Text Generation
GGUF
multilingual
llama.cpp
darkit-2.0
DarkAI Company
programming
reasoning
conversational
Instructions to use darkai-1/darkit-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use darkai-1/darkit-v2.0 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="darkai-1/darkit-v2.0", filename="Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use darkai-1/darkit-v2.0 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf darkai-1/darkit-v2.0:Q4_K_M # Run inference directly in the terminal: llama-cli -hf darkai-1/darkit-v2.0:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf darkai-1/darkit-v2.0:Q4_K_M # Run inference directly in the terminal: llama-cli -hf darkai-1/darkit-v2.0:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf darkai-1/darkit-v2.0:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf darkai-1/darkit-v2.0:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf darkai-1/darkit-v2.0:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf darkai-1/darkit-v2.0:Q4_K_M
Use Docker
docker model run hf.co/darkai-1/darkit-v2.0:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use darkai-1/darkit-v2.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "darkai-1/darkit-v2.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "darkai-1/darkit-v2.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/darkai-1/darkit-v2.0:Q4_K_M
- Ollama
How to use darkai-1/darkit-v2.0 with Ollama:
ollama run hf.co/darkai-1/darkit-v2.0:Q4_K_M
- Unsloth Studio new
How to use darkai-1/darkit-v2.0 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for darkai-1/darkit-v2.0 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for darkai-1/darkit-v2.0 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for darkai-1/darkit-v2.0 to start chatting
- Pi new
How to use darkai-1/darkit-v2.0 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf darkai-1/darkit-v2.0:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "darkai-1/darkit-v2.0:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use darkai-1/darkit-v2.0 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf darkai-1/darkit-v2.0:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default darkai-1/darkit-v2.0:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use darkai-1/darkit-v2.0 with Docker Model Runner:
docker model run hf.co/darkai-1/darkit-v2.0:Q4_K_M
- Lemonade
How to use darkai-1/darkit-v2.0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull darkai-1/darkit-v2.0:Q4_K_M
Run and chat with the model
lemonade run user.darkit-v2.0-Q4_K_M
List all available models
lemonade list
File size: 2,660 Bytes
446a244 540809c 446a244 bc95a68 446a244 bc95a68 5403104 bc95a68 7cd419b bc95a68 91f1244 bc95a68 446a244 5403104 bc95a68 91f1244 446a244 91f1244 0def099 91f1244 bc95a68 91f1244 446a244 91f1244 446a244 91f1244 446a244 540809c 446a244 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install llama-cpp-python huggingface_hub --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import HfApi\n",
"from llama_cpp import Llama\n",
"import os\n",
"\n",
"REPO_ID = \"darkai-1/darkit-v2.0\"\n",
"api = HfApi()\n",
"\n",
"files = api.list_repo_files(REPO_ID)\n",
"gguf_files = [f for f in files if f.endswith(\".gguf\")]\n",
"\n",
"for i, f in enumerate(gguf_files):\n",
" print(f\"[{i}] {f}\")\n",
"\n",
"choice = int(input(\"Select model number: \"))\n",
"filename = gguf_files[choice]\n",
"\n",
"llm = Llama.from_pretrained(\n",
" repo_id=REPO_ID,\n",
" filename=filename,\n",
" n_ctx=2048,\n",
" n_batch=128,\n",
" n_ubatch=128,\n",
" n_threads=os.cpu_count() or 4,\n",
" n_threads_batch=os.cpu_count() or 4,\n",
" n_gpu_layers=-1,\n",
" verbose=False,\n",
" no_perf=True,\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm.set_cache(None)\n",
"\n",
"PROMPT = \"Hi how are you?\"\n",
"\n",
"stream = llm(\n",
" f\"<|im_start|>user\\n{PROMPT}<|im_end|>\\n<|im_start|>assistant\\n\",\n",
" max_tokens=128,\n",
" temperature=0.7,\n",
" top_p=0.8,\n",
" top_k=20,\n",
" stream=True,\n",
" stop=[\n",
" \"<|im_end|>\",\n",
" \"<|im_start|>\",\n",
" \"\\n\\nUser:\",\n",
" \"\\n\\nAssistant:\"\n",
" ],\n",
" echo=False\n",
")\n",
"\n",
"for chunk in stream:\n",
" text = chunk[\"choices\"][0][\"text\"]\n",
"\n",
" if text:\n",
" print(text, end=\"\", flush=True)\n",
"\n",
"print()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |