/huggingface-local-models

name: huggingface-local-models
description: "Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving."
license: Apache-2.0 (modified; see UPSTREAMS.json)

Hugging Face Local Models

Search the Hugging Face Hub for llama.cpp-compatible GGUF repos, choose the right quant, and launch the model with `llama-cli` or `llama-server`.

Default Workflow

1. Search the Hub with `apps=llama.cpp`. 2. Open `https://huggingface.co/<repo>?local-app=llama.cpp`. 3. Prefer the exact HF local-app snippet and quant recommendation when it is visible. 4. Confirm exact `.gguf` filenames with `https://huggingface.co/api/models/<repo>/tree/main?recursive=true`. 5. Launch with `llama-cli -hf <repo>:<QUANT>` or `llama-server -hf <repo>:<QUANT>`. 6. Fall back to `--hf-repo` plus `--hf-file` when the repo uses custom file naming. 7. Convert from Transformers weights only if the repo does not already expose GGUF files.

Quick Start

Install llama.cpp

brew install llama.cpp
winget install llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
make

Authenticate for gated repos

hf auth login

Search the Hub

https://huggingface.co/models?apps=llama.cpp&sort=trending
https://huggingface.co/models?search=Qwen3.6&apps=llama.cpp&sort=trending
https://huggingface.co/models?search=<term>&apps=llama.cpp&num_parameters=min:0,max:24B&sort=trending

Run directly from the Hub

llama-cli -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M
llama-server -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_M

Run an exact GGUF file

llama-server \
    --hf-repo unsloth/Qwen3.6-35B-A3B-GGUF \
    --hf-file Qwen3.6-35B-A3B-UD-Q4_K_M.gguf \
    -c 4096

Convert only when no GGUF is available

hf download <repo-without-gguf> --local-dir ./model-src
python convert_hf_to_gguf.py ./model-src \
    --outfile model-f16.gguf \
    --outtype f16
llama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M

Smoke test a local server

With `llama-server` running (as started above), verify it responds:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer no-key" \
  -d '{
    "messages": [
      {"role": "user", "content": "Write a limerick about exception handling"}
    ]
  }'

Quant Choice

• Prefer the exact quant that HF marks as compatible on the `?local-app=llama.cpp` page.
• Keep repo-native labels such as `UD-Q4_K_M` instead of normalizing them.
• Default to `Q4_K_M` unless the repo page or hardware profile suggests otherwise.
• Prefer `Q5_K_M` or `Q6_K` for code or technical workloads when memory allows.
• Consider `Q3_K_M`, `Q4_K_S`, or repo-specific `IQ` / `UD-*` variants for tighter RAM or VRAM budgets.
• Treat `mmproj-*.gguf` files as projector weights, not the main checkpoint.

Load References

• Read [hub-discovery.md](references/hub-discovery.md) for URL-first workflows, model search, tree API extraction, and command reconstruction.
• Read [quantization.md](references/quantization.md) for format tables, model scaling, quality tradeoffs, and `imatrix`.
• Read [hardware.md](references/hardware.md) for Metal, CUDA, ROCm, or CPU build and acceleration details.

Resources

• llama.cpp: `https://github.com/ggml-org/llama.cpp`
• Hugging Face GGUF + llama.cpp docs: `https://huggingface.co/docs/hub/gguf-llamacpp`
• Hugging Face Local Apps docs: `https://huggingface.co/docs/hub/main/local-apps`
• Hugging Face Local Agents docs: `https://huggingface.co/docs/hub/agents-local`
• GGUF converter Space: `https://huggingface.co/spaces/ggml-org/gguf-my-repo`