Instructions to use VladHong/llama-cpp-K2-FAST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use VladHong/llama-cpp-K2-FAST2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: ./build/bin/llama cli -hf VladHong/llama-cpp-K2-FAST2
Use Docker
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Llama new
- LM Studio
- Jan
- Ollama
How to use VladHong/llama-cpp-K2-FAST2 with Ollama:
ollama run hf.co/VladHong/llama-cpp-K2-FAST2
- Unsloth Desktop
- Docker Model Runner
How to use VladHong/llama-cpp-K2-FAST2 with Docker Model Runner:
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Lemonade
How to use VladHong/llama-cpp-K2-FAST2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VladHong/llama-cpp-K2-FAST2
Run and chat with the model
lemonade run user.llama-cpp-K2-FAST2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
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Download tools/tuning/README.md from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 3.3 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/tuning/README.md
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/tuning/README.md
-
curl -L -o README.md https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/tuning/README.md
3.3 kB
| # ggml-metal-tuning | |
| Offline kernel tuner for the Metal backend. | |
| It sweeps a kernel's config grid on the machine it runs on and prints pasteable table rows for `ggml/src/ggml-metal/ggml-metal-tuning.cpp`. | |
| This is not a test: it never reports pass/fail on performance. | |
| A non-zero exit code means bad arguments or a wrong environment (no Metal device, missing proc bridges), never a perf result. | |
| | tuner | tunes | table | | |
| |---|---|---| | |
| | `fa-vec` | flash-attn vec `(Q, NE)` per `(dtype, head size, KV depth, batch width)` | `fa_vec_tuned_table` | | |
| ## Adding a device to the FA-vec table | |
| Build on the target machine: | |
| ```bash | |
| cmake -B build -DGGML_METAL=ON | |
| cmake --build build --target ggml-metal-tuning -j | |
| cmake --build build --target test-backend-ops -j | |
| ``` | |
| Sweep the grid (6 dtypes x 10 head sizes x 4 KV depths x 9 batch widths; a few hours): | |
| ```bash | |
| ./build/bin/ggml-metal-tuning fa-vec > fa_vec_rows.txt 2> fa_vec_sweep.log | |
| ``` | |
| `fa_vec_rows.txt` holds nothing but table rows, ready to paste into `fa_vec_tuned_table`: the min-max-regret target, the aggregate benefit gate, the short-KV drop and the pointwise compression are already applied. | |
| A config represents a bucket only if it is no slower than the baseline config at every point that bucket covers, so a config that wins on average but loses at one batch width leaves its bucket at baseline. | |
| `fa_vec_sweep.log` holds the per-cell timings, bucket coverage, noise floor, any cooldown activity, and every config the no-harm rule refused together with the point that refused it. | |
| Post both: the log is what makes the rows reviewable. | |
| Long sweeps can be split. | |
| `--dtype f16,q4_0` and `--dk 128,192` restrict the grid, and the emitted rows for one `(dtype, head size)` do not depend on the others. | |
| Concatenating the shard outputs in the order the full grid would visit them gives the same rows a single run prints. | |
| Then validate the numerics, where Metal is compared against the CPU reference: | |
| ```bash | |
| ./build/bin/test-backend-ops test -o FLASH_ATTN_EXT -b MTL0 | |
| ``` | |
| This forces every legal `(Q, NE)` on `dk=128` and `dk=576`. | |
| The tuner itself does no numerical checks, so the other head sizes have no automated numerical coverage. | |
| If the device is not in `enum ggml_metal_device_id` yet, register it in `ggml/src/ggml-metal/ggml-metal-device.{h,m}` first. | |
| The tuner emits whatever token the runtime reports for the machine, so an unregistered device emits `GGML_METAL_DEVICE_GENERIC` and its rows would apply to every unknown device. | |
| ## Thermal throttling | |
| Long sweeps heat the GPU, and a throttled measurement is indistinguishable from a slow kernel. | |
| The tuner re-measures a fixed baseline config every four candidates as an anchor. | |
| When the anchor drifts more than `--cool-drift` (10% by default) from the coolest anchor seen in that cell, the tuner: | |
| 1. discards every candidate measured since the last clean anchor, | |
| 2. sleeps with exponential backoff until the anchor comes back within `--cool-eps` (3%), | |
| 3. re-measures the discarded candidates. | |
| If it cannot cool down within `--cool-max-wait` seconds, or a cell needs more than `--cool-max-retry` rounds, that cell is dropped from the table and reported on stderr. | |
| `--no-cooldown` only warns on drift and keeps the measurement. | |
| Use it to reproduce a sweep taken without cooling. | |