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AI Models 82

• OpenCodeInterpreter-DS-6.7B-i1-GGUF↗

  mradermacher

• Nemotron-9B-OpenCode-i1-GGUF↗

  mradermacher

• OpenCodeReasoning-Nemotron-32B↗

  NVIDIA

• Nemotron-9B-OpenCode-GGUF↗

  mradermacher

• Qwen3.6-35B-A3B-MTP-GGUF↗

  unsloth

  MTP enables ~1.5-2x faster inference with no accuracy loss. You can now run Qwen3.6 MTP GGUFs in Unsloth Studio Unsloth Studio auto sets the ideal MTP settings for your hardware (Mac, CPU, GPU): Set `-DGGML_CUDA=OFF` for CPU/Metal. `-np > 1` and `--mmproj` are not yet supported with MTP. --- - Developer Role Support so Qwen3.6 can work in Codex , OpenCode and more! - Qwen3.6 can now be run and fine-tuned in Unsloth Studio . Read our guide . - Tool calling improvements: Makes parsing nested objects to make tool calling succeed more.

• DeepSeek-V4.1-Flash-UNCENSORED-DwarfStar-Q2↗

  pyrodog

  This is a community conversion of dealignAI's DeepSeek-V4.1-Flash-UNCENSORED-FP8 into the Q2 GGUF format that DwarfStar uses. **Why we're sharing it.** To save other people the trouble. We downloaded the source checkpoint, converted it on an Apple M5 Max MacBook Pro with 128 GB of unified memory, checked that the result worked, and hooked it up to OpenCode. If you want the same setup, you can download this file instead of pulling the roughly 510 GB source and spending several hours converting it yourself. This is an independent community effort, not an official release from DeepSeek, dealignAI, or DwarfStar. - **DeepSeek AI** built the original DeepSeek-V4.1-Flash model and its architecture. - **dealignAI** produced the abliterated checkpoint. Their model card credits **@dealignai and Jordan Schenck / @jordanschenck**. The abliteration is entirely their work. We didn't do it and don't take credit for it. - **Salvatore Sanfilippo (antirez) and the DwarfStar contributors** wrote the inference engine, the V4.1 converter, the quantization code, and the validation tools we used. DwarfStar in turn builds on **llama.cpp and the GGML authors**, whose copyright notices are kept in `LICENSE-DwarfStar`. - **pyrodog** ran the conversion, packaged it, did the local testing described below, and uploaded it, with help from OpenAI Codex. No training, fine-tuning, merging, or additional abliteration was done. The language weights are quantized from the source checkpoint; the optional vision tensors are copied unchanged in BF16.

• OpenCodeReasoning-Nemotron-1.1-7B-GGUF↗

  gabriellarson

• Qwen3.8-Flash-Next-REAP128-86B-Unity-OpenCode-GGUF↗

  talhasarac

• OpenCodeReasoning-Nemotron-1.1-7B↗

  NVIDIA

• MiniMax-M3-EAGLE3-GQA↗

  Inferact

  **Inferact/MiniMax-M3-EAGLE3-GQA** is a **grouped-query-attention (GQA)** EAGLE3 draft model for accelerating inference of MiniMax-M3, served with **vLLM** and trained with **TorchSpec**. It is **retrained on the same datasets** as the multi-head-attention version Inferact/MiniMax-M3-EAGLE3 — **kimi-mtp, OpenCodeInstruct, SWE-bench, and SWE-bench-Pro** — with the draft's attention changed from **MHA to GQA** (`num_key_value_heads: 64 → 4`) for **inference efficiency** (16× smaller draft KV cache) and **compatibility with the target model**. The draft is a 1-layer dense Llama (`LlamaForCausalLMEagle3`) on MiniMax-M3's `hidden_size=6144` / `vocab_size=200064`; at serve time it shares the target's embedding and LM head (EAGLE3). See `config.json` for the full architecture.