From 99cec097b6de089c74dfadb834b219ae3e6c1eec Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 10:18:43 +0000 Subject: [PATCH] docs: make Chinese README the default --- README.md | 327 +++++++++++++++++++++++++----------------------------- 1 file changed, 154 insertions(+), 173 deletions(-) diff --git a/README.md b/README.md index e38d62f..90b3d81 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,9 @@ + +> [!NOTE] +> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。 +> [English](./README.en.md) · [原始项目](https://github.com/Andyyyy64/whichllm) · [上游 README](https://github.com/Andyyyy64/whichllm/blob/HEAD/README.md) +> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。 + # whichllm [![PyPI version](https://img.shields.io/pypi/v/whichllm)](https://pypi.org/project/whichllm/) @@ -10,65 +16,62 @@ Andyyyy64%2Fwhichllm | Trendshift

-**Find the best local LLM that actually runs on your hardware.** +**找到真正能在你硬件上运行的最佳本地 LLM。** -Auto-detects your GPU/CPU/RAM and ranks the top models from HuggingFace that fit your system. +自动检测你的 GPU/CPU/RAM，并从 HuggingFace 中按适配度对适合你系统的顶级模型进行排名。 [日本語版はこちら](docs/README.ja.md) -## Quick start +## 快速开始 -Run the recommendation command once, with no project setup. +无需项目配置，运行一次推荐命令即可。 ```bash uvx whichllm@latest ``` -Simulate a GPU before you buy hardware. +在购买硬件之前模拟 GPU。 ```bash uvx whichllm@latest --gpu "RTX 4090" ``` -Install it when you use it often. +若经常使用，可安装到本地。 ```bash uv tool install whichllm uv tool upgrade whichllm # update an existing install ``` -Other install paths. +其他安装方式。 ```bash brew install andyyyy64/whichllm/whichllm pip install whichllm ``` -## Want a safer pick? +## 想要更稳妥的选择？ -By default, whichllm is ambitious. It ranks the best model that looks runnable -on your machine, including partial RAM offload and near-edge VRAM fits when -they seem usable. +默认情况下，whichllm 较为激进。它会对你机器上看起来可运行的最佳模型进行排名，包括部分 RAM 卸载（offload）以及看似可用的接近显存上限的 VRAM 配置。 -If you want a more comfortable LM Studio-style recommendation, start with: +若你想要更接近 LM Studio 风格的舒适推荐，可从以下方式开始： ```bash uvx whichllm@latest --gpu-only --speed usable --vram-headroom 1GB ``` -This keeps only models that fit fully in GPU VRAM, filters out slow estimates, -and leaves extra VRAM for runtime overhead. +这样只会保留完全装入 GPU VRAM 的模型，过滤掉速度较慢的估算结果，并预留额外 VRAM 以应对运行时开销。 -If LM Studio still says the model is slightly too large, increase the headroom: +若 LM Studio 仍提示模型略大，可增加余量： ```bash uvx whichllm@latest --gpu-only --speed usable --vram-headroom 1.5GB ``` -## Common workflows +## 常见工作流 -After install, run `whichllm` directly. For one-off runs, replace `whichllm` -with `uvx whichllm@latest`. +安装后，直接运行 `whichllm`。若只需一次性运行，将 `whichllm` +替换为 `uvx whichllm@latest`。 ```bash # Best models for this machine @@ -112,7 +115,7 @@ whichllm --top 1 --json ![demo](assets/demo.gif) -## See it +## 眼见为实 ```text $ whichllm --gpu "RTX 4090" @@ -122,16 +125,14 @@ $ whichllm --gpu "RTX 4090" #3 Qwen/Qwen3-30B-A3B 30.0B Q5_K_M score 82.7 102 t/s ``` -The 32B model **fits your card fine** — whichllm still ranks the 27B #1, -because it scores higher on real benchmarks and is a newer generation. -A size-only "what fits?" tool would hand you the bigger one. That gap is -the whole point of whichllm. (Note #3: a MoE model at 102 t/s — speed is -ranked on *active* params, quality on *total*.) +32B 模型**完全适配你的显卡**——whichllm 仍将 27B 排在第 1 位， +因为它在真实基准测试中得分更高，且属于更新一代。 +仅按尺寸判断「能跑什么？」的工具会把更大的模型推荐给你。这种差距正是 whichllm 的价值所在。（注 #3：MoE 模型达 102 t/s——速度按*活跃*参数量排名，质量按*总*参数量评估。） -## What can I run? +## 我能运行什么？ -Real top picks (snapshot 2026-05 — your results track **live** HuggingFace -data, this is not a static list): +真实精选推荐（快照 2026-05——你的结果会跟踪 **实时** HuggingFace +数据，这不是静态列表）： | Hardware | VRAM | Top pick | Speed | |---|---|---|---| @@ -141,68 +142,55 @@ data, this is not a static list): | Apple M3 Max | 36 GB | `Qwen3.6-27B` · Q5_K_M · score 89.4 | ~9 t/s | | CPU only | — | `gpt-oss-20b` (MoE) · Q4_K_M · score 45.2 | ~6 t/s | -`whichllm --gpu ""` simulates any of these before you buy. -By default, rankings include full-GPU, partial-offload, and CPU-only -candidates when they are usable. Use `--gpu-only` or `--fit full-gpu` when -you only want models that fit entirely in GPU VRAM. -The default table shows memory, estimated generation speed, fit type, and -published date. Speed is colored by practical usability: under 4 tok/s is red, -4-10 is yellow, 10-30 is green, and 30+ is bright green. `~` / `?` still mark -estimate confidence. +`whichllm --gpu ""` 可在购买前模拟上述任意配置。 +默认情况下，排名会包含全 GPU、部分卸载和仅 CPU 的候选（在可用时）。若你只想选择完全装入 GPU VRAM 的模型，请使用 `--gpu-only` 或 `--fit full-gpu`。 +默认表格会显示内存、预估生成速度、适配类型和发布日期。速度按实际可用性着色：低于 4 tok/s 为红色， +4-10 为黄色，10-30 为绿色，30+ 为亮绿色。`~` / `?` 仍会标注 +估算置信度。 -## Why whichllm? +## 为什么选择 whichllm？ -Fitting a model into your VRAM is the easy part. The hard part is knowing -**which of the models that fit is actually the best** — and that is what -whichllm is built to get right. +把模型塞进 VRAM 并不难。难的是知道**在能跑的模型里，哪一个才是真正最好的**——而这正是 +whichllm 要解决的问题。 -- **Evidence-based ranking, not a size heuristic** — The top pick is - chosen from merged real benchmarks (LiveBench, Artificial Analysis, - Aider, multimodal/vision, Chatbot Arena ELO, Open LLM Leaderboard) — - never "the biggest model that happens to fit." -- **Recency-aware** — Stale leaderboards are demoted along each model's - lineage, so a 2024 model can't outrank a current-generation one on an - outdated score. The benchmark snapshot date is printed under every - ranking, so a stale recommendation is self-evident instead of silently - trusted. -- **Evidence-graded and guarded** — Every score is tagged - `direct` / `variant` / `base` / `interpolated` / `self-reported` and - discounted by confidence. Fabricated uploader claims and cross-family - inheritance (a small fork borrowing its much larger base's score) are - actively rejected. -- **Architecture-aware estimates** — VRAM = weights + GQA KV cache + - activation + overhead; speed is bandwidth-bound with per-quant - efficiency, per-backend factors, MoE active-vs-total split, and - unified-memory vs discrete-PCIe partial-offload modeling. -- **One command, scriptable** — `whichllm` prints the answer; add - `--json | jq` for pipelines. No TUI, no keybindings to memorize. -- **Live data** — Models fetched directly from the HuggingFace API, with - curated frozen fallbacks for offline or rate-limited use. +- **基于证据的排名，而非尺寸启发式** —— 首选来自合并的真实基准测试（LiveBench、Artificial Analysis、 + Aider、多模态/视觉、Chatbot Arena ELO、Open LLM Leaderboard）—— + 绝不是「碰巧能塞进去的最大模型」。 +- **重视时效性** —— 过时的排行榜会沿各模型谱系降权，因此 2024 年的模型不能凭过时分数压过当前一代。 + 每次排名下方都会打印基准快照日期，过时推荐一目了然，而非被默默采信。 +- **证据分级与防护** —— 每个分数都会标注 + `direct` / `variant` / `base` / `interpolated` / `self-reported`，并 + 按置信度折减。上传者的虚假声明和跨系列继承（小分支借用大得多的基座分数）会被主动拒绝。 +- **架构感知的估算** —— VRAM = 权重 + GQA KV cache + + 激活 + 开销；速度受带宽限制，并考虑各量化效率、各后端因子、MoE 活跃/总参数拆分，以及统一内存与离散 PCIe 部分卸载建模。 +- **一条命令，可脚本化** —— `whichllm` 直接输出答案；添加 + `--json | jq` 即可接入流水线。无 TUI，无需记忆快捷键。 +- **实时数据** —— 模型直接从 HuggingFace API 获取，并为离线或限流场景提供精选的冻结回退数据。 -## Features +## 功能特性 -- **Auto-detect hardware** — NVIDIA, AMD, Intel, Apple Silicon, CPU-only -- **Smart ranking** — Scores models by VRAM fit, speed, and benchmark quality -- **One-command chat** — `whichllm run` downloads and starts a chat session instantly -- **Code snippets** — `whichllm snippet` prints ready-to-run Python for any model -- **Live data** — Fetches models directly from HuggingFace (cached for performance) -- **Benchmark-aware** — Integrates real eval scores with confidence-based dampening -- **Task profiles** — Filter by general, coding, vision, or math use cases -- **GPU simulation** — Test with any GPU: `whichllm --gpu "RTX 4090"` -- **Multi-GPU simulation** — Repeat `--gpu`, use commas, or write `2x RTX 4090` -- **Full-GPU filter** — `--gpu-only` / `--fit full-gpu` hides offload candidates -- **Speed-aware filtering** — `--speed usable|fast` hides slow rows by threshold -- **Markdown output** — `--markdown` / `-m` prints pasteable GFM tables -- **Runtime memory budgets** — `--vram-headroom` and `--ram-budget` avoid edge fits -- **Hardware planning** — Reverse lookup: `whichllm plan "llama 3 70b"` -- **Upgrade planning** — Compare your current machine with candidate GPUs -- **JSON output** — Pipe-friendly: `whichllm --json` +- **自动检测硬件** —— NVIDIA、AMD、Intel、Apple Silicon、仅 CPU +- **智能排名** —— 按 VRAM 适配、速度和基准质量为模型打分 +- **一条命令聊天** —— `whichllm run` 即时下载并启动聊天会话 +- **代码片段** —— `whichllm snippet` 为任意模型输出可直接运行的 Python 代码 +- **实时数据** —— 直接从 HuggingFace 获取模型（带缓存以提升性能） +- **基准感知** —— 整合真实评测分数，并按置信度衰减 +- **任务配置** —— 按通用、编程、视觉或数学场景筛选 +- **GPU 模拟** —— 用任意 GPU 测试：`whichllm --gpu "RTX 4090"` +- **多 GPU 模拟** —— 重复 `--gpu`、使用逗号分隔，或编写 `2x RTX 4090` +- **全 GPU 筛选** —— `--gpu-only` / `--fit full-gpu` 隐藏卸载候选 +- **速度感知筛选** —— `--speed usable|fast` 按阈值隐藏较慢行 +- **Markdown 输出** —— `--markdown` / `-m` 输出可粘贴的 GFM 表格 +- **运行时内存预算** —— `--vram-headroom` 和 `--ram-budget` 避免临界适配 +- **硬件规划** —— 反向查询：`whichllm plan "llama 3 70b"` +- **升级规划** —— 将当前机器与候选 GPU 对比 +- **JSON 输出** —— 便于管道处理：`whichllm --json` -## Run & Snippet +## 运行与代码片段 -Try any model with a single command. No manual installs needed — whichllm -creates an isolated environment via `uv`, installs dependencies, downloads the -model, and starts an interactive chat. +用一条命令试用任意模型。无需手动安装——whichllm +通过 `uv` 创建隔离环境，安装依赖，下载 +模型，并启动交互式聊天。 ![run demo](assets/demo-run.gif) @@ -217,12 +205,12 @@ whichllm run whichllm run "phi 3 mini gguf" --cpu-only ``` -Works with **all model formats**: -- **GGUF** — via `llama-cpp-python` (lightweight, fast) -- **AWQ / GPTQ** — via `transformers` + `autoawq` / `auto-gptq` -- **FP16 / BF16** — via `transformers` +支持**所有模型格式**： +- **GGUF** — 通过 `llama-cpp-python`（轻量、快速） +- **AWQ / GPTQ** — 通过 `transformers` + `autoawq` / `auto-gptq` +- **FP16 / BF16** — 通过 `transformers` -Get a **copy-paste Python snippet** instead: +也可直接获取**可复制粘贴的 Python 代码片段**： ```bash whichllm snippet "qwen 7b" @@ -245,7 +233,7 @@ output = llm.create_chat_completion( print(output["choices"][0]["message"]["content"]) ``` -## Usage +## 用法 ```bash # Auto-detect hardware and show best models @@ -318,26 +306,25 @@ whichllm snippet "qwen 7b" whichllm snippet "llama 3 8b gguf" --quant Q5_K_M ``` -Markdown output is intended for GitHub issues, READMEs, Slack, Discord, and -blog posts: +Markdown 输出适用于 GitHub issues、README、Slack、Discord 和博客文章： ```bash whichllm --markdown whichllm -m --top 5 --gpu "RTX 4090" ``` -JSON model rows include `fit_type`, `vram_required_bytes`, -`vram_available_bytes`, `uses_multi_gpu`, `multi_gpu_effective_vram_bytes`, -`estimated_tok_per_sec`, `speed_confidence`, `speed_range_tok_per_sec`, -`speed_notes`, `benchmark_source`, and `benchmark_confidence`. The speed range -is a planning range, not a live benchmark. +JSON 模型行包含 `fit_type`、`vram_required_bytes`、 +`vram_available_bytes`、`uses_multi_gpu`、`multi_gpu_effective_vram_bytes`、 +`estimated_tok_per_sec`、`speed_confidence`、`speed_range_tok_per_sec`、 +`speed_notes`、`benchmark_source` 以及 `benchmark_confidence`。速度范围 +是规划参考区间，而非实时基准测试结果。 -## Integrations +## 集成 ### Ollama -Use JSON output to feed scripts that map HuggingFace IDs to your local Ollama -model names: +使用 JSON 输出为脚本提供数据，将 HuggingFace ID 映射到本地 Ollama +模型名称： ```bash # Pick the top HuggingFace model ID @@ -347,95 +334,90 @@ whichllm --top 1 --json | jq -r '.models[0].model_id' whichllm --profile coding --top 1 --json | jq -r '.models[0].model_id' ``` -Ollama model names do not always match HuggingFace repo IDs, so a small mapping -step is usually needed before `ollama run`. +Ollama 模型名称并不总与 HuggingFace 仓库 ID 一致，因此在执行 `ollama run` 之前通常需要一步简单映射。 -### Shell alias +### Shell 别名 -Add to your `.bashrc` / `.zshrc`: +添加到你的 `.bashrc` / `.zshrc`： ```bash alias bestllm='whichllm --top 1 --json | jq -r ".models[0].model_id"' # Usage: ollama run $(bestllm) ``` -## Scoring +## 评分 -Each model gets a 0-100 score. Benchmark quality and size form the core; -evidence confidence and runtime fit then scale it, with speed, source -trust, and popularity as adjustments. +每个模型获得 0–100 分。基准测试质量与模型规模构成核心； +证据置信度与运行时适配随后进行缩放，速度、来源可信度与流行度作为调整项。 -| Factor | Effect | Description | +| 因素 | 作用 | 说明 | |--------|--------|-------------| -| Benchmark quality | core | Merged LiveBench / Artificial Analysis / Aider / Vision / Arena ELO / Open LLM Leaderboard, weighted by source confidence | -| Model size | up to 35 | `log2`-scaled world-knowledge proxy (MoE uses total params) | -| Quantization | × penalty | Lower-bit quants discounted multiplicatively | -| Evidence confidence | ×0.55–1.0 | none / self-reported ×0.55, inherited ×0.78, direct full | -| Runtime fit | ×0.50–1.0 | partial-offload ×0.72, CPU-only ×0.50 | -| Speed | -8 to +8 | Usability gate vs a fit-dependent tok/s floor; reported with confidence and range metadata | -| Source trust | -5 to +5 | Official-org bonus, known-repackager penalty | -| Popularity | tie-breaker | Downloads/likes; weight shrinks as evidence strengthens | +| 基准测试质量 | 核心 | 合并 LiveBench / Artificial Analysis / Aider / Vision / Arena ELO / Open LLM Leaderboard，按来源置信度加权 | +| 模型规模 | 最高 35 | 经 `log2` 缩放的世界知识代理指标（MoE 使用总参数量） | +| 量化 | × 惩罚 | 低位数量化按乘法折减 | +| 证据置信度 | ×0.55–1.0 | 无 / 自报 ×0.55，继承 ×0.78，直接完整 | +| 运行时适配 | ×0.50–1.0 | 部分卸载（partial-offload）×0.72，仅 CPU ×0.50 | +| 速度 | -8 至 +8 | 可用性门槛对比与适配类型相关的 tok/s 下限；附带置信度与区间元数据报告 | +| 来源可信度 | -5 至 +5 | 官方机构加分，已知重打包者减分 | +| 流行度 | 决胜项 | 下载量/点赞数；证据越强权重越小 | -Score markers: -- **`~`** (yellow) — No direct benchmark; score inherited/interpolated from the model family -- **`!sr`** (bright yellow) — Uploader-reported benchmark only, not independently verified -- **`?`** (red) — No benchmark data available +评分标记： +- **`~`**（黄色）— 无直接基准测试；分数从模型家族继承/插值 +- **`!sr`**（亮黄色）— 仅有上传者报告的基准测试，未经独立验证 +- **`?`**（红色）— 无可用基准测试数据 -Speed display: -- **red** — Slow generation speed (`<4 tok/s`) -- **yellow** — Marginal generation speed (`4-10 tok/s`) -- **green** — Usable generation speed (`10-30 tok/s`) -- **bright green** — Fast local generation speed (`>=30 tok/s`) -- **`~`** (yellow) — Estimated tok/s range is available -- **`?`** (red) — Low-confidence speed estimate; backend/runtime sensitivity is high +速度显示： +- **red** — 生成速度较慢（`<4 tok/s`） +- **yellow** — 生成速度勉强可用（`4-10 tok/s`） +- **green** — 生成速度可用（`10-30 tok/s`） +- **bright green** — 本地生成速度快（`>=30 tok/s`） +- **`~`**（黄色）— 提供估算的 tok/s 区间 +- **`?`**（红色）— 低置信度速度估算；对后端/运行时敏感度高 -## Documentation +## 文档 -- [CLI reference](docs/cli.md) -- [How it works](docs/how-it-works.md) -- [Scoring](docs/scoring.md) -- [Hardware detection and simulation](docs/hardware.md) -- [Run and snippet](docs/run-snippet.md) -- [Troubleshooting](docs/troubleshooting.md) +- [CLI 参考](docs/cli.md) +- [工作原理](docs/how-it-works.md) +- [评分](docs/scoring.md) +- [硬件检测与模拟](docs/hardware.md) +- [运行与代码片段](docs/run-snippet.md) +- [故障排除](docs/troubleshooting.md) -## How it works +## 工作原理 -### Data pipeline +### 数据流水线 -1. **Model fetching** — Fetches popular models from HuggingFace API: - - Text-generation (downloads + recently updated) - - GGUF-filtered (separate query for coverage) - - Vision models (`image-text-to-text`) when `--profile vision` or `any` -2. **Benchmark sources** — *Current tier* (LiveBench, Artificial Analysis - Index, Aider) merged live when reachable, plus a curated multimodal / - vision index; *frozen tier* (Open LLM Leaderboard v2, Chatbot Arena - ELO). Tiers have separate caps and lineage-aware recency demotion so - stale leaderboards stop over-rewarding older generations. -3. **Benchmark evidence** — Five resolution levels, increasingly discounted: - - `direct` — Exact model ID match - - `variant` — Suffix-stripped or -Instruct variant - - `base_model` — Base model from cardData - - `line_interp` — Size-aware interpolation within model family - - `self_reported` — Uploader-claimed eval (heavily discounted) +1. **模型获取** — 从 HuggingFace API 获取热门模型： + - 文本生成（按下载量 + 最近更新） + - GGUF 过滤（单独查询以覆盖） + - 视觉模型（`image-text-to-text`），当 `--profile vision` 或 `any` 时 +2. **基准测试来源** — *当前层级*（LiveBench、Artificial Analysis + Index、Aider）在可访问时实时合并，外加精选的多模态 / + 视觉指数；*冻结层级*（Open LLM Leaderboard v2、Chatbot Arena + ELO）。各层级设有独立上限，并配合谱系感知的时效性降权，使过时排行榜不再过度奖励旧代模型。 +3. **基准测试证据** — 五个解析层级，折扣递增： + - `direct` — 精确模型 ID 匹配 + - `variant` — 去除后缀或 -Instruct 变体 + - `base_model` — 来自 cardData 的基础模型 + - `line_interp` — 模型家族内按规模感知的插值 + - `self_reported` — 上传者声称的评测（大幅折减） - Inheritance is rejected when a model's params diverge more than 2× from - its family's dominant member, catching draft / MTP / abliterated forks - that share a `family_id` with a much larger base. -4. **Cache** — normally `~/.cache/whichllm/`, or `$XDG_CACHE_HOME/whichllm/` - when `XDG_CACHE_HOME` is set to an absolute path: + 当模型参数量与其家族主导成员相差超过 2× 时，拒绝继承， + 以捕获与更大基础模型共享 `family_id` 的 draft / MTP / abliterated 分支。 +4. **缓存** — 通常为 `~/.cache/whichllm/`，或将 `XDG_CACHE_HOME` 设为绝对路径时使用 `$XDG_CACHE_HOME/whichllm/`： - `models.json` — 6h TTL - `benchmark.json` — 24h TTL -### Ranking engine +### 排序引擎 -1. **Hardware detection** — NVIDIA (nvidia-ml-py), AMD (ROCm/dbgpu), Intel, Apple Silicon (Metal), CPU cores, RAM, disk -2. **VRAM estimation** — Weights + KV cache + activation + framework overhead (~500MB) -3. **Compatibility** — Full GPU / Partial Offload / CPU-only; compute capability and OS checks -4. **Speed** — tok/s from GPU memory bandwidth, quantization, backend, fit type, and MoE active parameters -5. **Scoring** — Benchmark (with confidence dampening), size, quantization penalty, fit type, speed, popularity, source trust (official vs repackager) -6. **Backend filter** — Apple Silicon and CPU-only restrict to GGUF for stability; Linux+NVIDIA allows AWQ/GPTQ +1. **硬件检测** — NVIDIA（nvidia-ml-py）、AMD（ROCm/dbgpu）、Intel、Apple Silicon（Metal）、CPU 核心数、RAM、磁盘 +2. **VRAM 估算** — 权重 + KV cache + 激活值 + 框架开销（约 500MB） +3. **兼容性** — 全 GPU / 部分卸载（Partial Offload）/ 仅 CPU；计算能力与操作系统检查 +4. **速度** — 根据 GPU 内存带宽、量化、后端、适配类型与 MoE 活跃参数量计算 tok/s +5. **评分** — 基准测试（含置信度衰减）、规模、量化惩罚、适配类型、速度、流行度、来源可信度（官方 vs 重打包者） +6. **后端过滤** — Apple Silicon 与仅 CPU 为稳定性限制为 GGUF；Linux+NVIDIA 允许 AWQ/GPTQ -### Project structure +### 项目结构 ``` src/whichllm/ @@ -484,19 +466,18 @@ uv run pytest ## Contributing -Contributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. +欢迎贡献！请参阅 [CONTRIBUTING.md](CONTRIBUTING.md) 了解指南。 ## Support -If whichllm helped you find a model or avoid a bad hardware guess, -sponsoring is appreciated. It helps keep the project maintained: hardware -reports, packaging, test fixtures, benchmark updates, and support for more -machines. +如果 whichllm 帮你找到了合适的模型，或避免了一次糟糕的硬件猜测， +欢迎赞助。这有助于维持项目：硬件报告、打包、测试夹具（test fixtures）、基准测试更新， +以及对更多设备的支持。 -whichllm will stay open-source either way. Issues and PRs are always welcome. +无论是否赞助，whichllm 都会保持开源。Issues 和 PR 始终欢迎。 -Useful? A GitHub star helps other people find it, and I'd genuinely like to -know what it picked for your rig. Drop it in [Issues](https://github.com/Andyyyy64/whichllm/issues). +觉得有用？GitHub star 能帮助其他人发现它，我也很想知道 +它为你的配置选了什么。欢迎在 [Issues](https://github.com/Andyyyy64/whichllm/issues). ## Star History @@ -505,8 +486,8 @@ know what it picked for your rig. Drop it in [Issues](https://github.com/Andyyyy ## Requirements - Python 3.11+ -- NVIDIA GPU detection via `nvidia-ml-py` (included by default) -- AMD / Apple Silicon detected automatically +- 通过 `nvidia-ml-py` 检测 NVIDIA GPU（默认已包含） +- AMD / Apple Silicon 可自动检测 ## License