Instructions to use waddie/mini-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use waddie/mini-2.0 with Transformers:

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

pipe = pipeline("text-generation", model="waddie/mini-2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("waddie/mini-2.0")
model = AutoModelForCausalLM.from_pretrained("waddie/mini-2.0")
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

vLLM

How to use waddie/mini-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "waddie/mini-2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "waddie/mini-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/waddie/mini-2.0

SGLang

How to use waddie/mini-2.0 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 "waddie/mini-2.0" \
    --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": "waddie/mini-2.0",
		"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 "waddie/mini-2.0" \
        --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": "waddie/mini-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use waddie/mini-2.0 with Docker Model Runner:
```
docker model run hf.co/waddie/mini-2.0
```

CloudWaddie Mini 2.0

This model is a fine-tuned version of Qwen2.5-7B-Instruct designed to mimic the specific conversational rhythm, slang, and technical jargon of a human,

Model Details

Dataset used

This model used 10k discord conversations.

Model Description

Unlike standard AI assistants that are helpful and formal, this model adopts a "random guy" persona. It was trained on curated conversation pairs from an AI Leaks community to capture a casual, lowercase-heavy, and slightly secretive "insider" vibe.

Developed by: Edward Fazackerley
Language(s): English (Informal/Slang)
Finetuned from model: Qwen/Qwen2.5-7B-Instruct
Persona: Casual, technical, secretive, lowercase-only.

Uses

Direct Use

This model is intended for Discord bots or roleplay scenarios where a "human-like" interaction is preferred over a robotic assistant.

Prompting Strategy

To get the best "human" feel, use all lowercase and skip formal punctuation.

Recommended Format (ChatML):

<|im_start|>user
yo did you see the new internal model?<|im_end|>
<|im_start|>assistant

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for waddie/mini-2.0

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(3326)

this model