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
|
Download tools/ui/tests/client/README-perf.md from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 2.78 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/tests/client/README-perf.md
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/tests/client/README-perf.md
-
curl -L -o README-perf.md https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/tests/client/README-perf.md
2.78 kB
| # Agentic thread perf harness | |
| Two tiers, both reusing the existing vitest projects (see `vite.config.ts`). | |
| ## Tier 1 - `agentic-stream.perf.svelte.test.ts` (project: `client`, real Chromium) | |
| Mounts `ChatMessageAgenticContent` and replays a stream, replacing the message | |
| object on each chunk exactly as the real pipeline does: | |
| - `chat.svelte.ts` `updateStreamingUI()` runs per SSE chunk | |
| - `conversations.svelte.ts` `updateMessageAtIndex` does `{ ...old, ...updates }` | |
| That new object identity is the thing under test: it cascades through | |
| `deriveAgenticSections` (which returns fresh `AgenticSection` objects) into every | |
| tool-call block in the message, including completed ones. | |
| ``` | |
| npx vitest --project=client --run tests/client/agentic-stream.perf.svelte.test.ts | |
| ``` | |
| ### Reading the output | |
| - `mean` / `p95` / `max` - the synchronous window per token: prop write, | |
| `await tick()`, then a forced `offsetHeight` read so style and layout are | |
| included rather than deferred. | |
| - `sync` - sum of those windows. This is the number to optimize. | |
| - `wall` - the whole run including work `MarkdownContent` defers into its own | |
| `requestAnimationFrame`. It carries a ~16.7ms/token idle floor because the | |
| harness yields a frame each iteration, so compare `wall` **across fixtures**, | |
| never against `sync`. | |
| ### The knobs, and what each one discriminates | |
| The point of the harness is the _scaling curve_, not any single number. | |
| | Knob | Reads on | | |
| | --------------------------- | --------------------------------------------------------------------------------------------------- | | |
| | `priorToolCalls` (0/1/5/20) | the reactive fan-out. Flat => no fan-out. Linear => confirmed. | | |
| | `toolResultBytes` | whole-blob string scans (`extractSearchResults`, `parseToolResultWithMedia`, `classifyToolResult`). | | |
| | `editFileEdits` | `computeLineDiff`, the O(m\*n) LCS. | | |
| | `openCodeFence` | `hljs.highlightAuto` on partial code. | | |
| Deliberately no hard assertions: CI timing is noisy and the value here is the | |
| before/after delta, not a gate. | |
| ### Caveat | |
| This measures one message's subtree. In the real app `ChatMessages.svelte` | |
| rebuilds its whole `displayMessages` list per token, so multiply by the number | |
| of rendered messages to get the conversation-level cost. | |
| ## Tier 2 - `../unit/agentic-hotpath.bench.ts` (project: `unit`, node) | |
| Per-call costs for the pure functions the curve implicates. | |
| ``` | |
| npx vitest bench --project=unit --run tests/unit/agentic-hotpath.bench.ts | |
| ``` | |