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"
| """ | |
| KVStore - persistent key-value cache (disk-backed JSON, TTL + LRU eviction). | |
| Wraps cross-turn conversation context so it survives process restarts. | |
| """ | |
| import json | |
| import threading | |
| import time | |
| from pathlib import Path | |
| from typing import Any, Dict, List, Optional | |
| class KVStore: | |
| def __init__(self, path: str, max_entries: int = 1000, default_ttl: float = 3600.0): | |
| self.path = Path(path) | |
| self.max_entries = max_entries | |
| self.default_ttl = default_ttl | |
| self._lock = threading.Lock() | |
| self._data: Dict[str, Any] = {} | |
| self._meta: Dict[str, Dict] = {} # key -> {exp, last, size} | |
| if self.path.exists(): | |
| self._load() | |
| def _load(self): | |
| try: | |
| raw = json.loads(self.path.read_text(encoding="utf-8")) | |
| self._data = raw.get("data", {}) | |
| self._meta = raw.get("meta", {}) | |
| except (json.JSONDecodeError, OSError): | |
| self._data, self._meta = {}, {} | |
| def _persist(self): | |
| self.path.parent.mkdir(parents=True, exist_ok=True) | |
| tmp = self.path.with_suffix(".tmp") | |
| tmp.write_text( | |
| json.dumps({"data": self._data, "meta": self._meta}), | |
| encoding="utf-8", | |
| ) | |
| tmp.replace(self.path) | |
| def _evict(self): | |
| now = time.time() | |
| expired = [k for k, m in self._meta.items() if m.get("exp", 0) < now] | |
| for k in expired: | |
| self._data.pop(k, None) | |
| self._meta.pop(k, None) | |
| while len(self._data) > self.max_entries: | |
| oldest = min(self._meta, key=lambda k: self._meta[k].get("last", 0)) | |
| self._data.pop(oldest, None) | |
| self._meta.pop(oldest, None) | |
| def get(self, key: str, default: Any = None) -> Any: | |
| with self._lock: | |
| m = self._meta.get(key) | |
| if m is None: | |
| return default | |
| if m.get("exp", 0) < time.time(): | |
| self._data.pop(key, None) | |
| self._meta.pop(key, None) | |
| return default | |
| m["last"] = time.time() | |
| return self._data.get(key, default) | |
| def set(self, key: str, value: Any, ttl: Optional[float] = None): | |
| with self._lock: | |
| exp = time.time() + (ttl if ttl is not None else self.default_ttl) | |
| self._data[key] = value | |
| self._meta[key] = { | |
| "exp": exp, | |
| "last": time.time(), | |
| "size": len(json.dumps(value, default=str)), | |
| } | |
| self._evict() | |
| self._persist() | |
| def delete(self, key: str): | |
| with self._lock: | |
| self._data.pop(key, None) | |
| self._meta.pop(key, None) | |
| self._persist() | |
| def keys(self) -> List[str]: | |
| with self._lock: | |
| return list(self._data.keys()) | |
| def flush(self): | |
| with self._lock: | |
| self._persist() |