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"
| """ | |
| RoutingMatrix - JSON skill routing with precedence and typed keys. | |
| Deterministic skill index (from matrix), hot-swappable LoRA adapter paths. | |
| Back-compatible with .skill files via load_legacy_skill(). | |
| """ | |
| import hashlib | |
| import json | |
| import re | |
| from dataclasses import dataclass | |
| from pathlib import Path | |
| from typing import Dict, List, Optional | |
| class RouteDecision: | |
| skill: Optional[str] = None | |
| token: Optional[str] = None | |
| index: Optional[int] = None | |
| adapter: Optional[str] = None | |
| score: float = 0.0 | |
| def is_skill(self) -> bool: | |
| return self.skill is not None | |
| def deterministic_index(name: str, num_slots: int = 64) -> int: | |
| """Stable skill->index mapping (replaces runtime hash(), which varies per process).""" | |
| return int(hashlib.sha256(name.encode("utf-8")).hexdigest(), 16) % num_slots | |
| class RoutingMatrix: | |
| def __init__(self, matrix_path: Optional[str] = None): | |
| self.skills: List[Dict] = [] | |
| self.default: Dict = {} | |
| if matrix_path: | |
| self.load(matrix_path) | |
| def load(self, matrix_path: str): | |
| data = json.loads(Path(matrix_path).read_text(encoding="utf-8")) | |
| self.skills = data.get("skills", []) | |
| self.default = data.get("default", {}) | |
| return len(self.skills) | |
| def load_legacy_skill(self, skill_path: str) -> Dict: | |
| """Adopt a .skill file into the matrix (keeps old runtime working).""" | |
| data = json.loads(Path(skill_path).read_text(encoding="utf-8")) | |
| entry = { | |
| "name": data["name"], | |
| "token": data["token"], | |
| "index": deterministic_index(data["name"]), | |
| "priority": 10, | |
| "patterns": [ | |
| {"type": "keyword", "value": p} for p in data.get("trigger_patterns", []) | |
| ], | |
| } | |
| self.skills.append(entry) | |
| return entry | |
| def _score(self, text: str, entry: Dict) -> float: | |
| low = text.lower() | |
| score = 0.0 | |
| for pat in entry.get("patterns", []): | |
| value = pat.get("value", "") | |
| ptype = pat.get("type", "regex") | |
| if ptype == "regex": | |
| if re.search(value, low): | |
| score += 1.0 | |
| elif ptype == "keyword": | |
| if value.lower() in low: | |
| score += 0.8 | |
| return score | |
| def route(self, text: str) -> RouteDecision: | |
| """Highest-scoring skill wins; ties broken by priority.""" | |
| best = RouteDecision() | |
| for entry in self.skills: | |
| score = self._score(text, entry) | |
| if score == 0: | |
| continue | |
| entry_prio = entry.get("priority", 0) | |
| best_prio = 0 if best.score == 0 else self._priority_of(best.skill) | |
| if score > best.score or (score == best.score and entry_prio > best_prio): | |
| best = RouteDecision( | |
| skill=entry["name"], | |
| token=entry.get("token"), | |
| index=entry.get("index", deterministic_index(entry["name"])), | |
| adapter=entry.get("adapter"), | |
| score=score, | |
| ) | |
| return best | |
| def _priority_of(self, name: Optional[str]) -> int: | |
| if not name: | |
| return 0 | |
| for e in self.skills: | |
| if e.get("name") == name: | |
| return e.get("priority", 0) | |
| return 0 |