Instructions to use waddie/mini-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use waddie/mini-1.0 with Transformers:

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

pipe = pipeline("text-generation", model="waddie/mini-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("waddie/mini-1.0")
model = AutoModelForCausalLM.from_pretrained("waddie/mini-1.0")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use waddie/mini-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "waddie/mini-1.0"
# 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-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/waddie/mini-1.0

SGLang

How to use waddie/mini-1.0 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-1.0" \
    --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-1.0",
		"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-1.0" \
        --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-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use waddie/mini-1.0 with Docker Model Runner:
```
docker model run hf.co/waddie/mini-1.0
```

waddie commited on Apr 27

Commit

2999cfb

verified ·

1 Parent(s): 75b004c

Update README.md

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Files changed (1) hide show

README.md +2 -15

README.md CHANGED Viewed

@@ -1,7 +1,6 @@
 ---
 library_name: transformers
 tags:
-- discord
 - human-style
 - conversational
 - qwen
@@ -14,7 +13,7 @@ license: apache-2.0
 # CloudWaddie Mini 1.0
-This model is a fine-tuned version of `Qwen2.5-0.5B-Instruct` designed to mimic the specific conversational rhythm, slang, and technical jargon of an AI-centric Discord community.
 ## Model Details
@@ -61,16 +60,4 @@ outputs = model.generate(
     eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>")
 )
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-## Training Details
-### Training Data
-Trained on 789 conversation pairs extracted from AI-related Discord channels, focusing on topics like reverse engineering, internal Google/Anthropic models, and general community banter.
-### Training Procedure
-- **Method:** QLoRA (4-bit)
-- **Hardware:** NVIDIA T4 GPU
-- **Epochs:** 2
-- **Learning Rate:** 5e-5
-- **Batch Size:** 1 (with 4 Gradient Accumulation Steps)

 ---
 library_name: transformers
 tags:
 - human-style
 - conversational
 - qwen
 # CloudWaddie Mini 1.0
+This model is a fine-tuned version of `Qwen2.5-0.5B-Instruct` designed to mimic the specific conversational rhythm, slang, and technical jargon of a human,
 ## Model Details
     eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>")
 )
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```