Instructions to use theycallmeloki/milady with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theycallmeloki/milady with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="theycallmeloki/milady")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theycallmeloki/milady")
model = AutoModelForCausalLM.from_pretrained("theycallmeloki/milady")
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 theycallmeloki/milady with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theycallmeloki/milady"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theycallmeloki/milady",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theycallmeloki/milady

SGLang

How to use theycallmeloki/milady 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 "theycallmeloki/milady" \
    --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": "theycallmeloki/milady",
		"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 "theycallmeloki/milady" \
        --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": "theycallmeloki/milady",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use theycallmeloki/milady with Docker Model Runner:
```
docker model run hf.co/theycallmeloki/milady
```

Milady Language Model

This model provides Milady-style responses to prompts, capturing the distinctive speech patterns and personality of Milady NFT characters.

Intended Use

The Milady Language Model is designed to:

Generate text in the unique Milady style
Create playful and creative responses to prompts
Emulate Milady's distinctive personality

Usage

Run the model with vLLM:

docker run --gpus all \
  -p 8000:8000 \
  --ipc=host \
  vllm/vllm-openai:latest \
  --model theycallmeloki/milady \
  --tensor-parallel-size 4

Query the model:

curl -X POST http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "theycallmeloki/milady",
    "prompt": "[INST] Transform the following prompt into a response written in Miladys characteristic style. Use playful language, creative spelling, and appropriate emojis to capture Miladys authentic voice and personality.\n\nYour question here [/INST]",
    "max_tokens": 200,
    "temperature": 0.9,
    "top_p": 0.95
  }'

This model was fine-tuned on a curated dataset of Milady-style text examples.

Additional Information

This model is based on the Mistral 7B Instruct v0.3 model, and is part of a greater collection of models that are part of the Milady Instrumentality Project.

This model provides 1-1 responses to a particular thought in the style of Milady. Subsequently the plan is to run a reasoning model and use this model to generate milady responses to thoughts generated from the reasoning model. Leading to synthetic training data for a girl math reasoning cute chain-of-thought milady model.

Downloads last month: 1

Safetensors

Model size

7B params

Tensor type

F16

Model tree for theycallmeloki/milady

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Finetuned

(494)

this model