[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71H3PDC9BR2CBESQQ1WXTFC":3,"skill-same:01M71H3PDC9BR2CBESQQ1WXTFC":101},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M71H3PDC9BR2CBESQQ1WXTFC","llama-cpp",null,"llama.cpp local GGUF inference + HF Hub model discovery.","0xzgbot\u002Fforge-nps-v01","0xzgbot","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F271900281?v=4","01M71GXWAFRXXJSJE3HBCVDKZY","01M71GXWBFQYRJK7M8RM6BDP0X",6,"MIT","https:\u002F\u002Fgithub.com\u002F0xzgbot\u002Fforge-nps-v01\u002Fblob\u002Fmaster\u002Fhermes_home\u002Fprofiles\u002Fcinesmith\u002Fskills\u002Fmlops\u002Finference\u002Fllama-cpp\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"Llama","llama cpp","cpp","GGUF","face","orchestra","URL","model discovery","Intel GPUs","AMD GPUs","Edge Deployment","CPU Inference","llama.cpp","CPU","silicon","apple","local","hugging face","model","NVIDIA","2026-11-10 10:12:30",false,"---\nname: llama-cpp\ndescription: llama.cpp local GGUF inference + HF Hub model discovery.\nversion: 2.1.2\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [llama-cpp-python>=0.2.0]\nmetadata:\n  hermes:\n    tags: [llama.cpp, GGUF, Quantization, Hugging Face Hub, CPU Inference, Apple Silicon, Edge Deployment, AMD GPUs, Intel GPUs, NVIDIA, URL-first]\n---\n\n# llama.cpp + GGUF\n\nUse this skill for local GGUF inference, quant selection, or Hugging Face repo discovery for llama.cpp.\n\n## When to use\n\n- Run local models on CPU, Apple Silicon, CUDA, ROCm, or Intel GPUs\n- Find the right GGUF for a specific Hugging Face repo\n- Build a `llama-server` or `llama-cli` command from the Hub\n- Search the Hub for models that already support llama.cpp\n- Enumerate available `.gguf` files and sizes for a repo\n- Decide between Q4\u002FQ5\u002FQ6\u002FIQ variants for the user's RAM or VRAM\n\n## Model Discovery workflow\n\nPrefer URL workflows before asking for `hf`, Python, or custom scripts.\n\n1. Search for candidate repos on the Hub:\n   - Base: `https:\u002F\u002Fhuggingface.co\u002Fmodels?apps=llama.cpp&sort=trending`\n   - Add `search=\u003Cterm>` for a model family\n   - Add `num_parameters=min:0,max:24B` or similar when the user has size constraints\n2. Open the repo with the llama.cpp local-app view:\n   - `https:\u002F\u002Fhuggingface.co\u002F\u003Crepo>?local-app=llama.cpp`\n3. Treat the local-app snippet as the source of truth when it is visible:\n   - copy the exact `llama-server` or `llama-cli` command\n   - report the recommended quant exactly as HF shows it\n4. Read the same `?local-app=llama.cpp` URL as page text or HTML and extract the section under `Hardware compatibility`:\n   - prefer its exact quant labels and sizes over generic tables\n   - keep repo-specific labels such as `UD-Q4_K_M` or `IQ4_NL_XL`\n   - if that section is not visible in the fetched page source, say so and fall back to the tree API plus generic quant guidance\n5. Query the tree API to confirm what actually exists:\n   - `https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002F\u003Crepo>\u002Ftree\u002Fmain?recursive=true`\n   - keep entries where `type` is `file` and `path` ends with `.gguf`\n   - use `path` and `size` as the source of truth for filenames and byte sizes\n   - separate quantized checkpoints from `mmproj-*.gguf` projector files and `BF16\u002F` shard files\n   - use `https:\u002F\u002Fhuggingface.co\u002F\u003Crepo>\u002Ftree\u002Fmain` only as a human fallback\n6. If the local-app snippet is not text-visible, reconstruct the command from the repo plus the chosen quant:\n   - shorthand quant selection: `llama-server -hf \u003Crepo>:\u003CQUANT>`\n   - exact-file fallback: `llama-server --hf-repo \u003Crepo> --hf-file \u003Cfilename.gguf>`\n7. Only suggest conversion from Transformers weights if the repo does not already expose GGUF files.