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/src/lib/constants/headers.constants.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.46 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/headers.constants.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/headers.constants.ts
-
curl -L -o headers.constants.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/headers.constants.ts
1.46 kB
| /** Number of trailing characters to keep visible when partially redacting mcp-session-id */ | |
| const MCP_SESSION_ID_VISIBLE_CHARS = 5; | |
| /** HTTP header handling for API and MCP requests. */ | |
| export const HEADERS = { | |
| /** Canonical casing for the Authorization header (RFC 7235) */ | |
| AUTHORIZATION: 'Authorization', | |
| /** Bearer scheme prefix used for Authorization headers (RFC 6750) */ | |
| BEARER: 'Bearer ', | |
| /** Content-Type HTTP header name */ | |
| CONTENT_TYPE: 'Content-Type', | |
| /** Partial-redaction rules for MCP headers: header name -> visible trailing chars */ | |
| PARTIAL_REDACT: new Map<string, number>([['mcp-session-id', MCP_SESSION_ID_VISIBLE_CHARS]]), | |
| /** Header names whose values should be redacted in diagnostic logs */ | |
| REDACTED: new Set([ | |
| 'authorization', | |
| 'api-key', | |
| 'cookie', | |
| 'mcp-session-id', | |
| 'proxy-authorization', | |
| 'set-cookie', | |
| 'x-auth-token', | |
| 'x-api-key' | |
| ]), | |
| /** Header carrying the stream-session identity (conversation id, optionally with a model suffix) */ | |
| X_CONVERSATION_ID_HEADER: 'X-Conversation-Id', | |
| /** Header asking the server to encode a tool's output differently, e.g. read_file returning base64. */ | |
| X_RESP_TYPE_HEADER: 'x-resp-type', | |
| /** Header carrying the working directory a tool call runs in; the model cannot override it */ | |
| X_TOOL_CWD_HEADER: 'x-tool-cwd' | |
| }; | |
| /** `X_RESP_TYPE_HEADER` value that makes read_file return raw bytes as base64 instead of text. */ | |
| export const RESP_TYPE_BASE64 = 'base64'; | |