Instructions to use yongchao98/R1-Code-Interpreter-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yongchao98/R1-Code-Interpreter-14B with Transformers:

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

pipe = pipeline("text-generation", model="yongchao98/R1-Code-Interpreter-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yongchao98/R1-Code-Interpreter-14B")
model = AutoModelForCausalLM.from_pretrained("yongchao98/R1-Code-Interpreter-14B", 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 yongchao98/R1-Code-Interpreter-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yongchao98/R1-Code-Interpreter-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yongchao98/R1-Code-Interpreter-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yongchao98/R1-Code-Interpreter-14B

SGLang

How to use yongchao98/R1-Code-Interpreter-14B 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 "yongchao98/R1-Code-Interpreter-14B" \
    --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": "yongchao98/R1-Code-Interpreter-14B",
		"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 "yongchao98/R1-Code-Interpreter-14B" \
        --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": "yongchao98/R1-Code-Interpreter-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yongchao98/R1-Code-Interpreter-14B with Docker Model Runner:
```
docker model run hf.co/yongchao98/R1-Code-Interpreter-14B
```

Improve model card: add library name and pipeline tag, link to code

by nielsr HF Staff - opened Jun 4, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+18

-3

Files changed (1) hide show

README.md +18 -3

README.md CHANGED Viewed

@@ -1,3 +1,18 @@
----
-license: mit
----

+---
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+---
+# R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised and Reinforcement Learning
+The model was presented in the paper [R1-Code-Interpreter: Training LLMs to Reason with Code via Supervised and Reinforcement Learning](https://huggingface.co/papers/2505.21668).
+Our code is based on [Llama-factory](https://github.com/hiyouga/LLaMA-Factory)/[VeRL](https://github.com/volcengine/verl)/[Search-R1](https://github.com/PeterGriffinJin/Search-R1?tab=readme-ov-file) for the SFT and RL training and [SymBench](https://github.com/yongchao98/CodeSteer-v1.0/tree/main)/[BIG-Bench-Hard](https://github.com/yongchao98/R1-Code-Interpreter/tree/main)/[reasoning-gym](https://github.com/open-thought/reasoning-gym) for datasets/benchmarks of reasoning/planning tasks.
+## 📝 Introduction
+R1-Code-Interpreter is the first framework to train LLMs for step-by-step code reasoning using multi-turn supervised fine-tuning and reinforcement learning. By curating 144 diverse reasoning and planning tasks, we enable Qwen-2.5 models (3B/7B/14B) to autonomously decide when and how to invoke code. Our best model, R1-CI-14B, outperforms GPT-4o (text-only) and approaches GPT-4o with Code Interpreter, showing emergent self-checking behavior via code generation.
+[Github repository](https://github.com/yongchao98/R1-Code-Interpreter)
+Project page: https://huggingface.co/yongchao98