\n\n## Quick start\n\n### Install llama.cpp\n\n```bash\n# macOS \u002F Linux (simplest)\nbrew install llama.cpp\n```\n\n```bash\nwinget install llama.cpp\n```\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\ncd llama.cpp\ncmake -B build\ncmake --build build --config Release\n```\n\n### Run directly from the Hugging Face Hub\n\n```bash\nllama-cli -hf bartowski\u002FLlama-3.2-3B-Instruct-GGUF:Q8_0\n```\n\n```bash\nllama-server -hf bartowski\u002FLlama-3.2-3B-Instruct-GGUF:Q8_0\n```\n\n### Run an exact GGUF file from the Hub\n\nUse this when the tree API shows custom file naming or the exact HF snippet is missing.\n\n```bash\nllama-server \\\n    --hf-repo microsoft\u002FPhi-3-mini-4k-instruct-gguf \\\n    --hf-file Phi-3-mini-4k-instruct-q4.gguf \\\n    -c 4096\n```\n\n### OpenAI-compatible server check\n\n```bash\ncurl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"messages\": [\n      {\"role\": \"user\", \"content\": \"Write a limerick about Python exceptions\"}\n    ]\n  }'\n```\n\n## Python bindings (llama-cpp-python)\n\n`pip install llama-cpp-python` (CUDA: `CMAKE_ARGS=\"-DGGML_CUDA=on\" pip install llama-cpp-python --force-reinstall --no-cache-dir`; Metal: `CMAKE_ARGS=\"-DGGML_METAL=on\" ...`).\n\n### Basic generation\n\n```python\nfrom llama_cpp import Llama\n\nllm = Llama(\n    model_path=\".\u002Fmodel-q4_k_m.gguf\",\n    n_ctx=4096,\n    n_gpu_layers=35,     # 0 for CPU, 99 to offload everything\n    n_threads=8,\n)\n\nout = llm(\"What is machine learning?\", max_tokens=256, temperature=0.7)\nprint(out[\"choices\"][0][\"text\"])\n```\n\n### Chat + streaming\n\n```python\nllm = Llama(\n    model_path=\".\u002Fmodel-q4_k_m.gguf\",\n    n_ctx=4096,\n    n_gpu_layers=35,\n    chat_format=\"llama-3\",   # or \"chatml\", \"mistral\", etc.\n)\n\nresp = llm.create_chat_completion(\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n        {\"role\": \"user\", \"content\": \"What is Python?\"},\n    ],\n    max_tokens=256,\n)\nprint(resp[\"choices\"][0][\"message\"][\"content\"])\n\n# Streaming\nfor chunk in llm(\"Explain quantum computing:\", max_tokens=256, stream=True):\n    print(chunk[\"choices\"][0][\"text\"], end=\"\", flush=True)\n```\n\n### Embeddings\n\n```python\nllm = Llama(model_path=\".\u002Fmodel-q4_k_m.gguf\", embedding=True, n_gpu_layers=35)\nvec = llm.embed(\"This is a test sentence.\")\nprint(f\"Embedding dimension: {len(vec)}\")\n```\n\nYou can also load a GGUF straight from the Hub:\n\n```python\nllm = Llama.from_pretrained(\n    repo_id=\"bartowski\u002FLlama-3.2-3B-Instruct-GGUF\",\n    filename=\"*Q4_K_M.gguf\",\n    n_gpu_layers=35,\n)\n```\n\n## Choosing a quant\n\nUse the Hub page first, generic heuristics second.\n\n- Prefer the exact quant that HF marks as compatible for the user's hardware profile.\n- For general chat, start with `Q4_K_M`.\n- For code or technical work, prefer `Q5_K_M` or `Q6_K` if memory allows.\n- For very tight RAM budgets, consider `Q3_K_M`, `IQ` variants, or `Q2` variants only if the user explicitly prioritizes fit over quality.\n- For multimodal repos, mention `mmproj-*.gguf` separately. The projector is not the main model file.\n- Do not normalize repo-native labels. If the page says `UD-Q4_K_M`, report `UD-Q4_K_M`.\n\n## Extracting available GGUFs from a repo\n\nWhen the user asks what GGUFs exist, return:\n\n- filename\n- file size\n- quant label\n- whether it is a main model or an auxiliary projector\n\nIgnore unless requested:\n\n- README\n- BF16 shard files\n- imatrix blobs or calibration artifacts\n\nUse the tree API for this step:\n\n- `https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002F\u003Crepo>\u002Ftree\u002Fmain?recursive=true`\n\nFor a repo like `unsloth\u002FQwen3.6-35B-A3B-GGUF`, the local-app page can show quant chips such as `UD-Q4_K_M`, `UD-Q5_K_M`, `UD-Q6_K`, and `Q8_0`, while the tree API exposes exact file paths such as `Qwen3.6-35B-A3B-UD-Q4_K_M.gguf` and `Qwen3.6-35B-A3B-Q8_0.gguf` with byte sizes. Use the tree API to turn a quant label into an exact filename.