Instructions to use sbintuitions/tiny-lm-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sbintuitions/tiny-lm-chat with Transformers:

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

pipe = pipeline("text-generation", model="sbintuitions/tiny-lm-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm-chat")
model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm-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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sbintuitions/tiny-lm-chat with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sbintuitions/tiny-lm-chat

SGLang

How to use sbintuitions/tiny-lm-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 "sbintuitions/tiny-lm-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": "sbintuitions/tiny-lm-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 "sbintuitions/tiny-lm-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": "sbintuitions/tiny-lm-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sbintuitions/tiny-lm-chat with Docker Model Runner:
```
docker model run hf.co/sbintuitions/tiny-lm-chat
```

tiny-lm-chat / README.md

ryo0634

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metadata

license: mit
datasets:
  - wikipedia
  - llm-jp/oasst1-21k-ja
  - llm-jp/oasst1-21k-en
language:
  - ja
  - en

tiny-lm

This repository provides a tiny 16M parameters language model for debugging and testing purposes. This is created by tuning sbintuitions/tiny-lm with oasset1 datasets in Japanese and English.

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm-chat", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm-chat", use_fast=False)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = tokenizer.apply_chat_template([{"role": "user", "content": "Hello!"}], add_generation_prompt=True, tokenize=False)
print(generator(prompt, max_length=30, do_sample=True, top_k=100))

Model architecture

A 4-layer, 512-hidden-size transformer-based language model.

Training

The model was first pre-trained on English Wikipedia and Japanese Wikipedia to optimize a traditional language modelling objective for 25B tokens. And then it was fine-tuned on oasst1 datasets in Japanese and English for 15 epochs.

License

MIT License