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: 2,866 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 | #pragma once
// clang-format off
#include "virtgpu.h"
#include "ggml-remoting.h"
#include "backend/shared/apir_backend.h"
#include "backend/shared/apir_cs_ggml.h"
#include "ggml-backend-impl.h"
// clang-format on
#define REMOTE_CALL_PREPARE(gpu_dev_name, encoder_name, apir_command_type__) \
int32_t REMOTE_CALL_PREPARE_forward_flag = (int32_t) apir_command_type__; \
const char * REMOTE_CALL_PREPARE_command_name = apir_dispatch_command_name(apir_command_type__); \
do { \
encoder_name = remote_call_prepare(gpu_dev_name, APIR_COMMAND_TYPE_FORWARD, REMOTE_CALL_PREPARE_forward_flag); \
if (!encoder_name) { \
GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__); \
} \
} while (0)
#define REMOTE_CALL(gpu_dev_name, encoder_name, decoder_name, ret_name) \
do { \
ret_name = (ApirForwardReturnCode) remote_call(gpu_dev_name, encoder_name, &decoder_name, 0, NULL); \
if (!decoder_name) { \
GGML_ABORT(GGML_VIRTGPU "%s: failed to kick the remote call", __func__); \
} \
if (ret_name < APIR_FORWARD_BASE_INDEX) { \
GGML_ABORT(GGML_VIRTGPU "%s: failed to forward the API call: %s: code %d", __func__, \
apir_forward_error(ret_name), ret_name); \
} \
ret_name = (ApirForwardReturnCode) (ret_name - APIR_FORWARD_BASE_INDEX); \
if (ret_name != 0) { \
GGML_ABORT(GGML_VIRTGPU "backend function '%s' failed (return code: %d)", \
REMOTE_CALL_PREPARE_command_name, ret_name); \
} \
} while (0)
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