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 tools/mtmd/models/nemotron-v2-vl.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/mtmd/models/nemotron-v2-vl.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/mtmd/models/nemotron-v2-vl.cpp
-
curl -L -o nemotron-v2-vl.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/mtmd/models/nemotron-v2-vl.cpp
1.16 kB
| ggml_cgraph * clip_graph_nemotron_v2_vl::build() { | |
| GGML_ASSERT(model.class_embedding != nullptr); | |
| GGML_ASSERT(model.position_embeddings != nullptr); | |
| const int n_registers = model.class_embedding->ne[1]; | |
| const int n_pos = n_patches + n_registers; | |
| ggml_tensor * inp = build_inp(); | |
| // add position embeddings (pre-downsampled during GGUF conversion for fixed 512x512 input) | |
| inp = ggml_add(ctx0, inp, model.position_embeddings); | |
| cb(inp, "inp_pos", -1); | |
| inp = ggml_concat(ctx0, model.class_embedding, inp, 1); | |
| ggml_tensor * cur = build_vit(inp, n_pos, NORM_TYPE_NORMAL, hparams.ffn_op, nullptr, nullptr); | |
| cur = ggml_view_2d(ctx0, cur, | |
| n_embd, n_patches, | |
| ggml_row_size(cur->type, n_embd), | |
| n_registers * ggml_row_size(cur->type, n_embd)); | |
| cur = build_patch_merge_permute(cur, model.hparams.n_merge); | |
| { | |
| cur = build_norm(cur, model.mm_0_w, nullptr, NORM_TYPE_RMS, 1e-6, -1); | |
| cur = build_ffn(cur, model.mm_1_w, nullptr, nullptr, nullptr, model.mm_3_w, nullptr, FFN_RELU_SQR, -1); | |
| } | |
| ggml_build_forward_expand(gf, cur); | |
| return gf; | |
| } | |