DeepScaleR-1.5B-Preview is a language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning (RL) to scale up to long context lengths. The model achieves 43.1% Pass@1 accuracy on AIME 2024, representing a 15% improvement over the base model (28.8%) and surpassing OpenAI's O1-Preview performance with just 1.5B parameters. Our training dataset consists of approximately 40,000 unique problem-answer pairs compiled from: - AIME problems (1984-2023) - AMC problems (prior to 2023) - Omni-MATH dataset - Still dataset We employ Deepseek's Group Relative Policy Optimization (GRPO), a simplified RL algorithm that extends PPO by: - Normalizing advantage function over all samples generated from the same prompt. - Applying KL divergence regularization on top of PPO's surrogate loss to prevent significant policy drift.

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DeepSWE-Preview

agentica-org

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Qwen3.8-27B-AP-GGUF

agentionai

Agention Precision quants are the highest precision quants of Qwen3.8 27B byte-for-byte. Each file has a non-uniform assignment and low-loss error correction built with a custom encoder. This is a drop-in GGUF pack for Qwen3.8-27B: standard llama.cpp types, no fork, no flags. Vision projector and MTP draft head included. Every 27B quant gives something up. These give up less. We measured the leading public GGUFs against the full BF16 model on one protocol. At every size we ship, ours is the closest to Qwen’s next-token distribution.

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Qwen3.8-27B-DFlash2-ROCmFP4-FAST-GGUF

agentionai

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Qwen3.8-Flash-Next-AP-GGUF

agentionai

> **Updated 2026-10-13: `AP-Q5_K_M`.** Same file name, size and tensor types, better weights from an improved encode: > KL divergence **0.0771** and top-1 agreement **86.95 %** on held-out text, **0.0181** on code: > level with unsloth's UD-Q4_K_XL on held-out text and ahead on code, in the same VRAM. Download the file again. > > **Updated 2026-10-12: `AP-Q4_K_XL`.** KL divergence **0.0886** and top-1 agreement **86.14 %** on held-out text, > **0.0206** on code. 0 loops in a 45-run loop stress test. Quantized for accuracy per gigabyte of VRAM, and measured — every tier below is scored against the same reference on a held-out corpus. Method and full results . Each tier varies the quant type per tensor group rather than using one type throughout, to get the most accuracy per gigabyte of VRAM.

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Qwen3.8-Flash-Next-Gyro-Coder-GGUF

agentionai

Qwen3.8-Flash-Next is a 125B mixture-of-experts model, the kind that normally needs a workstation full of GPUs. Gyro-Coder brings it to a single RTX 3090, 4090 or 7900 XTX: 19.9 GiB of GPU memory. **New: Gyro-Coder-L for 32 GB cards.** The same experts at about 3.2 bits per weight: 27.5 GiB of GPU memory, and closer to the full model than Gyro-Coder on every set we measure. On C++ it is closer than Gyro-M, which keeps all 512 experts. **Updated 2026-10-29: Gyro-Coder-L v1.2** (`Qwen3.8-Flash-Next-Gyro-Coder-L-TQ3_0.gguf`, same name and size). Thanks to a detailed community report, three experts that matter for tool calls and symbols are back in the expert set, in place of three that did less. Tool calls no longer break under sampling: across 100 `<parameter=` sites in real agent sessions, v1.0 wrote a broken tag at 4 and v1.2 at none. Roman numerals are fixed too, and KLD is unchanged on every set. v1.2 replaces both v1.0 and the v1.1 preview.

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Qwen3.8-Flash-Next-MTP-Q8_0-GGUF

agentionai

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Qwen3.8-Flash-Next-MTP-ROCmFP4-FAST-GGUF

agentionai

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Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF

agentionai

A 180 B model that runs **entirely on the GPU** of a 128 GB unified-memory box — 87.06 GiB at 4.23 bpw, within 2.5% perplexity of the unquantized model. Sized for the 96 GiB VRAM carve-out of a Strix Halo (Ryzen AI MAX+ 395 / Radeon 8060S). *Throughput against context depth, against our closest mainline-compatible quant run on both this fork and stock llama.cpp — numbers and discussion under Holding up at long context.* - **Everything stays on the GPU.** Not the experts, not the 51.2 B-parameter n-gram table. No host-RAM fallback, no CPU compute in the hot path. - **Fast prefill that holds at depth** — 423 t/s at 512 tokens, 357 t/s at 8k, still 245 t/s at 32k and 138 t/s at 128k. Prefill is compute-bound and the ROCmFP4 types are cheaper to dequantize. Matters most for long prompts, agentic loops, and anything that re-reads a large context. - **Better perplexity than that same mainline tier**, by about two standard errors, and better than AesSedai's IQ4_XS and IQ3_S at 20–30 GB smaller. - **Vision included** — the model's own tower, at f16, losslessly converted. - **Speculative decoding included** — the model's own MTP head. Up to **40 tok/s generation** observed on a Strix Halo with adaptive drafting; acceptance is content-dependent, so the rate moves with what you are generating. - **imatrix-calibrated** on 1540 chunks from two corpora, so the low-bit tensors are fitted to real activations rather than assumed.

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siba-meristem-native-0.5b-base

aghasalim

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Agente-GPT-Qwen2.5-3B-Instruct-Spanish_8bit

Agnuxo

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CAJAL-4B

Agnuxo

> **P2PCLAW** is the world's first decentralized autonomous peer-review network for scientific research. **Francisco Angulo de Lafuente** (Agnuxo1) - Spanish independent researcher, 35 years trajectory - Author of "La Reliquia" and "Ecofa" - ORCID: 0009-0001-1634-7063 - Wikipedia: Francisco Angulo de Lafuente **Collaborators:** - Vladimir Veselov (MIET, Moscow) - Seid Mehammed Abdu (Woldia University, Ethiopia) - Nirmal Tej Kumar (UT Dallas)

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