\n\n## Search patterns\n\nUse these URL shapes directly:\n\n```text\nhttps:\u002F\u002Fhuggingface.co\u002Fmodels?apps=llama.cpp&sort=trending\nhttps:\u002F\u002Fhuggingface.co\u002Fmodels?search=\u003Cterm>&apps=llama.cpp&sort=trending\nhttps:\u002F\u002Fhuggingface.co\u002Fmodels?search=\u003Cterm>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending\nhttps:\u002F\u002Fhuggingface.co\u002F\u003Crepo>?local-app=llama.cpp\nhttps:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002F\u003Crepo>\u002Ftree\u002Fmain?recursive=true\nhttps:\u002F\u002Fhuggingface.co\u002F\u003Crepo>\u002Ftree\u002Fmain\n```\n\n## Output format\n\nWhen answering discovery requests, prefer a compact structured result like:\n\n```text\nRepo: \u003Crepo>\nRecommended quant from HF: \u003Clabel> (\u003Csize>)\nllama-server: \u003Ccommand>\nOther GGUFs:\n- \u003Cfilename> - \u003Csize>\n- \u003Cfilename> - \u003Csize>\nSource URLs:\n- \u003Clocal-app URL>\n- \u003Ctree API URL>\n```\n\n## References\n\n- **[hub-discovery.md](references\u002Fhub-discovery.md)** - URL-only Hugging Face workflows, search patterns, GGUF extraction, and command reconstruction\n- **[advanced-usage.md](references\u002Fadvanced-usage.md)** — speculative decoding, batched inference, grammar-constrained generation, LoRA, multi-GPU, custom builds, benchmark scripts\n- **[quantization.md](references\u002Fquantization.md)** — quant quality tradeoffs, when to use Q4\u002FQ5\u002FQ6\u002FIQ, model size scaling, imatrix\n- **[server.md](references\u002Fserver.md)** — direct-from-Hub server launch, OpenAI API endpoints, Docker deployment, NGINX load balancing, monitoring\n- **[optimization.md](references\u002Foptimization.md)** — CPU threading, BLAS, GPU offload heuristics, batch tuning, benchmarks\n- **[troubleshooting.md](references\u002Ftroubleshooting.md)** — install\u002Fconvert\u002Fquantize\u002Finference\u002Fserver issues, Apple Silicon, debugging\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\n- **Hugging Face GGUF + llama.cpp docs**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fgguf-llamacpp\n- **Hugging Face Local Apps docs**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fmain\u002Flocal-apps\n- **Hugging Face Local Agents docs**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fagents-local\n- **Example local-app page**: https:\u002F\u002Fhuggingface.co\u002Funsloth\u002FQwen3.6-35B-A3B-GGUF?local-app=llama.cpp\n- **Example tree API**: https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002Funsloth\u002FQwen3.6-35B-A3B-GGUF\u002Ftree\u002Fmain?recursive=true\n- **Example llama.cpp search**: https:\u002F\u002Fhuggingface.co\u002Fmodels?num_parameters=min:0,max:24B&apps=llama.cpp&sort=trending\n- **License**: MIT\n","# llama.cpp + GGUF\n\nUse this skill for local GGUF inference, quant selection, or Hugging Face repo discovery for llama.cpp.\n\n## When to use\n\n- Run local models on CPU, Apple Silicon, CUDA, ROCm, or Intel GPUs\n- Find the right GGUF for a specific Hugging Face repo\n- Build a `llama-server` or `llama-cli` command from the Hub\n- Search the Hub for models that already support llama.cpp\n- Enumerate available `.gguf` files and sizes for a repo\n- Decide between Q4\u002FQ5\u002FQ6\u002FIQ variants for the user's RAM or VRAM\n\n## Model Discovery workflow\n\nPrefer URL workflows before asking for `hf`, Python, or custom scripts.