Cloudflare/clef-flash quantized for llama.cpp. The source is a 9.4B-parameter model (architecture `Qwen3_5ForConditionalGeneration`, 32 layers, hidden size 4096, vocab 248,320), post-trained by Cloudflare from Qwen/Qwen3.5-9B and released under Apache-2.0. This repo has 30 verified files: 29 GGUF quants plus the full-precision BF16 source. Clef-Flash is a **decision model**: it reads a state (text, JSON, images, or video) plus a schema of typed questions and returns a probability for every allowed option of every question in a single forward pass — no free-form text generation, no output parsing. That behavior comes from a backbone plus a separate **joint schema head** (and optionally the vision encoder). For the full decision-model behavior, use Cloudflare/clef-flash directly with `transformers` (5.10.2) and `torch` (2.11) per its README. What you get from this repo in llama.cpp is the post-trained Qwen3.5-9B language tower.
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This repo contains llama.cpp GGUF conversions of **microsoft/FrogNano-4B-2609** — Microsoft Corporation's compact repository-level coding agent (released 22-SEP-2026). All 29 standard quant types are here, each cut from the BF16 checkpoint with an importance matrix computed on those same BF16 weights, plus the BF16 file itself and the vision-tower export, quantized 2026-10-04. These files are llama.cpp GGUF. They are **not** loaded by vLLM, SGLang or Transformers the way the source repo's safetensors are — use `llama-cli`, `llama-server`, or another GGUF runtime. From Microsoft's model card: FrogNano is a repository-level coding agent derived from **Qwen/Qwen3.5-4B**, post-trained exclusively with reinforcement learning on ~1,500 synthetic software-engineering tasks that were generated, validated, and calibrated against the evolving policy using TaskPilot. It operates through the lightweight five-tool **Leaf** harness (file read/write/edit, glob-based search, shell execution) inside an isolated repository sandbox. A distinctive choice: the agent post-training used **no stronger-model solution trajectories, actions, reasoning traces, or patch targets** — the model learned from its own policy's trajectories, rewarded by executable test results.
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GGUF quantizations of IFM/K2-Horizon-3.7B, a dense 3.7B-parameter causal decoder (`K2HorizonForCausalLM`, `model_type: k2_horizon`). Quantized by **NANI-Nithin** using a custom pipeline built on the MBZUAI-IFM llama.cpp fork (branch `model/K2Horizon`). > **Note on file naming:** IFM's official GGUF repo ships this model as > `K2-Horizon-4B-BF16.gguf` (rounded name). Files in this repo are renamed > to the `K2-Horizon-3.7B-*` convention for consistency.
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GGUF quantizations of IFM/K2-Horizon-MoVA-36B-A4B, produced with the MBZUAI-IFM fork of llama.cpp (`model/K2Horizon` branch) that adds native `k2-horizon` architecture support. > **Architecture note:** Upstream llama.cpp does not yet support the `k2_horizon` / > `K2HorizonForCausalLM` architecture (PR in progress as of September 2026). > You must use the MBZUAI-IFM fork linked above — or upstream once the PR merges — > to load these GGUFs. K2-Horizon-MoVA-36B-A4B is the sparse member of the K2-Horizon family released by the Institute of Foundation Models (IFM) at MBZUAI on 3 September 2026. It is a **Mixture-of-Experts (MoE)** model augmented with a novel **Mixture-of-Values (MoVA)** attention mechanism, storing ~36 B parameters while activating only ~4 B per token.
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Quantized GGUF files for **Ling-3.0-tiny**, IBM's lightweight hybrid reasoning MoE model optimized for deployment with llama.cpp. This model delivers strong reasoning and agentic capabilities at low inference cost through an efficient hybrid architecture combining KDA and MLA attention with a sparse MoE FFN. **Ling-3.0-tiny** is a 7.9B parameter model with only 1.3B activated parameters per token, designed for efficient local and edge deployment. It features: *All files are cut from the BF16 source (14.72 GB). Each quant is independently uploaded and deleted immediately after successful upload to minimize peak disk usage.*
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GGUF builds of meta-models/Muse-Glimmer-30B — Meta Superintelligence Lab's ~30B multimodal agent model, distilled from Muse Spark and purpose-built for autonomous agentic work on consumer hardware. Every file here was cut from the same BF16 source weights; the repo holds full-precision copies, a vision projector, and 29 quants from Q8_0 down to Q1_0. Quantized 2026-09-07. **These are llama.cpp GGUF files.** They are not loaded by vLLM, SGLang or `transformers` the way the source repo is. Muse Glimmer is a 30B-class dense causal transformer with a dedicated perception encoder (image + text input, text output), trained for the capabilities an autonomous agent needs working together:
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