Instructions to use ahxt/LiteLlama-460M-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ahxt/LiteLlama-460M-1T with Transformers:

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

pipe = pipeline("text-generation", model="ahxt/LiteLlama-460M-1T")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ahxt/LiteLlama-460M-1T")
model = AutoModelForCausalLM.from_pretrained("ahxt/LiteLlama-460M-1T")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ahxt/LiteLlama-460M-1T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ahxt/LiteLlama-460M-1T"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ahxt/LiteLlama-460M-1T",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ahxt/LiteLlama-460M-1T

SGLang

How to use ahxt/LiteLlama-460M-1T 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 "ahxt/LiteLlama-460M-1T" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ahxt/LiteLlama-460M-1T",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ahxt/LiteLlama-460M-1T" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ahxt/LiteLlama-460M-1T",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ahxt/LiteLlama-460M-1T with Docker Model Runner:
```
docker model run hf.co/ahxt/LiteLlama-460M-1T
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LiteLlama: Reduced-Scale Llama

We present an open-source reproduction of Meta AI's LLaMa 2. However, with significantly reduced model sizes, LiteLlama-460M-1T has 460M parameters trained with 1T tokens.

Dataset and Tokenization

We train our models on part of RedPajama dataset. We use the GPT2Tokenizer to tokenize the text.

Training Details

The model was trained with ~1T tokens (0.98T). num of tokens = stepslengthbatch_size=4996791024192=98240888832≈0.98T.

The training curve is at this WandB project.

Using with HuggingFace Transformers

The experimental checkpoints can be directly loaded by Transformers library. The following code snippet shows how to load the our experimental model and generate text with it.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = 'ahxt/LiteLlama-460M-1T'

model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()

prompt = 'Q: What is the largest bird?\nA:'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
tokens = model.generate(input_ids, max_length=20)
print( tokenizer.decode(tokens[0].tolist(), skip_special_tokens=True) )
# Q: What is the largest bird?\nA: The largest bird is a black-headed gull.

Evaluation

We evaluate our models on the MMLU task.

Models	#parameters	zero-shot	5-shot
llama	7B	28.46	35.05
openllama	3B	24.90	26.71
TinyLlama-1.1B-step-50K-105b	1.1B	19.00	26.53
LiteLlama-460M-1T	0.46B	21.13	26.39

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	26.65
ARC (25-shot)	24.91
HellaSwag (10-shot)	38.47
MMLU (5-shot)	26.17
TruthfulQA (0-shot)	41.59
Winogrande (5-shot)	49.88
GSM8K (5-shot)	0.0
DROP (3-shot)	5.51

Contact

This model was developed by Xiaotian Han from Texas A&M University at the DATA Lab under the supervision of Prof. Xia "Ben" Hu, and the model is released under MIT License.

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