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: 4,094 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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | #include "backend-dispatched.h"
#include "backend-virgl-apir.h"
#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-impl.h"
#include <cstdint>
uint32_t backend_device_get_device_count(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
int32_t dev_count = reg->iface.get_device_count(reg);
apir_encode_int32_t(enc, &dev_count);
return 0;
}
uint32_t backend_device_get_count(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
int32_t dev_count = reg->iface.get_device_count(reg);
apir_encode_int32_t(enc, &dev_count);
return 0;
}
uint32_t backend_device_get_name(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
const char * string = dev->iface.get_name(dev);
const size_t string_size = strlen(string) + 1;
apir_encode_array_size(enc, string_size);
apir_encode_char_array(enc, string, string_size);
return 0;
}
uint32_t backend_device_get_description(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
const char * string = dev->iface.get_description(dev);
const size_t string_size = strlen(string) + 1;
apir_encode_array_size(enc, string_size);
apir_encode_char_array(enc, string, string_size);
return 0;
}
uint32_t backend_device_get_type(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
uint32_t type = dev->iface.get_type(dev);
apir_encode_uint32_t(enc, &type);
return 0;
}
uint32_t backend_device_get_memory(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
size_t free, total;
dev->iface.get_memory(dev, &free, &total);
apir_encode_size_t(enc, &free);
apir_encode_size_t(enc, &total);
return 0;
}
uint32_t backend_device_supports_op(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
const ggml_tensor * op = apir_decode_ggml_tensor_inplace(dec);
bool supports_op = dev->iface.supports_op(dev, op);
apir_encode_bool_t(enc, &supports_op);
return 0;
}
uint32_t backend_device_get_buffer_type(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
ggml_backend_buffer_type_t bufft = dev->iface.get_buffer_type(dev);
apir_encode_ggml_buffer_type(enc, bufft);
return 0;
}
uint32_t backend_device_get_props(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
ggml_backend_dev_props props;
dev->iface.get_props(dev, &props);
apir_encode_bool_t(enc, &props.caps.async);
apir_encode_bool_t(enc, &props.caps.host_buffer);
apir_encode_bool_t(enc, &props.caps.buffer_from_host_ptr);
apir_encode_bool_t(enc, &props.caps.events);
apir_encode_bool_t(enc, &props.caps.mmap_support);
return 0;
}
uint32_t backend_device_buffer_from_ptr(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) {
GGML_UNUSED(ctx);
GGML_UNUSED(dec);
uint32_t shmem_res_id;
apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id);
void * shmem_ptr = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id);
if (!shmem_ptr) {
GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__);
apir_decoder_set_fatal(dec);
return 1;
}
size_t size;
apir_decode_size_t(dec, &size);
size_t max_tensor_size;
apir_decode_size_t(dec, &max_tensor_size);
ggml_backend_buffer_t buffer;
buffer = dev->iface.buffer_from_host_ptr(dev, shmem_ptr, size, max_tensor_size);
apir_encode_ggml_buffer(enc, buffer);
apir_encode_ggml_buffer_type(enc, buffer->buft);
if (buffer) {
apir_track_backend_buffer(buffer);
}
return 0;
}
|