Instructions to use muralcode/Oracle.Aritha-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muralcode/Oracle.Aritha-AI with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="muralcode/Oracle.Aritha-AI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muralcode/Oracle.Aritha-AI")
model = AutoModelForCausalLM.from_pretrained("muralcode/Oracle.Aritha-AI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use muralcode/Oracle.Aritha-AI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="muralcode/Oracle.Aritha-AI",
	filename="Oracle.Aritha-AI-4B.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use muralcode/Oracle.Aritha-AI with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf muralcode/Oracle.Aritha-AI:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf muralcode/Oracle.Aritha-AI:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf muralcode/Oracle.Aritha-AI:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf muralcode/Oracle.Aritha-AI: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 muralcode/Oracle.Aritha-AI:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf muralcode/Oracle.Aritha-AI: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 muralcode/Oracle.Aritha-AI:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf muralcode/Oracle.Aritha-AI:Q4_K_M

Use Docker

docker model run hf.co/muralcode/Oracle.Aritha-AI:Q4_K_M

LM Studio
Jan

vLLM

How to use muralcode/Oracle.Aritha-AI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "muralcode/Oracle.Aritha-AI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muralcode/Oracle.Aritha-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/muralcode/Oracle.Aritha-AI:Q4_K_M

SGLang

How to use muralcode/Oracle.Aritha-AI with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "muralcode/Oracle.Aritha-AI" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muralcode/Oracle.Aritha-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "muralcode/Oracle.Aritha-AI" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muralcode/Oracle.Aritha-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use muralcode/Oracle.Aritha-AI with Ollama:
```
ollama run hf.co/muralcode/Oracle.Aritha-AI:Q4_K_M
```

Unsloth Studio new

How to use muralcode/Oracle.Aritha-AI 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 muralcode/Oracle.Aritha-AI 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 muralcode/Oracle.Aritha-AI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for muralcode/Oracle.Aritha-AI to start chatting

Pi new

How to use muralcode/Oracle.Aritha-AI with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf muralcode/Oracle.Aritha-AI: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": "muralcode/Oracle.Aritha-AI:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use muralcode/Oracle.Aritha-AI with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf muralcode/Oracle.Aritha-AI: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 muralcode/Oracle.Aritha-AI:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use muralcode/Oracle.Aritha-AI with Docker Model Runner:
```
docker model run hf.co/muralcode/Oracle.Aritha-AI:Q4_K_M
```

Lemonade

How to use muralcode/Oracle.Aritha-AI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull muralcode/Oracle.Aritha-AI:Q4_K_M

Run and chat with the model

lemonade run user.Oracle.Aritha-AI-Q4_K_M

List all available models

lemonade list

Oracle.Aritha-AI / chat_template.jinja

muralcode

Release Oracle.Aritha-AI by Aritha AI

5960019 verified 5 months ago

raw

history blame contribute delete

4.04 kB

	{%- if tools %}
	{{- '<\|im_start\|>system\n' }}
	{%- if messages[0].role == 'system' %}
	{{- messages[0].content + '\n\n' }}
	{%- endif %}
	{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
	{%- for tool in tools %}
	{{- "\n" }}
	{{- tool \| tojson }}
	{%- endfor %}
	{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }}
	{%- else %}
	{%- if messages[0].role == 'system' %}
	{{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }}
	{%- endif %}
	{%- endif %}
	{%- set ns = namespace(multi_step_tool=true, last_query_index=messages\|length - 1) %}
	{%- for message in messages[::-1] %}
	{%- set index = (messages\|length - 1) - loop.index0 %}
	{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
	{%- set ns.multi_step_tool = false %}
	{%- set ns.last_query_index = index %}
	{%- endif %}
	{%- endfor %}
	{%- for message in messages %}
	{%- if message.content is string %}
	{%- set content = message.content %}
	{%- else %}
	{%- set content = '' %}
	{%- endif %}
	{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
	{{- '<\|im_start\|>' + message.role + '\n' + content + '<\|im_end\|>' + '\n' }}
	{%- elif message.role == "assistant" %}
	{%- set reasoning_content = '' %}
	{%- if message.reasoning_content is string %}
	{%- set reasoning_content = message.reasoning_content %}
	{%- else %}
	{%- if '</think>' in content %}
	{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
	{%- set content = content.split('</think>')[-1].lstrip('\n') %}
	{%- endif %}
	{%- endif %}
	{%- if loop.index0 > ns.last_query_index %}
	{%- if loop.last or (not loop.last and reasoning_content) %}
	{{- '<\|im_start\|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
	{%- else %}
	{{- '<\|im_start\|>' + message.role + '\n' + content }}
	{%- endif %}
	{%- else %}
	{{- '<\|im_start\|>' + message.role + '\n' + content }}
	{%- endif %}
	{%- if message.tool_calls %}
	{%- for tool_call in message.tool_calls %}
	{%- if (loop.first and content) or (not loop.first) %}
	{{- '\n' }}
	{%- endif %}
	{%- if tool_call.function %}
	{%- set tool_call = tool_call.function %}
	{%- endif %}
	{{- '<tool_call>\n{"name": "' }}
	{{- tool_call.name }}
	{{- '", "arguments": ' }}
	{%- if tool_call.arguments is string %}
	{{- tool_call.arguments }}
	{%- else %}
	{{- tool_call.arguments \| tojson }}
	{%- endif %}
	{{- '}\n</tool_call>' }}
	{%- endfor %}
	{%- endif %}
	{{- '<\|im_end\|>\n' }}
	{%- elif message.role == "tool" %}
	{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
	{{- '<\|im_start\|>user' }}
	{%- endif %}
	{{- '\n<tool_response>\n' }}
	{{- content }}
	{{- '\n</tool_response>' }}
	{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
	{{- '<\|im_end\|>\n' }}
	{%- endif %}
	{%- endif %}
	{%- endfor %}
	{%- if add_generation_prompt %}
	{{- '<\|im_start\|>assistant\n' }}
	{%- endif %}