Instructions to use Salesforce/E1-Math-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Salesforce/E1-Math-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="Salesforce/E1-Math-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/E1-Math-1.5B")
model = AutoModelForCausalLM.from_pretrained("Salesforce/E1-Math-1.5B", 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 Salesforce/E1-Math-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Salesforce/E1-Math-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Salesforce/E1-Math-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Salesforce/E1-Math-1.5B

SGLang

How to use Salesforce/E1-Math-1.5B 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 "Salesforce/E1-Math-1.5B" \
    --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": "Salesforce/E1-Math-1.5B",
		"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 "Salesforce/E1-Math-1.5B" \
        --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": "Salesforce/E1-Math-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Salesforce/E1-Math-1.5B with Docker Model Runner:
```
docker model run hf.co/Salesforce/E1-Math-1.5B
```

yuhuixu commited on May 25, 2025

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@@ -10,6 +10,13 @@ license: cc-by-nc-4.0
 ## Introduction
 E1-Math-1.5B is a language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B. It is trained for Elastic Reasoning by budget-constrained rollout strategy, integrated into GRPO, which teaches the model to reason adaptively when the thinking process is cut short and generalizes effectively to unseen budget constraints without additional training.
 ## Usage
 For detailed usage, please refer to [repo](https://github.com/SalesforceAIResearch/Elastic-Reasoning).

 ## Introduction
 E1-Math-1.5B is a language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B. It is trained for Elastic Reasoning by budget-constrained rollout strategy, integrated into GRPO, which teaches the model to reason adaptively when the thinking process is cut short and generalizes effectively to unseen budget constraints without additional training.
+## Performance
+| Model | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) | Tokens | Acc (%) |
+|---------------|--------------|---------------|--------------|---------------|--------------|---------------|--------------|---------------|--------------|---------------|
+| DeepSscaleR-1.5B | 10050 | 41.0|  1488 | 5.2 | 1904 | 9.6 | 2809 | 15.8 | 3700 | 22.7 |
+| E1-Math-1.5B | 6825 | 35.0 | 1340   | 13.5   | 1799   | 17.5   | 2650   | 24.8   | 3377   | 27.9   |
 ## Usage
 For detailed usage, please refer to [repo](https://github.com/SalesforceAIResearch/Elastic-Reasoning).