Text Generation
Transformers
Safetensors
GGUF
English
causal-lm
qwen2.5
reasoning
code-generation
Mixture of Experts
qlora
multimodal
tool-use
Eval Results (legacy)
conversational
Instructions to use ram1234598766/Cesium2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ram1234598766/Cesium2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ram1234598766/Cesium2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ram1234598766/Cesium2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ram1234598766/Cesium2 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 ram1234598766/Cesium2:Q8_0 # Run inference directly in the terminal: llama cli -hf ram1234598766/Cesium2:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ram1234598766/Cesium2:Q8_0 # Run inference directly in the terminal: llama cli -hf ram1234598766/Cesium2:Q8_0
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 ram1234598766/Cesium2:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf ram1234598766/Cesium2:Q8_0
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 ram1234598766/Cesium2:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ram1234598766/Cesium2:Q8_0
Use Docker
docker model run hf.co/ram1234598766/Cesium2:Q8_0
- LM Studio
- Jan
- vLLM
How to use ram1234598766/Cesium2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ram1234598766/Cesium2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ram1234598766/Cesium2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ram1234598766/Cesium2:Q8_0
- SGLang
How to use ram1234598766/Cesium2 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 "ram1234598766/Cesium2" \ --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": "ram1234598766/Cesium2", "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 "ram1234598766/Cesium2" \ --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": "ram1234598766/Cesium2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use ram1234598766/Cesium2 with Ollama:
ollama run hf.co/ram1234598766/Cesium2:Q8_0
- Unsloth Studio
How to use ram1234598766/Cesium2 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 ram1234598766/Cesium2 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 ram1234598766/Cesium2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ram1234598766/Cesium2 to start chatting
- Pi
How to use ram1234598766/Cesium2 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ram1234598766/Cesium2:Q8_0
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": "ram1234598766/Cesium2:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ram1234598766/Cesium2 with Docker Model Runner:
docker model run hf.co/ram1234598766/Cesium2:Q8_0
- Lemonade
How to use ram1234598766/Cesium2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ram1234598766/Cesium2:Q8_0
Run and chat with the model
lemonade run user.Cesium2-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use ram1234598766/Cesium2 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ram1234598766/Cesium2:Q8_0
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 ram1234598766/Cesium2:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ram1234598766/Cesium2 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ram1234598766/Cesium2:Q8_0
Configure OpenClaw
# 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 "ram1234598766/Cesium2:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
| """ | |
| ToolRegistry - JSON-structured function calling for MORPH-AI v6. | |
| Provides tool registration, validation, and execution. | |
| """ | |
| import json | |
| import re | |
| from dataclasses import dataclass, field | |
| from typing import Any, Callable, Dict, List, Optional | |
| class ToolCall: | |
| tool_name: str | |
| arguments: Dict[str, Any] | |
| confidence: float = 1.0 | |
| raw: str = "" | |
| def to_dict(self) -> dict: | |
| return { | |
| "tool": self.tool_name, | |
| "arguments": self.arguments, | |
| "confidence": self.confidence, | |
| } | |
| class Tool: | |
| name: str | |
| description: str | |
| parameters: Dict[str, Any] | |
| handler: Callable | |
| required: List[str] = field(default_factory=list) | |
| class ToolRegistry: | |
| """Registry for tools that can be called by the model.""" | |
| def __init__(self): | |
