Instructions to use JingyaoLi/ScienceLLaMA-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JingyaoLi/ScienceLLaMA-1b with Transformers:

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

pipe = pipeline("text-generation", model="JingyaoLi/ScienceLLaMA-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JingyaoLi/ScienceLLaMA-1b")
model = AutoModelForCausalLM.from_pretrained("JingyaoLi/ScienceLLaMA-1b", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JingyaoLi/ScienceLLaMA-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JingyaoLi/ScienceLLaMA-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JingyaoLi/ScienceLLaMA-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JingyaoLi/ScienceLLaMA-1b

SGLang

How to use JingyaoLi/ScienceLLaMA-1b 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 "JingyaoLi/ScienceLLaMA-1b" \
    --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": "JingyaoLi/ScienceLLaMA-1b",
		"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 "JingyaoLi/ScienceLLaMA-1b" \
        --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": "JingyaoLi/ScienceLLaMA-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JingyaoLi/ScienceLLaMA-1b with Docker Model Runner:
```
docker model run hf.co/JingyaoLi/ScienceLLaMA-1b
```

ScienceLLaMA-1b / README.md

JingyaoLi

Update README.md

c523fdc verified about 1 year ago

preview code

Raw

History Blame Contribute Delete

1.89 kB

metadata

library_name: transformers
license: other
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
  - llama-factory
  - full
  - generated_from_trainer
model-index:
  - name: ScienceLLaMA-1B
    results: []

ScienceLLaMA-3B

• 🤗 Data • 🤗 ScienceLLaMA-3B • 🤗 ScienceLLaMA-1B • 🐱 Code • 📃 Paper

This model is a fine-tuned with Logits-Based Finetuning on the JingyaoLi/Science-Logits-1.2M, which integrates the strengths of supervised learning and knowledge distillation by combining teacher logits with ground truth labels. This preserves both correctness and linguistic diversity.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Framework versions

Transformers 4.45.0
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.20.1