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"
File size: 5,790 Bytes
82f262a | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | """
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
@dataclass
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,
}
@dataclass
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")
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