Knixee/atlas-ru-dataset-v2
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How to use Knixee/Atlas-nms-v2 with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Knixee/Atlas-nms-v2:Q4_K_M # Run inference directly in the terminal: llama cli -hf Knixee/Atlas-nms-v2:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Knixee/Atlas-nms-v2:Q4_K_M # Run inference directly in the terminal: llama cli -hf Knixee/Atlas-nms-v2:Q4_K_M
# 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 Knixee/Atlas-nms-v2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Knixee/Atlas-nms-v2:Q4_K_M
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 Knixee/Atlas-nms-v2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Knixee/Atlas-nms-v2:Q4_K_M
docker model run hf.co/Knixee/Atlas-nms-v2:Q4_K_M
How to use Knixee/Atlas-nms-v2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Knixee/Atlas-nms-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Knixee/Atlas-nms-v2",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/Knixee/Atlas-nms-v2:Q4_K_M
How to use Knixee/Atlas-nms-v2 with Ollama:
ollama run hf.co/Knixee/Atlas-nms-v2:Q4_K_M
How to use Knixee/Atlas-nms-v2 with Unsloth Studio:
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 Knixee/Atlas-nms-v2 to start chatting
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 Knixee/Atlas-nms-v2 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Knixee/Atlas-nms-v2 to start chatting
How to use Knixee/Atlas-nms-v2 with Pi:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Knixee/Atlas-nms-v2:Q4_K_M
# 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": "Knixee/Atlas-nms-v2:Q4_K_M"
}
]
}
}
}# Start Pi in your project directory: pi
How to use Knixee/Atlas-nms-v2 with Hermes Agent:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Knixee/Atlas-nms-v2:Q4_K_M
# 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 Knixee/Atlas-nms-v2:Q4_K_M
hermes
How to use Knixee/Atlas-nms-v2 with OpenClaw:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Knixee/Atlas-nms-v2:Q4_K_M
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Knixee/Atlas-nms-v2:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
openclaw agent --local --agent main --message "Hello from Hugging Face"
How to use Knixee/Atlas-nms-v2 with Docker Model Runner:
docker model run hf.co/Knixee/Atlas-nms-v2:Q4_K_M
How to use Knixee/Atlas-nms-v2 with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Knixee/Atlas-nms-v2:Q4_K_M
lemonade run user.Atlas-nms-v2-Q4_K_M
lemonade list
Local Atlas from the game No Man's Sky.
Examples for running GGUF (via llama.cpp) and AWQ (via vLLM).
# pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="Knixee/Atlas-nms-v2",
filename="Atlas-2.0-Q4_K_M.gguf", # Or Q4_K_S, Q8_0, BF16
)
output = llm("User: Hello!\nAssistant:", max_tokens=512)
print(output["choices"][0]["text"])
To launch 4bit AWQ version you need to download this folder
# pip install vllm
vllm serve Knixee/Atlas-nms-v2 \
--subfolder Atlas-2.0-AWQ-4bit \
--served-model-name Atlas-nms-v2 \
--quantization awq \
--max-model-len 8192 \
--gpu-memory-utilization 0.88 \
--max-num-seqs 6 \
--port 8148
API Request Example:
curl http://localhost:8148/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "Atlas-nms-v2", "messages": [{"role": "user", "content": "Hello!"}]}'
4-bit
8-bit
16-bit