings. Its compact vocabulary reduces the embedding footprint and leaves a larger share of the parameter budget available to the transformer layers.
Despite its relatively modest training budget, Limen0.2B achieves competitive results for its scale across the reported language understanding, commonsense reasoning, and grammatical evaluation tasks. Comparisons with other compact models are provided as context rather than strict rankings, as their training data, token budgets, architectures, and evaluation settings differ.
The release includes the model weights, implementation, training configuration, checkpoint progression, and evaluation results, all under Apache 2.0.
UniversalComputingResearch/Limen0.2B
Technical feedback, independent evaluations, and further experiments with the model and tokenizer are welcome.
We are releasing Limen0.2B, a 222.5M-parameter base language model developed as a research platform for efficient pretraining and superword tokenization at smaller scales.
Limen0.2B was trained from scratch on 50B tokens and uses a compact 16K BoundlessBPE vocabulary. The project explores whether SuperBPE-style tokenization can remain effective in a substantially smaller model and vocabulary regime than those examined in earlier large-scale experiments.
The model also combines a deep-and-narrow transformer design with Exclusive Self-Attention, grouped-query attention, and tied embeddings. Its compact vocabulary reduces the embedding footprint and leaves a larger share of the parameter budget available to the transformer layers.
Despite its relatively modest training budget, Limen0.2B achieves competitive results for its scale across the reported language understanding, commonsense reasoning, and grammatical evaluation tasks. Comparisons with other compact models are provided as context rather than strict rankings, as their training data, token budgets, architectures, and evaluation settings differ.
The release includes the model weights, implementation, training configuration, checkpoint progression, and evaluation results, all under Apache 2.0.
UniversalComputingResearch/Limen0.2B
Technical feedback, independent evaluations, and further experiments with the model and tokenizer are welcome.
reacted to AiNudizm's post with 🤗 about 10 hours ago
Post
63
I love the freedom of this platform. Thank you to all involved. Great ideas flowing!
reacted to SeaWolf-AI's post with 🔥 about 10 hours ago
Post
1827
POCKET now speaks Gemma 4 — a 26B model that loads in every app, and runs on your PC with no GPU
We're adding a Gemma-4 sibling to POCKET: POCKET-26B, built from Google's Gemma-4-26B-A4B (Apache-2.0). Our flagship POCKET-35B is a Qwen-family MoE and needs a recent llama.cpp; POCKET-26B trades a little size for the thing people kept asking for — it just loads, everywhere, today: Ollama, LM Studio, PocketPal, MLX, any stock llama.cpp. No fork, no bleeding-edge runtime, no CUDA, no cloud.
It's a sparse Mixture-of-Experts (25.2B total, ~4B active per token), so the work per token stays small — a real 26B that generates on a CPU with no graphics card.
Two things make it stand out:
1) Universal compatibility. Gemma 4 is a standard, widely-supported architecture, so POCKET-26B runs on the tools you already have — no waiting for your app to add a new model type.
2) Quality that survives compression. Measured GPQA-Diamond (198 q, greedy):
• Full base: 67.7%
• POCKET-26B Q4_K_M (17 GB): 67.7% — lossless
• POCKET-26B Q2_K (11 GB): 67.2% — near-lossless, at 11 GB
Live, on a CPU-only box (our demo Space — POCKET-26B vs Bonsai-27B, same machine, same stock llama.cpp): POCKET-26B ≈ 19 tok/s vs Bonsai ≈ 6 tok/s → about 3× faster generation, no GPU. (Honest notes: shared CPU box, sequential race; a dedicated machine is faster.)
Where it fits in the family:
• POCKET-35B (Qwen MoE) — bigger, top-tier, needs a recent llama.cpp.
• POCKET-26B (Gemma 4) — loads in any app, quality-robust when compressed. The demo runs the Q4_K_M build; Q2_K (11 GB) is the smallest footprint. For a true ≤8 GB phone, the 5 GB POCKET-KR (Qwen) is still the pick.
Try it and grab it:
🖥️ Live demo (Gemma4-based, answering on a CPU, no GPU): FINAL-Bench/POCKET-26B-CPU
📦 POCKET-26B-GGUF (Q4_K_M 17 GB · Q2_K 11 GB): FINAL-Bench/POCKET-26B-GGUF
📚 POCKET collection: https://huggingface.co/collections/FINAL-Bench/pocket-models
We're adding a Gemma-4 sibling to POCKET: POCKET-26B, built from Google's Gemma-4-26B-A4B (Apache-2.0). Our flagship POCKET-35B is a Qwen-family MoE and needs a recent llama.cpp; POCKET-26B trades a little size for the thing people kept asking for — it just loads, everywhere, today: Ollama, LM Studio, PocketPal, MLX, any stock llama.cpp. No fork, no bleeding-edge runtime, no CUDA, no cloud.
It's a sparse Mixture-of-Experts (25.2B total, ~4B active per token), so the work per token stays small — a real 26B that generates on a CPU with no graphics card.
Two things make it stand out:
1) Universal compatibility. Gemma 4 is a standard, widely-supported architecture, so POCKET-26B runs on the tools you already have — no waiting for your app to add a new model type.
2) Quality that survives compression. Measured GPQA-Diamond (198 q, greedy):
• Full base: 67.7%
• POCKET-26B Q4_K_M (17 GB): 67.7% — lossless
• POCKET-26B Q2_K (11 GB): 67.2% — near-lossless, at 11 GB
Live, on a CPU-only box (our demo Space — POCKET-26B vs Bonsai-27B, same machine, same stock llama.cpp): POCKET-26B ≈ 19 tok/s vs Bonsai ≈ 6 tok/s → about 3× faster generation, no GPU. (Honest notes: shared CPU box, sequential race; a dedicated machine is faster.)
Where it fits in the family:
• POCKET-35B (Qwen MoE) — bigger, top-tier, needs a recent llama.cpp.
• POCKET-26B (Gemma 4) — loads in any app, quality-robust when compressed. The demo runs the Q4_K_M build; Q2_K (11 GB) is the smallest footprint. For a true ≤8 GB phone, the 5 GB POCKET-KR (Qwen) is still the pick.
Try it and grab it:
🖥️ Live demo (Gemma4-based, answering on a CPU, no GPU): FINAL-Bench/POCKET-26B-CPU
📦 POCKET-26B-GGUF (Q4_K_M 17 GB · Q2_K 11 GB): FINAL-Bench/POCKET-26B-GGUF
📚 POCKET collection: https://huggingface.co/collections/FINAL-Bench/pocket-models
reacted to ProCreations's post with 🚀 about 10 hours ago
Post
575
what should i do for 200 followers... hmmmm... top reply to this post (most emojis) wins, just not anything too crazy
- 5 replies