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
File size: 1,456 Bytes
c59fd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | /** 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';
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