Model Releases
VeriLoop E2 is now open on Hugging Face — curious what Ollama/GGUF quantization would make the most sense for this 27B model
Saw a new open 27B model release that looks like it could be interesting for the Ollama crowd: VeriLoop E2. It is a Qwen3.8-27B post-train aimed at code, mathematics, scientific reasoning, and agentic
Saw a new open 27B model release that looks like it could be interesting for the Ollama crowd: VeriLoop E2. It is a Qwen3.8-27B post-train aimed at code, mathematics, scientific reasoning, and agentic workloads. The current public release is full safetensors weights on Hugging Face. As far as I can tell, there is not yet an official GGUF/Ollama release, which is actually why I’m posting this here. The model has: 27B parameters Qwen3.8-27B base Apache-2.0 model weights 262K native context configuration shipped chat template public benchmark/evaluation evidence It also uses an external verifier/Harness design where model-generated candidates can be committed or rolled back based on external evidence instead of the model simply declaring success. The performance on 9 benchmarks looks promising as well: SWE-bench Pro 76.2% DeepSWE v1.1 64.6% SWE-Marathon v1.1 45.0% Terminal-Bench 2.1 88.8% Terminal-Bench 3.0 29.7% Terminal-Bench 4.0 37.9% AIME 2026 98.3% GPQA Diamond 93.9% MathArena Apex 2025 89.6% If this gets an Ollama/GGUF release, what would people here actually want first? Q4_K_M? Q5_K_M? Q6_K? Q8_0? And for a model around this size, which deployment target would be most useful to optimize for: 24 GB GPU 32–48 GB GPU Apple Silicon CPU/GPU offload high-context local inference It would be nice to see an Ollama release with actual tested context limits, memory numbers, prompt-template validation, hashes, and conversion provenance instead of just another unverified GGUF upload. Original model: https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2 Technical report: https://openreview.net/forum?id=P6FIQILHwX¬eId=P6FIQILHwX If anyone here ends up converting it before an official GGUF appears, I’d be interested in the memory usage, tokens/sec, and whether the shipped chat template behaves correctly under llama.cpp/Ollama. submitted by /u/PleaseLee [link] [comments]
Related
- Prism-ML Ollama Support
- I built a thing that benchmarks Ollama models on your own hardware and ranks them
- I built a desktop benchmark for local Ollama models — TTFT, tok/s, VRAM, with a HuggingFace GGUF catalogue
- Qwen 3.8 27B running on a single RTX 5090 researches and creates a full animation using only code.
Source: r/ollama | 2026-09-23