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AIモデルライブラリ/Schiltmans/Qwen3.8-Flash-Next-Coder-cut240
モデル情報

Qwen3.8-Flash-Next-Coder-cut240

Schiltmans · Qwen3.8-Flash-Next-Coder-cut240

ISTA-DASLab's IQ1_M Coder GGUF of Qwen3.8-Flash-Next (ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF at 5348543e) with 768 of its 12,288 routed experts removed, served through MLX as an OpenAI-compatible endpoint for coding agents. Each layer keeps 185 to 255 of 256 routed experts, 240 on average; kept tensors are byte for byte as published. This repository is the model folder and its runner in one; the runtime is a TensorFold fork that reads the GGUF tensors in place through mlx-kquant. The fork builds on TensorFold 0.3.4 (MIT) and carries its Python engine, which upstream later replaced with a native one. `DRAFTING=off ./serve.sh` turns drafting off and keeps the disconnect and title behaviour. Some `evidence/` pages were measured with drafting off and without the disconnect and title behaviour; each says which. Requires an Apple Silicon Mac with 48 GB (for 24 GB and 32 GB Macs, see below) and macOS 26 or later (measured on an M4 Pro), about 62 GB of free disk (57 GB model files, 2.2 GB head, about 1 GB environment), git and uv (the download below uses `uvx`; `install.sh` also works with python3.11). Close memory-heavy apps first: `serve.sh` will not start if macOS reports memory pressure or has less than an estimated 36 GB free or held by cached files. This is a startup check; it does not guarantee that memory will suffice later.

翻訳待ちのため原文を表示しています。

テキスト生成codelicense:other
コンテキスト長非公開
入力 → 出力テキスト → テキスト
モデルウェイト未確認
情報更新日時2026/10/30 06:37:43
OVERVIEW

モデル紹介

ISTA-DASLab's IQ1_M Coder GGUF of Qwen3.8-Flash-Next (ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF at 5348543e) with 768 of its 12,288 routed experts removed, served through MLX as an OpenAI-compatible endpoint for coding agents. Each layer keeps 185 to 255 of 256 routed experts, 240 on average; kept tensors are byte for byte as published. This repository is the model folder and its runner in one; the runtime is a TensorFold fork that reads the GGUF tensors in place through mlx-kquant. The fork builds on TensorFold 0.3.4 (MIT) and carries its Python engine, which upstream later replaced with a native one. `DRAFTING=off ./serve.sh` turns drafting off and keeps the disconnect and title behaviour. Some `evidence/` pages were measured with drafting off and without the disconnect and title behaviour; each says which. Requires an Apple Silicon Mac with 48 GB (for 24 GB and 32 GB Macs, see below) and macOS 26 or later (measured on an M4 Pro), about 62 GB of free disk (57 GB model files, 2.2 GB head, about 1 GB environment), git and uv (the download below uses `uvx`; `install.sh` also works with python3.11). Close memory-heavy apps first: `serve.sh` will not start if macOS reports memory pressure or has less than an estimated 36 GB free or held by cached files. This is a startup check; it does not guarantee that memory will suffice later.

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

出典・チャネル入力出力課金単位収集日時
確認可能な料金がありません。出典を確認してください。

月額の基本トークン費用を試算

—

試算はキャッシュ割引、段階料金、マルチモーダル追加料金、検索、税金、追加推論出力を含みません。

INDEPENDENT BENCHMARKS

公開評価と実行設定

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。

収録済みの全ランキング →

よくある質問

オープンウェイトなら無料で商用利用できますか？

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか？

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

huggingface · Schiltmans/Qwen3.8-Flash-Next-Coder-cut240 ↗2026/10/29 19:01:19
huggingface · enrich:ccf2b921-8dc9-4293-b031-0f51e7e045cc ↗2026/10/30 06:37:43

许可证：other。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。