\n\n1. Search for candidate repos on the Hub:\n   - Base: `https:\u002F\u002Fhuggingface.co\u002Fmodels?apps=llama.cpp&sort=trending`\n   - Add `search=\u003Cterm>` for a model family\n   - Add `num_parameters=min:0,max:24B` or similar when the user has size constraints\n2. Open the repo with the llama.cpp local-app view:\n   - `https:\u002F\u002Fhuggingface.co\u002F\u003Crepo>?local-app=llama.cpp`\n3. Treat the local-app snippet as the source of truth when it is visible:\n   - copy the exact `llama-server` or `llama-cli` command\n   - report the recommended quant exactly as HF shows it\n4. Read the same `?local-app=llama.cpp` URL as page text or HTML and extract the section under `Hardware compatibility`:\n   - prefer its exact quant labels and sizes over generic tables\n   - keep repo-specific labels such as `UD-Q4_K_M` or `IQ4_NL_XL`\n   - if that section is not visible in the fetched page source, say so and fall back to the tree API plus generic quant guidance\n5. Query the tree API to confirm what actually exists:\n   - `https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002F\u003Crepo>\u002Ftree\u002Fmain?recursive=true`\n   - keep entries where `type` is `file` and `path` ends with `.gguf`\n   - use `path` and `size` as the source of truth for filenames and byte sizes\n   - separate quantized checkpoints from `mmproj-*.gguf` projector files and `BF16\u002F` shard files\n   - use `https:\u002F\u002Fhuggingface.co\u002F\u003Crepo>\u002Ftree\u002Fmain` only as a human fallback\n6. If the local-app snippet is not text-visible, reconstruct the command from the repo plus the chosen quant:\n   - shorthand quant selection: `llama-server -hf \u003Crepo>:\u003CQUANT>`\n   - exact-file fallback: `llama-server --hf-repo \u003Crepo> --hf-file \u003Cfilename.gguf>`\n7. Only suggest conversion from Transformers weights if the repo does not already expose GGUF files.\n\n## Quick start\n\n### Install llama.cpp\n\n```bash\n# macOS \u002F Linux (simplest)\nbrew install llama.cpp\n```\n\n```bash\nwinget install llama.cpp\n```\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\ncd llama.cpp\ncmake -B build\ncmake --build build --config Release\n```\n\n### Run directly from the Hugging Face Hub\n\n```bash\nllama-cli -hf bartowski\u002FLlama-3.2-3B-Instruct-GGUF:Q8_0\n```\n\n```bash\nllama-server -hf bartowski\u002FLlama-3.2-3B-Instruct-GGUF:Q8_0\n```\n\n### Run an exact GGUF file from the Hub\n\nUse this when the tree API shows custom file naming or the exact HF snippet is missing.\n\n```bash\nllama-server \\\n    --hf-repo microsoft\u002FPhi-3-mini-4k-instruct-gguf \\\n    --hf-file Phi-3-mini-4k-instruct-q4.gguf \\\n    -c 4096\n```\n\n### OpenAI-compatible server check\n\n```bash\ncurl http:\u002F\u002Flocalhost:8080\u002Fv1\u002Fchat\u002Fcompletions \\\n  -H \"Content-Type: application\u002Fjson\" \\\n  -d '{\n    \"messages\": [\n      {\"role\": \"user\", \"content\": \"Write a limerick about Python exceptions\"}\n    ]\n  }'\n```\n\n## Python bindings (llama-cpp-python)\n\n`pip install llama-cpp-python` (CUDA: `CMAKE_ARGS=\"-DGGML_CUDA=on\" pip install llama-cpp-python --force-reinstall --no-cache-dir`; Metal: `CMAKE_ARGS=\"-DGGML_METAL=on\" ...`).\n\n### Basic generation\n\n```python\nfrom llama_cpp import Llama\n\nllm = Llama(\n    model_path=\".\u002Fmodel-q4_k_m.gguf\",\n    n_ctx=4096,\n    n_gpu_layers=35,     # 0 for CPU, 99 to offload everything\n    n_threads=8,\n)\n\nout = llm(\"What is machine learning?\", max_tokens=256, temperature=0.7)\nprint(out[\"choices\"][0][\"text\"])\n```\n\n### Chat + streaming\n\n```python\nllm = Llama(\n    model_path=\".\u002Fmodel-q4_k_m.gguf\",\n    n_ctx=4096,\n    n_gpu_layers=35,\n    chat_format=\"llama-3\",   # or \"chatml\", \"mistral\", etc.