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 examples/model-conversion/scripts/utils/hf-upload-gguf-model.py from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.97 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
-
curl -L -o hf-upload-gguf-model.py https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/model-conversion/scripts/utils/hf-upload-gguf-model.py
1.97 kB
| #!/usr/bin/env python3 | |
| from huggingface_hub import HfApi | |
| import argparse | |
| import os | |
| def upload_gguf_file(local_file_path, repo_id, filename_in_repo=None): | |
| """ | |
| Upload a GGUF file to a Hugging Face model repository | |
| Args: | |
| local_file_path: Path to your local GGUF file | |
| repo_id: Your repository ID (e.g., "username/model-name") | |
| filename_in_repo: Optional custom name for the file in the repo | |
| """ | |
| if not os.path.exists(local_file_path): | |
| print(f"❌ File not found: {local_file_path}") | |
| return False | |
| if filename_in_repo is None: | |
| filename_in_repo = os.path.basename(local_file_path) | |
| if filename_in_repo is None or filename_in_repo == "": | |
| filename_in_repo = os.path.basename(local_file_path) | |
| print(f"📤 Uploading {local_file_path} to {repo_id}/{filename_in_repo}") | |
| api = HfApi() | |
| try: | |
| api.upload_file( | |
| path_or_fileobj=local_file_path, | |
| path_in_repo=filename_in_repo, | |
| repo_id=repo_id, | |
| repo_type="model", | |
| commit_message=f"Upload {filename_in_repo}" | |
| ) | |
| print("✅ Upload successful!") | |
| print(f"🔗 File available at: https://huggingface.co/{repo_id}/blob/main/{filename_in_repo}") | |
| return True | |
| except Exception as e: | |
| print(f"❌ Upload failed: {e}") | |
| return False | |
| # This script requires that the environment variable HF_TOKEN is set with your | |
| # Hugging Face API token. | |
| api = HfApi() | |
| parser = argparse.ArgumentParser(description='Upload a GGUF model to a Huggingface model repository') | |
| parser.add_argument('--gguf-model-path', '-m', help='The GGUF model file to upload', required=True) | |
| parser.add_argument('--repo-id', '-r', help='The repository to upload to', required=True) | |
| parser.add_argument('--name', '-o', help='The name in the model repository', required=False) | |
| args = parser.parse_args() | |
| upload_gguf_file(args.gguf_model_path, args.repo_id, args.name) | |