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 conversion/codeshell.py from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.02 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/conversion/codeshell.py
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/conversion/codeshell.py
-
curl -L -o codeshell.py https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/conversion/codeshell.py
1.02 kB
| from __future__ import annotations | |
| from .base import ModelBase, TextModel, gguf | |
| class CodeShellModel(TextModel): | |
| model_arch = gguf.MODEL_ARCH.CODESHELL | |
| def set_gguf_parameters(self): | |
| self.gguf_writer.add_context_length(self.hparams["n_positions"]) | |
| self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) | |
| self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) | |
| self.gguf_writer.add_block_count(self.block_count) | |
| self.gguf_writer.add_head_count(self.hparams["n_head"]) | |
| self.gguf_writer.add_head_count_kv(self.hparams["num_query_groups"]) | |
| self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) | |
| self.gguf_writer.add_file_type(self.ftype) | |
| self.gguf_writer.add_rope_freq_base(10000.0) | |
| self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) | |
| self.gguf_writer.add_rope_scaling_factor(1.0) | |