\n)\n\nresp = llm.create_chat_completion(\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n        {\"role\": \"user\", \"content\": \"What is Python?\"},\n    ],\n    max_tokens=256,\n)\nprint(resp[\"choices\"][0][\"message\"][\"content\"])\n\n# Streaming\nfor chunk in llm(\"Explain quantum computing:\", max_tokens=256, stream=True):\n    print(chunk[\"choices\"][0][\"text\"], end=\"\", flush=True)\n```\n\n### Embeddings\n\n```python\nllm = Llama(model_path=\".\u002Fmodel-q4_k_m.gguf\", embedding=True, n_gpu_layers=35)\nvec = llm.embed(\"This is a test sentence.\")\nprint(f\"Embedding dimension: {len(vec)}\")\n```\n\nYou can also load a GGUF straight from the Hub:\n\n```python\nllm = Llama.from_pretrained(\n    repo_id=\"bartowski\u002FLlama-3.2-3B-Instruct-GGUF\",\n    filename=\"*Q4_K_M.gguf\",\n    n_gpu_layers=35,\n)\n```\n\n## Choosing a quant\n\nUse the Hub page first, generic heuristics second.\n\n- Prefer the exact quant that HF marks as compatible for the user's hardware profile.\n- For general chat, start with `Q4_K_M`.\n- For code or technical work, prefer `Q5_K_M` or `Q6_K` if memory allows.\n- For very tight RAM budgets, consider `Q3_K_M`, `IQ` variants, or `Q2` variants only if the user explicitly prioritizes fit over quality.\n- For multimodal repos, mention `mmproj-*.gguf` separately. The projector is not the main model file.\n- Do not normalize repo-native labels. If the page says `UD-Q4_K_M`, report `UD-Q4_K_M`.\n\n## Extracting available GGUFs from a repo\n\nWhen the user asks what GGUFs exist, return:\n\n- filename\n- file size\n- quant label\n- whether it is a main model or an auxiliary projector\n\nIgnore unless requested:\n\n- README\n- BF16 shard files\n- imatrix blobs or calibration artifacts\n\nUse the tree API for this step:\n\n- `https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002F\u003Crepo>\u002Ftree\u002Fmain?recursive=true`\n\nFor a repo like `unsloth\u002FQwen3.6-35B-A3B-GGUF`, the local-app page can show quant chips such as `UD-Q4_K_M`, `UD-Q5_K_M`, `UD-Q6_K`, and `Q8_0`, while the tree API exposes exact file paths such as `Qwen3.6-35B-A3B-UD-Q4_K_M.gguf` and `Qwen3.6-35B-A3B-Q8_0.gguf` with byte sizes. Use the tree API to turn a quant label into an exact filename.\n\n## Search patterns\n\nUse these URL shapes directly:\n\n```text\nhttps:\u002F\u002Fhuggingface.co\u002Fmodels?apps=llama.cpp&sort=trending\nhttps:\u002F\u002Fhuggingface.co\u002Fmodels?search=\u003Cterm>&apps=llama.cpp&sort=trending\nhttps:\u002F\u002Fhuggingface.co\u002Fmodels?search=\u003Cterm>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending\nhttps:\u002F\u002Fhuggingface.co\u002F\u003Crepo>?local-app=llama.cpp\nhttps:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002F\u003Crepo>\u002Ftree\u002Fmain?recursive=true\nhttps:\u002F\u002Fhuggingface.co\u002F\u003Crepo>\u002Ftree\u002Fmain\n```\n\n## Output format\n\nWhen answering discovery requests, prefer a compact structured result like:\n\n```text\nRepo: \u003Crepo>\nRecommended quant from HF: \u003Clabel> (\u003Csize>)\nllama-server: \u003Ccommand>\nOther GGUFs:\n- \u003Cfilename> - \u003Csize>\n- \u003Cfilename> - \u003Csize>\nSource URLs:\n- \u003Clocal-app URL>\n- \u003Ctree API URL>\n```\n\n## References\n\n- **[hub-discovery.md](references\u002Fhub-discovery.md)** - URL-only Hugging Face workflows, search patterns, GGUF extraction, and command reconstruction\n- **[advanced-usage.md](references\u002Fadvanced-usage.md)** — speculative decoding, batched inference, grammar-constrained generation, LoRA, multi-GPU, custom builds, benchmark scripts\n- **[quantization.md](references\u002Fquantization.md)** — quant quality tradeoffs, when to use Q4\u002FQ5\u002FQ6\u002FIQ, model size