Instructions to use LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit with Transformers:

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

pipe = pipeline("text-generation", model="LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit")
model = AutoModelForMultimodalLM.from_pretrained("LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit")
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 LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit

SGLang

How to use LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit 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 "LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit" \
    --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": "LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit",
		"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 "LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit" \
        --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": "LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit with Docker Model Runner:
```
docker model run hf.co/LnL-AI/TinyLlama-1.1B-Chat-v1.0-GPTQ-4bit
```

This is TinyLlama/TinyLlama-1.1B-Chat-v1.0 quantized with AutoGPTQ in GPTQ 4-bit format.

Quantize config:

{
  "bits": 4,
  "group_size": 128,
  "damp_percent": 0.01,
  "desc_act": false,
  "static_groups": false,
  "sym": true,
  "true_sequential": true,
  "model_name_or_path": null,
  "model_file_base_name": null,
  "quant_method": "gptq",
  "checkpoint_format": "gptq"
}

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