| self._tools: Dict[str, Tool] = {} | |
| self._register_defaults() | |
| def _register_defaults(self): | |
| """Register built-in tools.""" | |
| self.register(Tool( | |
| name="calculator", | |
| description="Evaluate a mathematical expression", | |
| parameters={ | |
| "type": "object", | |
| "properties": { | |
| "expression": {"type": "string", "description": "Math expression to evaluate"} | |
| }, | |
| "required": ["expression"] | |
| }, | |
| handler=self._calc_handler, | |
| required=["expression"] | |
| )) | |
| self.register(Tool( | |
| name="search", | |
| description="Search the web for information", | |
| parameters={ | |
| "type": "object", | |
| "properties": { | |
| "query": {"type": "string", "description": "Search query"} | |
| }, | |
| "required": ["query"] | |
| }, | |
| handler=self._search_handler, | |
| required=["query"] | |
| )) | |
| self.register(Tool( | |
| name="code_exec", | |
| description="Execute Python code safely", | |
| parameters={ | |
| "type": "object", | |
| "properties": { | |
| "code": {"type": "string", "description": "Python code to execute"} | |
| }, | |
| "required": ["code"] | |
| }, | |
| handler=self._code_exec_handler, | |
| required=["code"] | |
| )) | |
| self.register(Tool( | |
| name="current_time", | |
| description="Get the current date and time", | |
| parameters={ | |
| "type": "object", | |
| "properties": { | |
| "timezone": {"type": "string", "description": "Timezone (optional)"} | |
| }, | |
| "required": [] | |
| }, | |
| handler=self._time_handler, | |
| required=[] | |
| )) | |
| def register(self, tool: Tool): | |
| self._tools[tool.name] = tool | |
| def get_tool_schema(self) -> str: | |
| """Return JSON schema of all registered tools for prompt injection.""" | |
| tools = [] | |
| for t in self._tools.values(): | |
| tools.append({ | |
| "name": t.name, | |
| "description": t.description, | |
| "parameters": t.parameters | |
| }) | |
| return json.dumps(tools, indent=2) | |
| def parse_calls(self, text: str) -> List[ToolCall]: | |
| """Extract JSON tool calls from model response.""" | |
| calls = [] | |
| pattern = r'```json\s*(\{.*?\})\s*```' | |
| for m in re.finditer(pattern, text, re.DOTALL): | |
| try: | |
| data = json.loads(m.group(1)) | |
| if "tool" in data: | |
| calls.append(ToolCall( | |
| tool_name=data["tool"], | |
| arguments=data.get("arguments", {}), | |
| confidence=data.get("confidence", 1.0), | |
| raw=m.group(1) | |
| )) | |
| except json.JSONDecodeError: | |
| continue | |
| return calls | |
| def validate(self, call: ToolCall) -> bool: | |
| tool = self._tools.get(call.tool_name) | |
| if not tool: | |
| return False | |
| for req in tool.required: | |
| if req not in call.arguments: | |
| return False | |
| return True | |
| def execute(self, call: ToolCall) -> str: | |
| if not self.validate(call): | |
| return f"Error: invalid tool call {call.tool_name}" | |
| tool = self._tools[call.tool_name] | |
| try: | |
| result = tool.handler(**call.arguments) | |
| return str(result) | |
| except Exception as e: | |
| return f"Error executing {call.tool_name}: {e}" | |
| def _calc_handler(self, expression: str) -> Any: | |
| try: | |
| result = eval(expression, {"__builtins__": {}}, {}) | |
| return result | |
| except Exception as e: | |
| return f"Calculation error: {e}" | |
| def _search_handler(self, query: str) -> str: | |
| from search import SearchClient | |
| client = SearchClient() | |
| results = client.search(query, num=3) | |
| return "\n".join(f"- {r.title}: {r.snippet}" for r in results) | |
| def _code_exec_handler(self, code: str) -> str: | |
| from architecture import CodeSandbox | |
| sandbox = CodeSandbox() | |
| result = sandbox.execute(code) | |
| if result["success"]: | |
| return f"Output: {result['output']}" | |
| return f"Error: {result['error']}" | |
| def _time_handler(self, timezone: Optional[str] = None) -> str: | |
| from datetime import datetime, timezone as tz | |
| import pytz | |
| if timezone: | |
| try: | |
| tz_obj = pytz.timezone(timezone) | |
| now = datetime.now(tz_obj) | |
| except Exception: | |
| now = datetime.now(tz.UTC) | |
| else: | |
| now = datetime.now() | |
| return now.strftime("%Y-%m-%d %H:%M:%S %Z") | |