The next iteration of Qwythos: **all the reasoning of Qwythos-9B, with the looping behavior fixed.** v2 keeps the deep chain-of-thought, the uncensored research posture, and the 1M-token context of its predecessor, and cleans up the rough edges that showed up in real use. The fix uses **FTPO (Final-Token Preference Optimization)**: we identify the exact token that *starts* a repetition loop and gently train the model to prefer coherent alternatives at that one position, leaving the rest of the distribution — and therefore the model's knowledge and reasoning — untouched. Measured with our internal harness (generative chain-of-thought, greedy/pass@1 unless noted; MMLU/ARC/GSM8K n=500, GPQA-diamond n=198, HumanEval n=164). Judge for the quality metric: an independent LLM grader.
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View details ↗GGUF quantizations of **empero-ai/Qwythos-9B-v2** for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes. **Qwythos-9B-v2 is the new and improved Qwythos** — all the deep chain-of-thought reasoning of the base Qwythos, with the **looping behavior fixed**. The looping/degeneration that showed up under greedy or low-temperature decoding is trained out (**6.7% → 0%**), the native **MTP head is restored**, and the identity prompt is cleaned up — while knowledge and reasoning are held at (or above) the base Qwythos level. The fix uses **FTPO (Final-Token Preference Optimization)**: the exact token that *starts* a repetition loop is identified and the model is gently trained to prefer coherent alternatives at that one position, leaving the rest of the distribution — and therefore its knowledge and reasoning — untouched.
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View details ↗**gmcoder** is a 9B merged coding model developed with support from **Galactic Mandate Linux**. It is intended for code generation, debugging, explanations, and algorithmic problem solving. The reported HumanEval+ Mini result is competitive with the compared 9B coding models. On a separate internal knowledge and reasoning benchmark suite, gmcoder scored 30% higher than Qwen3.5-9B and Ornith-1.5-9B. Each model received one greedy completion per task at temperature 0. The run used EvalPlus 0.4.0.dev2 and all 164 HumanEval tasks. The gmcoder and internal comparator entries are Q8_0. The numbers are pass@1; HumanEval+ requires passing both the original and augmented tests. This is a 164-task, single-sample comparison. Oxcoder's HumanEval/132 response was manually skipped after it stopped progressing; a blank answer was scored as a failure, but the cause of that non-completion was not recorded. The internal comparator returned two empty answers; both counted as failures. These results measure short coding problems, not repository-level or agent performance.
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View details ↗This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model was trained on Turkish machine translated versions of NLI and STS-b datasets, using example training scripts from sentence-transformers GitHub repository. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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