scaling, imatrix\n- **[server.md](references\u002Fserver.md)** — direct-from-Hub server launch, OpenAI API endpoints, Docker deployment, NGINX load balancing, monitoring\n- **[optimization.md](references\u002Foptimization.md)** — CPU threading, BLAS, GPU offload heuristics, batch tuning, benchmarks\n- **[troubleshooting.md](references\u002Ftroubleshooting.md)** — install\u002Fconvert\u002Fquantize\u002Finference\u002Fserver issues, Apple Silicon, debugging\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\n- **Hugging Face GGUF + llama.cpp docs**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fgguf-llamacpp\n- **Hugging Face Local Apps docs**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fmain\u002Flocal-apps\n- **Hugging Face Local Agents docs**: https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fagents-local\n- **Example local-app page**: https:\u002F\u002Fhuggingface.co\u002Funsloth\u002FQwen3.6-35B-A3B-GGUF?local-app=llama.cpp\n- **Example tree API**: https:\u002F\u002Fhuggingface.co\u002Fapi\u002Fmodels\u002Funsloth\u002FQwen3.6-35B-A3B-GGUF\u002Ftree\u002Fmain?recursive=true\n- **Example llama.cpp search**: https:\u002F\u002Fhuggingface.co\u002Fmodels?num_parameters=min:0,max:24B&apps=llama.cpp&sort=trending\n- **License**: MIT\n",{"name":5,"author":43,"license":14,"version":44,"metadata":45,"description":7,"dependencies":52},"Orchestra 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DESCRIPTION.","https:\u002F\u002Fgithub.com\u002F0xzgbot\u002Fforge-nps-v01\u002Fblob\u002Fmaster\u002Fhermes_home\u002Fskills\u002Fworkflow_ltx_i2v\u002FSKILL.md",[121,126],{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":127,"categoryName":128,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000023","Skill与智能体开发",[130,131,132,133,134,135,136],"ltx","workflow ltx i2v","workflow","Production","task","when","use",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":145,"publishedAt":38},"01M71J5NYW853AAPB17VR0F7ZX","workflow-flux2-text-to-image","Flux2 Text-to-Image Workflow Reference.","https:\u002F\u002Fgithub.com\u002F0xzgbot\u002Fforge-nps-v01\u002Fblob\u002Fmaster\u002Fhermes_home\u002Fskills\u002Fworkflow_flux2_text_to_image\u002FSKILL.md",[143,144],{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},{"categoryNo":127,"categoryName":128,"taxonomyType":124,"taxonomyTypeDesc":125},[146,147,132,148,149,133,150],"workflow flux2 text to image","image","text","reference","Text-to-Image",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":158,"publishedAt":38},"01M71J5KWY96MD6B6YC2XP6NXB","wide-angle-environmental","Use when the task involves wide angle environmental. DESCRIPTION.","https:\u002F\u002Fgithub.com\u002F0xzgbot\u002Fforge-nps-v01\u002Fblob\u002Fmaster\u002Fhermes_home\u002Fskills\u002Fwide_angle_environmental\u002FSKILL.md",[157],{"categoryNo":127,"categoryName":128,"taxonomyType":124,"taxonomyTypeDesc":125},[159,160,161,133,134,135,136],"environmental","wide","wide angle environmental",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":169,"publishedAt":38},"01M71J5DXHECTDVDF8HRP81YFY","wes-anderson-specialist","Use when the task involves wes anderson specialist. 1. EXECUTIVE SUMMARY.","https:\u002F\u002Fgithub.com\u002F0xzgbot\u002Fforge-nps-v01\u002Fblob\u002Fmaster\u002Fhermes_home\u002Fskills\u002Fwes_anderson_specialist\u002FSKILL.md",[168],{"categoryNo":127,"categoryName":128,"taxonomyType":124,"taxonomyTypeDesc":125},[170,171,172,173,135,136,134,133],"specialist","wes anderson specialist","summary","executive summary",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":181,"publishedAt":38},"01M71J5BRHX2N4BRG2VQMWJK3E","volumetric-god-rays-lighting","Use when the task involves volumetric god rays lighting. 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