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")
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 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
```

Add pipeline tag and link to code

by nielsr HF Staff - opened Jun 12, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

Files changed (1) hide show

README.md +5 -4

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 library_name: transformers
 license: other
-base_model: meta-llama/Llama-3.2-1B-Instruct
 tags:
 - llama-factory
 - full
@@ -9,6 +9,7 @@ tags:
 model-index:
 - name: ScienceLLaMA-1B
   results: []
 ---
 # ScienceLLaMA-3B
@@ -16,8 +17,8 @@ model-index:
 • 🤗 <a href="https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M" target="_blank">Data </a>
 • 🤗 <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-3b" target="_blank">ScienceLLaMA-3B </a>
 • 🤗 <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-1b" target="_blank">ScienceLLaMA-1B </a>
-• 🐱 <a href="Logits-based Finetuning" target="_blank">Code</a>
-• 📃 Paper (TO be released) <br>
 </p>
 This model is a fine-tuned with **Logits-Based Finetuning** on the [JingyaoLi/Science-Logits-1.2M](https://huggingface.co/datasets/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.
@@ -56,4 +57,4 @@ The following hyperparameters were used during training:
 - Transformers 4.45.0
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
-- Tokenizers 0.20.1

 ---
+base_model: meta-llama/Llama-3.2-1B-Instruct
 library_name: transformers
 license: other
 tags:
 - llama-factory
 - full
 model-index:
 - name: ScienceLLaMA-1B
   results: []
+pipeline_tag: text-generation
 ---
 # ScienceLLaMA-3B
 • 🤗 <a href="https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M" target="_blank">Data </a>
 • 🤗 <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-3b" target="_blank">ScienceLLaMA-3B </a>
 • 🤗 <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-1b" target="_blank">ScienceLLaMA-1B </a>
+• 🐱 <a href="https://github.com/hiyouga/LLaMA-Factory" target="_blank">Code</a>
+• 📃 <a href="https://arxiv.org/abs/2505.24461" target="_blank">Paper</a> <br>
 </p>
 This model is a fine-tuned with **Logits-Based Finetuning** on the [JingyaoLi/Science-Logits-1.2M](https://huggingface.co/datasets/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.
 - Transformers 4.45.0
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
+- Tokenizers 0.20.1