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 ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 5.14 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp
-
curl -L -o backend-dispatched-buffer.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp
5.14 kB
| static uint32_t validate_buffer_operation(size_t offset, size_t size, const char * operation) { | |
| // Only check for critical integer overflow - no arbitrary size limits | |
| if (offset > SIZE_MAX - size) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Integer overflow in offset+size: %zu + %zu\n", operation, offset, size); | |
| return 1; | |
| } | |
| return 0; // Valid | |
| } | |
| uint32_t backend_buffer_get_base(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| uintptr_t base = (uintptr_t) buffer->iface.get_base(buffer); | |
| apir_encode_uintptr_t(enc, &base); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_set_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| GGML_UNUSED(enc); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| ggml_tensor * tensor; | |
| // safe to remove the const qualifier here | |
| tensor = (ggml_tensor *) (uintptr_t) apir_decode_ggml_tensor(dec); | |
| uint32_t shmem_res_id; | |
| apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); | |
| size_t offset; | |
| apir_decode_size_t(dec, &offset); | |
| size_t size; | |
| apir_decode_size_t(dec, &size); | |
| if (validate_buffer_operation(offset, size, __func__) != 0) { | |
| return 1; | |
| } | |
| void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); | |
| if (!shmem_data) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__); | |
| return 1; | |
| } | |
| buffer->iface.set_tensor(buffer, tensor, shmem_data, offset, size); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_get_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| GGML_UNUSED(enc); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| const ggml_tensor * tensor; | |
| // safe to remove the const qualifier here | |
| tensor = apir_decode_ggml_tensor(dec); | |
| uint32_t shmem_res_id; | |
| apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); | |
| size_t offset; | |
| apir_decode_size_t(dec, &offset); | |
| size_t size; | |
| apir_decode_size_t(dec, &size); | |
| if (validate_buffer_operation(offset, size, __func__) != 0) { | |
| return 1; | |
| } | |
| void * shmem_data = ctx->iface->get_shmem_ptr(ctx->ctx_id, shmem_res_id); | |
| if (!shmem_data) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Couldn't get the shmem addr from virgl\n", __func__); | |
| return 1; | |
| } | |
| buffer->iface.get_tensor(buffer, tensor, shmem_data, offset, size); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_cpy_tensor(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| const ggml_tensor * src; | |
| // safe to remove the const qualifier here | |
| src = apir_decode_ggml_tensor(dec); | |
| ggml_tensor * dst = (ggml_tensor *) (uintptr_t) apir_decode_ggml_tensor(dec); | |
| bool ret = buffer->iface.cpy_tensor(buffer, src, (ggml_tensor *) dst); | |
| apir_encode_bool_t(enc, &ret); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_clear(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| GGML_UNUSED(enc); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| uint8_t value; | |
| apir_decode_uint8_t(dec, &value); | |
| buffer->iface.clear(buffer, value); | |
| return 0; | |
| } | |
| uint32_t backend_buffer_free_buffer(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| GGML_UNUSED(enc); | |
| ggml_backend_buffer_t buffer; | |
| buffer = apir_decode_ggml_buffer(dec); | |
| if (!buffer || apir_decoder_get_fatal(dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid buffer handle from guest\n", __func__); | |
| return 1; | |
| } | |
| if (!apir_untrack_backend_buffer(buffer)) { | |
| GGML_LOG_WARN(GGML_VIRTGPU_BCK "%s: unknown buffer %p\n", __func__, (void *) buffer); | |
| return 1; | |
| } | |
| buffer->iface.free_buffer(buffer); | |
| return 0; | |
| } | |