Instructions to use anaghaa/Resonance-27B-S2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anaghaa/Resonance-27B-S2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="anaghaa/Resonance-27B-S2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("anaghaa/Resonance-27B-S2")
model = AutoModelForMultimodalLM.from_pretrained("anaghaa/Resonance-27B-S2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anaghaa/Resonance-27B-S2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anaghaa/Resonance-27B-S2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anaghaa/Resonance-27B-S2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/anaghaa/Resonance-27B-S2

SGLang

How to use anaghaa/Resonance-27B-S2 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 "anaghaa/Resonance-27B-S2" \
    --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": "anaghaa/Resonance-27B-S2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "anaghaa/Resonance-27B-S2" \
        --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": "anaghaa/Resonance-27B-S2",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use anaghaa/Resonance-27B-S2 with Docker Model Runner:
```
docker model run hf.co/anaghaa/Resonance-27B-S2
```

Resonance-27B-S2

S2: rejection-sampling SFT from S1. Part of Resonance, from the preprint Distillation, Self-Training, and the Fragility of Learned Chemical Reasoning (Aneesh, Ibarraran, Hu, Liu, