模型：由 satindergrewal 添加 Hy3 (hy_v3) 支持及 MTP 推测解码 · 拉取请求 #25395 · ggml-org/llama.cpp

Reddit r/LocalLLaMA 工具
pull-request speculative-decoding hy3 llama.cpp model-support open-source

摘要

此拉取请求为 llama.cpp 添加了对 Hy3 (hy_v3) 模型的支持及 MTP 推测解码，从而实现了该架构的高效推理。

暂无内容
查看原文
查看缓存全文

缓存时间: 2026/07/14 04:24

ggml-org/llama.cpp 来源：https://github.com/ggml-org/llama.cpp

llama.cpp

llama 许可证：MIT (https://opensource.org/licenses/MIT) 发布 (https://github.com/ggml-org/llama.cpp/releases) 服务器 (https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml) Docker (https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml) Winget (https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml) 宣言 (https://github.com/ggml-org/llama.cpp/discussions/205) / ggml (https://github.com/ggml-org/ggml) / 操作 (https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / 维护者 PR (https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Abartowski1182%20OR%20author%3Ahipudding%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3A0cc4m%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc)

用C/C++进行LLM推理

近期API变更

  • libllama API 更新日志 (https://github.com/ggml-org/llama.cpp/issues/9289)
  • llama-server REST API 更新日志 (https://github.com/ggml-org/llama.cpp/issues/9291)

热门话题

  • Hugging Face 缓存迁移：使用 -hf 下载的模型现存储于标准 Hugging Face 缓存目录，可与其他 HF 工具共享。
  • 指南：使用 llama.cpp 的新 WebUI (https://github.com/ggml-org/llama.cpp/discussions/16938)
  • 指南：使用 llama.cpp 运行 gpt-oss (https://github.com/ggml-org/llama.cpp/discussions/15396)
  • [反馈] 优化 llama.cpp 打包以支持下游消费者 🤗
  • 已添加对原生 MXFP4 格式的 gpt-oss 模型支持 | PR (https://github.com/ggml-org/llama.cpp/pull/15091) | 与 NVIDIA 合作 (https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | 评论 (https://github.com/ggml-org/llama.cpp/discussions/15095)
  • llama-server 现已支持多模态：#12898 (https://github.com/ggml-org/llama.cpp/pull/12898) | 文档
  • 用于 FIM 补全的 VS Code 扩展：https://github.com/ggml-org/llama.vscode
  • 用于 FIM 补全的 Vim/Neovim 插件：https://github.com/ggml-org/llama.vim
  • Hugging Face 推理端点现已原生支持 GGUF！https://github.com/ggml-org/llama.cpp/discussions/9669
  • Hugging Face GGUF 编辑器：讨论 (https://github.com/ggml-org/llama.cpp/discussions/9268) | 工具 (https://huggingface.co/spaces/CISCai/gguf-editor)
  • 浏览器中现已支持 WebGPU，在此查看介绍博客/演示 (https://reeselevine.github.io/llamas-on-the-web/)。

快速开始

开始使用 llama.cpp 非常简单。以下是一些在您的机器上安装它的方法：

安装后，您需要一个模型来使用。前往 获取和量化模型 部分了解更多信息。

示例命令：

# 使用本地模型文件
llama-cli -m my_model.gguf

# 或者直接从 Hugging Face 下载并运行模型
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF

# 启动兼容 OpenAI 的 API 服务器
llama-server -hf ggml-org/gemma-3-1b-it-GGUF

描述

llama.cpp 的主要目标是实现 LLM 推理，并具有最少的设置和最先进的性能，适用于各种硬件——本地和云端。

  • 纯 C/C++ 实现，无任何依赖
  • 苹果芯片作为一等公民——通过 ARM NEON、Accelerate 和 Metal 框架优化
  • 支持 x86 架构的 AVX、AVX2、AVX512 和 AMX
  • 支持 RISC-V 架构的 RVV、ZVFH、ZFH、ZICBOP 和 ZIHINTPAUSE
  • 1.5 位、2 位、3 位、4 位、5 位、6 位和 8 位整数量化，以加快推理速度并减少内存使用
  • 自定义 CUDA 内核，用于在 NVIDIA GPU 上运行 LLM（通过 HIP 支持 AMD GPU，通过 MUSA 支持摩尔线程 GPU）
  • 支持 Vulkan 和 SYCL 后端
  • CPU+GPU 混合推理，可部分加速大于总 VRAM 容量的模型

llama.cpp 项目是开发 ggml (https://github.com/ggml-org/ggml) 库新功能的主要试验场。

支持的模型

通常也支持以下基础模型的微调版本。添加新模型支持的说明：HOWTO-add-model.md

纯文本模型

  • LLaMA 🦙
  • LLaMA 2 🦙🦙
  • LLaMA 3 🦙🦙🦙
  • Mistral 7B (https://huggingface.co/mistralai/Mistral-7B-v0.1)
  • Mixtral MoE (https://huggingface.co/models?search=mistral-ai/Mixtral)
  • DBRX (https://huggingface.co/databricks/dbrx-instruct)
  • Jamba (https://huggingface.co/ai21labs)
  • Falcon (https://huggingface.co/models?search=tiiuae/falcon)
  • 中文 LLaMA / Alpaca (https://github.com/ymcui/Chinese-LLaMA-Alpaca) 和中文 LLaMA-2 / Alpaca-2 (https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
  • Vigogne（法语）(https://github.com/bofenghuang/vigogne)
  • BERT (https://github.com/ggml-org/llama.cpp/pull/5423)
  • Koala (https://bair.berkeley.edu/blog/2023/04/03/koala/)
  • Baichuan 1 & 2 (https://huggingface.co/models?search=baichuan-inc/Baichuan) + 衍生模型 (https://huggingface.co/hiyouga/baichuan-7b-sft)
  • Aquila 1 & 2 (https://huggingface.co/models?search=BAAI/Aquila)
  • Starcoder 模型 (https://github.com/ggml-org/llama.cpp/pull/3187)
  • Refact (https://huggingface.co/smallcloudai/Refact-1_6B-fim)
  • MPT (https://github.com/ggml-org/llama.cpp/pull/3417)
  • Bloom (https://github.com/ggml-org/llama.cpp/pull/3553)
  • Yi 模型 (https://huggingface.co/models?search=01-ai/Yi)
  • StableLM 模型 (https://huggingface.co/stabilityai)
  • Deepseek 模型 (https://huggingface.co/models?search=deepseek-ai/deepseek)
  • Qwen 模型 (https://huggingface.co/models?search=Qwen/Qwen)
  • PLaMo-13B (https://github.com/ggml-org/llama.cpp/pull/3557)
  • Phi 模型 (https://huggingface.co/models?search=microsoft/phi)
  • PhiMoE (https://github.com/ggml-org/llama.cpp/pull/11003)
  • GPT-2 (https://huggingface.co/gpt2)
  • Orion 14B (https://github.com/ggml-org/llama.cpp/pull/5118)
  • InternLM2 (https://huggingface.co/models?search=internlm2)
  • CodeShell (https://github.com/WisdomShell/codeshell)
  • Gemma (https://ai.google.dev/gemma)
  • Mamba (https://github.com/state-spaces/mamba)
  • Grok-1 (https://huggingface.co/keyfan/grok-1-hf)
  • Xverse (https://huggingface.co/models?search=xverse)
  • Command-R 模型 (https://huggingface.co/models?search=CohereForAI/c4ai-command-r)
  • SEA-LION (https://huggingface.co/models?search=sea-lion)
  • GritLM-7B (https://huggingface.co/GritLM/GritLM-7B) + GritLM-8x7B (https://huggingface.co/GritLM/GritLM-8x7B)
  • OLMo (https://allenai.org/olmo)
  • OLMo 2 (https://allenai.org/olmo)
  • OLMoE (https://huggingface.co/allenai/OLMoE-1B-7B-0924)
  • Granite 模型 (https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330)
  • GPT-NeoX (https://github.com/EleutherAI/gpt-neox) + Pythia (https://github.com/EleutherAI/pythia)
  • Snowflake-Arctic MoE (https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520)
  • Smaug (https://huggingface.co/models?search=Smaug)
  • Poro 34B (https://huggingface.co/LumiOpen/Poro-34B)
  • Bitnet b1.58 模型 (https://huggingface.co/1bitLLM)
  • Flan T5 (https://huggingface.co/models?search=flan-t5)
  • Open Elm 模型 (https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
  • ChatGLM3-6b (https://huggingface.co/THUDM/chatglm3-6b) + ChatGLM4-9b (https://huggingface.co/THUDM/glm-4-9b) + GLMEdge-1.5b (https://huggingface.co/THUDM/glm-edge-1.5b-chat) + GLMEdge-4b (https://huggingface.co/THUDM/glm-edge-4b-chat)
  • GLM-4-0414 (https://huggingface.co/collections/THUDM/glm-4-0414-67f3cbcb34dd9d252707cb2e)
  • SmolLM (https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
  • EXAONE-3.0-7.8B-Instruct (https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
  • FalconMamba 模型 (https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
  • Jais (https://huggingface.co/inceptionai/jais-13b-chat)
  • Bielik-11B-v2.3 (https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
  • RWKV-7 (https://huggingface.co/collections/shoumenchougou/rwkv7-gxx-gguf)
  • RWKV-6 (https://github.com/BlinkDL/RWKV-LM)
  • QRWKV-6 (https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1)
  • GigaChat-20B-A3B (https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
  • Trillion-7B-preview (https://huggingface.co/trillionlabs/Trillion-7B-preview)
  • Ling 模型 (https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
  • Liquid LFM2 模型 (https://huggingface.co/collections/LiquidAI/lfm2)
  • Liquid LFM2.5 模型 (https://huggingface.co/collections/LiquidAI/lfm25)
  • Liquid Nanos (https://huggingface.co/collections/LiquidAI/liquid-nanos)
  • Hunyuan 模型 (https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
  • BailingMoeV2（Ling 2.0）模型 (https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
  • Mellum 模型 (https://huggingface.co/JetBrains/models?search=mellum)

多模态模型

  • LLaVA 1.5 模型 (https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e)、LLaVA 1.6 模型 (https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2)
  • BakLLaVA (https://huggingface.co/models?search=SkunkworksAI/Bakllava)
  • Obsidian (https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
  • ShareGPT4V (https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
  • MobileVLM 1.7B/3B 模型 (https://huggingface.co/models?search=mobileVLM)
  • Yi-VL (https://huggingface.co/models?search=Yi-VL)
  • Mini CPM (https://huggingface.co/models?search=MiniCPM)
  • Moondream (https://huggingface.co/vikhyatk/moondream2)
  • Bunny (https://github.com/BAAI-DCAI/Bunny)
  • GLM-EDGE (https://huggingface.co/models?search=glm-edge)
  • Qwen2-VL (https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
  • LFM2-VL (https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa)

绑定

  • Python: ddh0/easy-llama (https://github.com/ddh0/easy-llama)
  • Python: abetlen/llama-cpp-python (https://github.com/abetlen/llama-cpp-python)
  • Go: go-skynet/go-llama.cpp (https://github.com/go-skynet/go-llama.cpp)
  • Node.js: withcatai/node-llama-cpp (https://github.com/withcatai/node-llama-cpp)
  • JS/TS（llama.cpp 服务器客户端）：lgrammel/modelfusion (https://modelfusion.dev/integration/model-provider/llamacpp)
  • JS/TS（可编程提示引擎 CLI）：offline-ai/cli (https://github.com/offline-ai/cli)
  • JavaScript/Wasm（可在浏览器中运行）：tangledgroup/llama-cpp-wasm (https://github.com/tangledgroup/llama-cpp-wasm)
  • Typescript/Wasm（更友好的 API，可在 npm 获取）：ngxson/wllama (https://github.com/ngxson/wllama)
  • Ruby: yoshoku/llama_cpp.rb (https://github.com/yoshoku/llama_cpp.rb)
  • Ruby: docusealco/rllama (https://github.com/docusealco/rllama)
  • Rust（更多功能）：edgenai/llama_cpp-rs (https://github.com/edgenai/llama_cpp-rs)
  • Rust（更友好的 API）：mdrokz/rust-llama.cpp (https://github.com/mdrokz/rust-llama.cpp)
  • Rust（更直接的绑定）：utilityai/llama-cpp-rs (https://github.com/utilityai/llama-cpp-rs)
  • Rust（从 crates.io 自动构建）：ShelbyJenkins/llm_client (https://github.com/ShelbyJenkins/llm_client)
  • C#/.NET: SciSharp/LLamaSharp (https://github.com/SciSharp/LLamaSharp)
  • C#/VB.NET（更多功能 - 社区许可证）：LM-Kit.NET (https://docs.lm-kit.com/lm-kit-net/index.html)
  • Scala 3: donderom/llm4s (https://github.com/donderom/llm4s)
  • Clojure: phronmophobic/llama.clj (https://github.com/phronmophobic/llama.clj)
  • React Native: mybigday/llama.rn (https://github.com/mybigday/llama.rn)
  • Java: kherud/java-llama.cpp (https://github.com/kherud/java-llama.cpp)
  • Java: QuasarByte/llama-cpp-jna (https://github.com/QuasarByte/llama-cpp-jna)
  • Zig: deins/llama.cpp.zig (https://github.com/Deins/llama.cpp.zig)
  • Flutter/Dart: netdur/llama_cpp_dart (https://github.com/netdur/llama_cpp_dart)
  • Flutter: xuegao-tzx/Fllama (https://github.com/xuegao-tzx/Fllama)
  • PHP（基于 llama.cpp 的 API 绑定和功能）：distantmagic/resonance (https://github.com/distantmagic/resonance)（更多信息）(https://github.com/ggml-org/llama.cpp/pull/6326)
  • Guile Scheme: guile_llama_cpp (https://savannah.nongnu.org/projects/guile-llama-cpp)
  • Swift: srgtuszy/llama-cpp-swift (https://github.com/srgtuszy/llama-cpp-swift)
  • Swift: ShenghaiWang/SwiftLlama (https://github.com/ShenghaiWang/SwiftLlama)
  • Delphi: Embarcadero/llama-cpp-delphi (https://github.com/Embarcadero/llama-cpp-delphi)
  • Go（无需 CGo）：hybridgroup/yzma (https://github.com/hybridgroup/yzma)
  • Android: llama.android

用户界面

（要使项目在此列出，应明确声明其依赖于 llama.cpp）

  • AI Sublime Text 插件 (https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
  • BonzAI App (https://apps.apple.com/us/app/bonzai-your-local-ai-agent/id6752847988)（专有）
  • cztomsik/ava (https://github.com/cztomsik/ava) (MIT)
  • Dot (https://github.com/alexpinel/Dot) (GPL)
  • eva (https://github.com/ylsdamxssjxxdd/eva) (MIT)
  • iohub/collama (https://github.com/iohub/coLLaMA) (Apache-2.0)
  • janhq/jan (https://github.com/janhq/jan) (AGPL)
  • johnbean393/Sidekick (https://github.com/johnbean393/Sidekick) (MIT)
  • KanTV (https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0)
  • KodiBot (https://github.com/firatkiral/kodibot) (GPL)
  • llama.vim (https://github.com/ggml-org/llama.vim) (MIT)
  • LARS (https://github.com/abgulati/LARS) (AGPL)
  • Llama Assistant (https://github.com/vietanhdev/llama-assistant) (GPL)
  • LlamaLib (https://github.com/undreamai/LlamaLib) (Apache-2.0)
  • LLMFarm (https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
  • LLMUnity (https://github.com/undreamai/LLMUnity) (MIT)
  • LMStudio (https://lmstudio.ai/)（专有）
  • LocalAI (https://github.com/mudler/LocalAI) (MIT)
  • LostRuins/koboldcpp (https://github.com/LostRuins/koboldcpp) (AGPL)
  • MindMac (https://mindmac.app)（专有）
  • MindWorkAI/AI-Studio (https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
  • Mobile-Artificial-Intelligence/maid (https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
  • Mozilla-Ocho/llamafile (https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0)
  • nat/openplayground (https://github.com/nat/openplayground) (MIT)
  • nomic-ai/gpt4all (https://github.com/nomic-ai/gpt4all) (MIT)
  • ollama/ollama (https://github.com/ollama/ollama) (MIT)
  • oobabooga/text-generation-webui (https://github.com/oobabooga/text-generation-webui) (AGPL)
  • PocketPal AI (https://github.com/a-ghorbani/pocketpal-ai) (MIT)
  • psugihara/FreeChat (https://github.com/psugihara

相似文章

llama.cpp: Hy3 PR 与 GGUFs

Reddit r/LocalLLaMA

Hy3 模型现已通过拉取请求在 llama.cpp 中得到支持，并提供 GGUF 量化版本。早期测试显示，在高端硬件上，Q2_K 模型能以 10-11 t/s 的速度生成连贯输出。

AngelSlim/Hy3-GGUF

Hugging Face Models Trending

AngelSlim/Hy3-GGUF 是一个用于在 llama.cpp 上量化和部署 Hy3 大语言模型的工具包，具有 MTP 自我推测解码和思考/工具调用解析器，以实现高效推理。