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-backend.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 3.19 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp
-
curl -L -o backend-dispatched-backend.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp
3.19 kB
| static uint32_t validate_graph_operation(size_t cgraph_size, uint32_t shmem_res_id, const char * operation) { | |
| if (cgraph_size == 0) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Zero-size computation graph\n", operation); | |
| return 1; | |
| } | |
| // place-holder: validate that the size of shmem_res_id is <= cgraph_size | |
| // need to add another method in the Virgl->APIR callback interface | |
| GGML_UNUSED(shmem_res_id); | |
| return 0; // Valid | |
| } | |
| uint32_t backend_backend_graph_compute(apir_encoder * enc, apir_decoder * dec, virgl_apir_context * ctx) { | |
| GGML_UNUSED(ctx); | |
| static bool async_backend_initialized = false; | |
| static bool async_backend; | |
| if (!async_backend_initialized) { | |
| ggml_backend_dev_props props; | |
| dev->iface.get_props(dev, &props); | |
| async_backend = props.caps.async; | |
| async_backend_initialized = true; | |
| } | |
| uint32_t shmem_res_id; | |
| apir_decode_virtgpu_shmem_res_id(dec, &shmem_res_id); | |
| const 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__); | |
| apir_decoder_set_fatal(dec); | |
| return 1; | |
| } | |
| size_t cgraph_size; | |
| apir_decode_size_t(dec, &cgraph_size); | |
| if (validate_graph_operation(cgraph_size, shmem_res_id, __func__) != 0) { | |
| apir_decoder_set_fatal(dec); | |
| return 1; | |
| } | |
| apir_decoder secondary_dec = apir_new_decoder((const char *) shmem_data, cgraph_size); | |
| ggml_cgraph * cgraph = apir_decode_ggml_cgraph(&secondary_dec, cgraph_size); | |
| if (!cgraph || apir_decoder_get_fatal(&secondary_dec)) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Failed to deserialize computation graph\n", __func__); | |
| return 1; | |
| } | |
| if (cgraph->n_nodes < 0 || cgraph->n_leafs < 0) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Invalid negative node/leaf count: nodes=%d leafs=%d\n", __func__, | |
| cgraph->n_nodes, cgraph->n_leafs); | |
| return 1; | |
| } | |
| ggml_status status; | |
| for (int idx = 0; idx < cgraph->n_nodes; idx++) { | |
| ggml_tensor * op = ggml_graph_node(cgraph, idx); | |
| if (dev->iface.supports_op(dev, op)) { | |
| continue; | |
| } | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Graph node %d (%s) not supported by the backend\n", __func__, idx, | |
| ggml_op_desc(op)); | |
| status = GGML_STATUS_ABORTED; | |
| apir_encode_ggml_status(enc, &status); | |
| return 0; | |
| } | |
| // Check if backend is properly initialized | |
| if (!bck) { | |
| GGML_LOG_ERROR(GGML_VIRTGPU_BCK "%s: Backend not initialized (bck is null)\n", __func__); | |
| return 1; | |
| } | |
| status = bck->iface.graph_compute(bck, cgraph); | |
| if (async_backend && bck->iface.synchronize) { | |
| bck->iface.synchronize(bck); | |
| } | |
| apir_encode_ggml_status(enc, &status); | |
| return 0; | |
| } | |