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

Libraries

How to use TinyLlama/TinyLlama-1.1B-Chat-v1.0 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
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 TinyLlama/TinyLlama-1.1B-Chat-v1.0 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0

SGLang

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

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

Dataset for DPO, with a Template?

#17

by ewqr2130 - opened Jan 11, 2024

Discussion

ewqr2130

Jan 11, 2024

•

edited Jan 11, 2024

Hello Team

Thanks very much for the model, it is awesome!
Quick question, for your DPO, do you still have to follow that template from your example?
"""""""
<|system|> You are a chatbot who can help code!</s> <|user|> Write me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.</s> <|assistant|>
""""""""

Or do you just use the raw training data from openbmb/UltraFeedback directly (i..e, no need to wrap them onto template)?

ewqr2130 changed discussion title from Dataset for DPO to Dataset for DPO, with a Template? Jan 11, 2024

SwatiM

Jan 22, 2024

Have not tried it personally but on a general note wrapping is always beneficial in sft.
For DPO chosen and rejected template should be used "https://huggingface.co/blog/dpo-trl".

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