Instructions to use KU-AGI/RetroReasoner-RoundTrip-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KU-AGI/RetroReasoner-RoundTrip-8B with Transformers:

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

pipe = pipeline("text-generation", model="KU-AGI/RetroReasoner-RoundTrip-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KU-AGI/RetroReasoner-RoundTrip-8B")
model = AutoModelForCausalLM.from_pretrained("KU-AGI/RetroReasoner-RoundTrip-8B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KU-AGI/RetroReasoner-RoundTrip-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KU-AGI/RetroReasoner-RoundTrip-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KU-AGI/RetroReasoner-RoundTrip-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KU-AGI/RetroReasoner-RoundTrip-8B

SGLang

How to use KU-AGI/RetroReasoner-RoundTrip-8B 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 "KU-AGI/RetroReasoner-RoundTrip-8B" \
    --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": "KU-AGI/RetroReasoner-RoundTrip-8B",
		"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 "KU-AGI/RetroReasoner-RoundTrip-8B" \
        --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": "KU-AGI/RetroReasoner-RoundTrip-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KU-AGI/RetroReasoner-RoundTrip-8B with Docker Model Runner:
```
docker model run hf.co/KU-AGI/RetroReasoner-RoundTrip-8B
```

RetroReasoner-RoundTrip-8B

Forward reaction prediction model (reactants → product) used for round-trip evaluation in the RetroReasoner project. Given the starting materials as SMILES, it predicts the resulting product. Fine-tuned from Qwen/Qwen3-8B.

Note this is not a retrosynthesis model — see KU-AGI/RetroReasoner-RL for that. The prompt below is the one it was trained and evaluated with; other prompt formats are out of distribution.

Prompt format

system: You are a chemist.
user:   {REACTANT_SMILES} Considering the given starting materials, what might be the resulting product in a chemical reaction?

Apply the Qwen3 chat template and generate a short completion (max_new_tokens=500); this model answers directly and is not used with long chain-of-thought.

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "KU-AGI/RetroReasoner-RoundTrip-8B"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")

reactants = "CC(=O)Oc1ccccc1C(=O)O.OCC"
messages = [
    {"role": "system", "content": "You are a chemist."},
    {"role": "user", "content": f"{reactants} Considering the given starting materials, what might be the resulting product in a chemical reaction?"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=500)
print(tokenizer.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Serving with vLLM:

vllm serve KU-AGI/RetroReasoner-RoundTrip-8B --max-model-len 40960

Model card with training details and results to follow.

Downloads last month: 16

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for KU-AGI/RetroReasoner-RoundTrip-8B

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

(2,336)

this model