Spark-X2.5-4B-abliterated-FIT-GGUF

모델 소개

FIT-GGUF v0.2.0 GATE-VERIFIED TIERS 4 TIERS · 2.26–3.15 GiB ABLITERATED (T615) MEASURED KL + SAME-TOP APACHE-2.0 Spark-X2.5-4B-abliterated · FIT-GGUF Four fidelity tiers of a 1M-context hybrid-attention 4B — every shipped file re-verified against its own BF16, byte-exact and reproducible. 🧭 About FIT-GGUF — the tool behind these files Every file in this repository was planned, executed and verified by FIT-GGUF , an open-source, deterministic tensor-level planning layer on top of llama.cpp quantization. Standard GGUF quantization asks you to pick one of a handful of presets; FIT-GGUF instead asks what quality do you want , then finds and verifies the smallest GGUF that demonstrably meets it. Traditional GGUF gives you presets. FIT gives you a fidelity contract: macro KL ≤ tier anchor ∧ same-top ≥ model-calibrated floor. Deterministic size prediction & byte-exact delivery ✅ Validated (G2 gate, delta = 0) Universally optimal tensor allocation ⚠️ Not established — FIT claims verified fidelity contracts, not a universal quality optimum The method, the preregistered research record and the fit CLI are open source: github.com/Scorp1o117/FIT-GGUF ⚠️ Safety notice / 安全提示 The source model is an abliterated, refusal-removed model with no meaningful built-in guardrails, and may comply with harmful, illegal or unsafe requests. Use it only where you can provide appropriate moderation, access control and legal review. Do not deploy it to end users without your own safety layer. 源模型经过拒答方向消融，不具备可靠的内置安全护栏；请仅在合法、受控、具备审核与滥用防护的环境中使用，使用者自行承担部署责任。

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。