Text Classification
Transformers
Safetensors
GGUF
English
qwen3_5
image-text-to-text
clefsift
clef
ai-text-detection
classification
structured-output
systemone
custom-code
decision-model
conversational
Instructions to use spmurrayzzz/clefsift with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spmurrayzzz/clefsift with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spmurrayzzz/clefsift") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("spmurrayzzz/clefsift") model = AutoModelForMultimodalLM.from_pretrained("spmurrayzzz/clefsift", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use spmurrayzzz/clefsift 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 spmurrayzzz/clefsift:Q8_0 # Run inference directly in the terminal: llama cli -hf spmurrayzzz/clefsift:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf spmurrayzzz/clefsift:Q8_0 # Run inference directly in the terminal: llama cli -hf spmurrayzzz/clefsift: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 spmurrayzzz/clefsift:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf spmurrayzzz/clefsift: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 spmurrayzzz/clefsift:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf spmurrayzzz/clefsift:Q8_0
Use Docker
docker model run hf.co/spmurrayzzz/clefsift:Q8_0
- LM Studio
- Jan
- Ollama
How to use spmurrayzzz/clefsift with Ollama:
ollama run hf.co/spmurrayzzz/clefsift:Q8_0
- Unsloth Desktop
- Pi
How to use spmurrayzzz/clefsift with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf spmurrayzzz/clefsift:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "spmurrayzzz/clefsift:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use spmurrayzzz/clefsift with Docker Model Runner:
docker model run hf.co/spmurrayzzz/clefsift:Q8_0
- Lemonade
How to use spmurrayzzz/clefsift with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull spmurrayzzz/clefsift:Q8_0
Run and chat with the model
lemonade run user.clefsift-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use spmurrayzzz/clefsift with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf spmurrayzzz/clefsift: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 spmurrayzzz/clefsift:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use spmurrayzzz/clefsift with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf spmurrayzzz/clefsift: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 "spmurrayzzz/clefsift: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"
Download release.json from spmurrayzzz/clefsift: direct link, hf CLI and curl.
- Browser
- Download file 1.46 kB
-
https://huggingface.co/spmurrayzzz/clefsift/resolve/main/release.json
- Command line
-
hf download hf://spmurrayzzz/clefsift/release.json
-
curl -L -o release.json https://huggingface.co/spmurrayzzz/clefsift/resolve/main/release.json
1.46 kB
| { | |
| "model": "ClefSift", | |
| "repo_id": "spmurrayzzz/clefsift", | |
| "experiment": "clef-27b-llmtrace-en-fourway-v2", | |
| "checkpoint_step": 128, | |
| "base_model": "Cloudflare/clef", | |
| "base_revision": "2f3de3dd85f379784083b0814d997ab627200f0c", | |
| "architecture": "Qwen3_5ForConditionalGeneration", | |
| "backbone_dtype": "bfloat16", | |
| "joint_head_dtype": "float32", | |
| "joint_schema_model_sha256": "0e304cf7c6500e8bb59bef7e2afd2c6373f82596dfb3b57d1aa93c175e2dc3a3", | |
| "training": { | |
| "toolkit": "clef-finetune", | |
| "toolkit_version": "0.1.0", | |
| "seed": 0, | |
| "steps": 128, | |
| "example_exposures": 512, | |
| "lora_rank": 64, | |
| "lora_alpha": 128, | |
| "lora_dropout": 0.05, | |
| "gradient_accumulation": 4, | |
| "learning_rate": 0.0001, | |
| "head_learning_rate": 0.00005, | |
| "max_request_tokens": 1536 | |
| }, | |
| "dataset": { | |
| "source": "iitolstykh/LLMTrace_detection", | |
| "revision": "332053804f8798177ff2ecb1148e1839ed429ba3", | |
| "language": "en", | |
| "training_groups": 298, | |
| "training_rows": 1192, | |
| "labels": ["human", "ai-assisted", "mixed", "ai"], | |
| "label_status": "provisional", | |
| "source_rights_status": "not-independently-cleared" | |
| }, | |
| "gguf": { | |
| "path": "ClefSift-Q8_0.gguf", | |
| "quantization": "Q8_0", | |
| "bytes": 28732214816, | |
| "sha256": "1fbdedec7989395973e5364a47dcd4a01a5f419a425b26ba476faed5556cb7fa", | |
| "llama_cpp_commit": "a46709b683aba9274d8ab29f5b42f7e551d1dff6", | |
| "llama_cpp_build": 11450 | |
| }, | |
| "license": "apache-2.0" | |
| } | |