Instructions to use nicholasKluge/TeenyTinyLlama-460m-Chat-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nicholasKluge/TeenyTinyLlama-460m-Chat-awq with Transformers:

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

pipe = pipeline("text-generation", model="nicholasKluge/TeenyTinyLlama-460m-Chat-awq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/TeenyTinyLlama-460m-Chat-awq")
model = AutoModelForMultimodalLM.from_pretrained("nicholasKluge/TeenyTinyLlama-460m-Chat-awq")
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 nicholasKluge/TeenyTinyLlama-460m-Chat-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nicholasKluge/TeenyTinyLlama-460m-Chat-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nicholasKluge/TeenyTinyLlama-460m-Chat-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nicholasKluge/TeenyTinyLlama-460m-Chat-awq

SGLang

How to use nicholasKluge/TeenyTinyLlama-460m-Chat-awq 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 "nicholasKluge/TeenyTinyLlama-460m-Chat-awq" \
    --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": "nicholasKluge/TeenyTinyLlama-460m-Chat-awq",
		"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 "nicholasKluge/TeenyTinyLlama-460m-Chat-awq" \
        --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": "nicholasKluge/TeenyTinyLlama-460m-Chat-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nicholasKluge/TeenyTinyLlama-460m-Chat-awq with Docker Model Runner:
```
docker model run hf.co/nicholasKluge/TeenyTinyLlama-460m-Chat-awq
```

TeenyTinyLlama-460m-Chat-awq / emissions.csv

nicholasKluge

Upload emissions.csv

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	timestamp,project_name,run_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
	2024-01-18T02:02:26,TeenyTinyLlama,39b206a4-e86a-41fa-a5f9-44deef6d7e34,49516.08560705185,2.5308193028462096,5.1111053545916445e-05,42.5,279.50887502416805,31.303916931152344,0.5845638546203586,4.576422722524138,0.43049185652675864,5.591478433671226,United States,USA,iowa,,,Linux-6.1.58+-x86_64-with-glibc2.35,3.10.12,2.3.2,12,Intel(R) Xeon(R) CPU @ 2.20GHz,1,1 x NVIDIA A100-SXM4-40GB,-95.8517,41.2591,83.47711181640625,machine,N,1.0