Text Generation
Transformers
Safetensors
mistral
roleplay
creative-writing
Merge
mergekit
conversational
text-generation-inference
Instructions to use Delta-Vector/Archaeo-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Delta-Vector/Archaeo-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Delta-Vector/Archaeo-12B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Archaeo-12B") model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Archaeo-12B") 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
- vLLM
How to use Delta-Vector/Archaeo-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Delta-Vector/Archaeo-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Delta-Vector/Archaeo-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Delta-Vector/Archaeo-12B
- SGLang
How to use Delta-Vector/Archaeo-12B 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 "Delta-Vector/Archaeo-12B" \ --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": "Delta-Vector/Archaeo-12B", "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 "Delta-Vector/Archaeo-12B" \ --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": "Delta-Vector/Archaeo-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Delta-Vector/Archaeo-12B with Docker Model Runner:
docker model run hf.co/Delta-Vector/Archaeo-12B
metadata
tags:
- roleplay
- creative-writing
- merge
- mergekit
base_model:
- Delta-Vector/Francois-Huali-12B
- Delta-Vector/Rei-12B
pipeline_tag: text-generation
library_name: transformers
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'/||\' "Archaeopteryx"
A series of Merges made for Roleplaying & Creative Writing, This model uses Rei-12B and Francois-Huali-12B and Slerp to merge the 2 models.
ChatML formatting
"""<|im_start|>system
system prompt<|im_end|>
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""
MergeKit Configuration
models:
- model: Delta-Vector/Francois-Huali-12B
- model: Delta-Vector/Rei-12B
merge_method: slerp
base_model: Delta-Vector/Rei-12B
parameters:
t:
- value: 0.2
dtype: bfloat16
tokenizer_source: base
Quants:
Credits
Thank you to: Kubernetes-bad, LucyKnada, Intervitens & The rest of Anthracite