Text Generation
Transformers
PyTorch
Safetensors
English
mistral
instruct
finetune
chatml
axolotl
roleplay
conversational
text-generation-inference
Instructions to use Gryphe/Pantheon-RP-1.5-12b-Nemo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gryphe/Pantheon-RP-1.5-12b-Nemo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Gryphe/Pantheon-RP-1.5-12b-Nemo") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Gryphe/Pantheon-RP-1.5-12b-Nemo") model = AutoModelForCausalLM.from_pretrained("Gryphe/Pantheon-RP-1.5-12b-Nemo", 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Gryphe/Pantheon-RP-1.5-12b-Nemo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Gryphe/Pantheon-RP-1.5-12b-Nemo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Gryphe/Pantheon-RP-1.5-12b-Nemo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Gryphe/Pantheon-RP-1.5-12b-Nemo
- SGLang
How to use Gryphe/Pantheon-RP-1.5-12b-Nemo 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 "Gryphe/Pantheon-RP-1.5-12b-Nemo" \ --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": "Gryphe/Pantheon-RP-1.5-12b-Nemo", "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 "Gryphe/Pantheon-RP-1.5-12b-Nemo" \ --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": "Gryphe/Pantheon-RP-1.5-12b-Nemo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Gryphe/Pantheon-RP-1.5-12b-Nemo with Docker Model Runner:
docker model run hf.co/Gryphe/Pantheon-RP-1.5-12b-Nemo

- Xet hash:
- 879f8988c44356275e99c88d91084183a013144f26358bd6ee5a3b2d61baaaca
- Size of remote file:
- 1.05 MB
- SHA256:
- d7246d5f8af7619a81d407b81664f993ad40e8ac91a41e0e843db163ccd90fc6
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