Instructions to use ProCreations/Booper-Big-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ProCreations/Booper-Big-Chat with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ProCreations/Booper-Big-Chat")
model = AutoModelForCausalLM.from_pretrained("ProCreations/Booper-Big-Chat", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ProCreations/Booper-Big-Chat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ProCreations/Booper-Big-Chat

SGLang

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

Docker Model Runner
How to use ProCreations/Booper-Big-Chat with Docker Model Runner:
```
docker model run hf.co/ProCreations/Booper-Big-Chat
```

Booper-Big-Chat

Booper-Big-Chat is Booper-Big supervised-fine-tuned on mookiezi/Discord-Dialogues with assistant-only loss. Its final 25,023,805 assistant tokens used symmetric per-output-channel INT8 fake-quantization-aware training. This repository keeps the learned values in broadly compatible BF16; the separate ProCreations/Booper-Big-Chat-INT8 repository stores the weights as real INT8 tensors.

Property	Value
Total / active parameters	149,602,432 / 50,512,000
Context window	4,096
Assistant SFT tokens	100,023,805
QAT scheme	symmetric per-output-channel INT8 weights
Final SFT loss	2.4934
Held-out Discord loss	2.2578

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ProCreations/Booper-Big-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
messages = [{"role": "user", "content": "hey, what's up?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
output = model.generate(inputs, max_new_tokens=80, temperature=0.8, do_sample=True)
print(tokenizer.decode(output[0, inputs.shape[1]:], skip_special_tokens=True))

Validation sample for hey, what's up?: I'm tired, and is it just me or am I okay? How tired?

The Discord corpus is informal internet dialogue. The model can reproduce profanity, bias, personal data patterns, and unreliable claims. It is not suitable for high-stakes use.

Downloads last month: 284

Safetensors

Model size

0.2B params

Tensor type

BF16

Model tree for ProCreations/Booper-Big-Chat

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ProCreations/Booper-Big

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Datasets used to train ProCreations/Booper-Big-Chat