AI Model2026
Provides a full GGUF quant ladder of an "abliterated" Qwen3.8-27B for local llama.cpp inference — includes every K-quant, embedded MTP speculative head, and optional vision projectors; refusal behavior was reduced at the weight level, so validate before production.
AI Model2026
A 9B-parameter distillation that transfers chain-of-thought reasoning from Qwen3.8 into the Qwen3.5-9B architecture for single‑GPU deployment; trained on ~70,000 teacher traces, it offers 262k-token context, native function-calling, and improved MMLU performance.
AI Model2026
An FP8-quantized, uncensored mirror of Qwen3.8-27B for image-text-to-text tasks — preserves native multimodal vision and very long context while targeting transformers/vLLM deployments; intended for offline testing and red-teaming and may bypass built-in safety filters.
AI Model2026
A Gated-DeltaNet-aware mixed-precision GGUF quantization of Qwen3.8-27B for efficient local inference; preserves the MTP draft head and offers an optional BF16 mmproj for images. Weights are ~11.73 GiB (3.69 bpw), sized for 16–24 GB GPUs at modest context.
AI Dataset2026
A synthetic, verifiable-first agentic training corpus with 19,072 training traces and 2,135 held-out evaluation rows. Provides per-turn visible reasoning, real sandboxed tool executions, 13 verifiable task families, and NeMo Gym / RL-ready reward contracts for SFT and RL workflows.
AI Model2026
Local GGUF build of Qwen 3.8 27B with the refusal direction ablated for llama.cpp; includes vision projector (mmproj), MTP speculative head, a 262k context window and multiple quant tiers (Q2–Q8, F16). Research-only release that requires updated llama.cpp and explicit safety layers.
Analyzes how on-policy distillation (OPD) transfers teacher LLM capabilities to student models across in-domain shifts, cross-domain transfer, and multi-teacher settings. Key findings: OPD conveys reasoning patterns rather than specific answers, same-origin teacher-student pairs generalize broadly, and multi-teacher combinations induce mixture-dependent tradeoffs.
AI Model2026
Provides quantized GGUF variants of Qwen3.8-27B with an 'Aggressive' uncensoring profile and an optional HauhauCS FastMTP sidecar to accelerate MTP speculative decoding; includes a BF16 vision projector and K_P quant levels for VRAM/quality trade-offs.
AI Model2026
Post-trained Qwen3.8-27B variant using the COLD FUSION (GAIN+Unsloth) tuning to reduce internal reasoning-token use and improve instruction following while keeping base capabilities. Deliverables include 256k-context-compatible GGUF quants (regular and MTP, NEO IMATRIX), vision support via an mmproj, and three reasoning-effort modes (xhigh/medium/low).
AI Model2026
Provides a quantized MLX conversion of Qwen3.8-27B for Apple Silicon (2/4/6/8-bit) with the model's refusal-direction ablated, preserving multimodal vision+text capability; intended for red‑teaming, interpretability and safety research, not unmoderated production use.
AI Model2026
A 9B open-weight reasoning LLM that uses a self-improvement loop to auto-generate tasks, construct scaffolds, and optimize rollouts for stronger agentic coding and long-context reasoning. Single-GPU deployable, supports tool-calling and a 262,144-token context window.
AI Model2026
Provides an abliterated (refusal-removed) build of Qwen3.8-27B for offline research and red‑teaming, keeping multimodal vision, an MTP speculative head, and a 262,144-token context. It has no built-in safety guardrails and is released under Apache‑2.0 for research use only.