Text Generation
Transformers
Safetensors
English
French
Latin
mistral
conversational
text-generation-inference
Instructions to use FriendliAI/MonadGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FriendliAI/MonadGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FriendliAI/MonadGPT") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("FriendliAI/MonadGPT") model = AutoModelForCausalLM.from_pretrained("FriendliAI/MonadGPT", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FriendliAI/MonadGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FriendliAI/MonadGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FriendliAI/MonadGPT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FriendliAI/MonadGPT
- SGLang
How to use FriendliAI/MonadGPT 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 "FriendliAI/MonadGPT" \ --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": "FriendliAI/MonadGPT", "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 "FriendliAI/MonadGPT" \ --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": "FriendliAI/MonadGPT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use FriendliAI/MonadGPT with Docker Model Runner:
docker model run hf.co/FriendliAI/MonadGPT
| license: apache-2.0 | |
| language: | |
| - en | |
| - fr | |
| - la | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - conversational | |
| base_model: teknium/OpenHermes-2-Mistral-7B | |
| datasets: | |
| - Pclanglais/MonadGPT | |
| <!-- header start --> | |
| <p align="center"> | |
| <img src="https://huggingface.co/datasets/FriendliAI/documentation-images/resolve/main/model-card-assets/friendliai.png" width="100%" alt="FriendliAI Logo"> | |
| </p> | |
| <!-- header end --> | |
| # Pclanglais/MonadGPT | |
| * Model creator: [Pclanglais](https://huggingface.co/Pclanglais) | |
| * Original model: [MonadGPT](https://huggingface.co/Pclanglais/MonadGPT) | |
| ## Differences | |
| * Added tokenizer.json to the model, which was previously missing. | |
| ## License | |
| Refer to the license of the original model card. | |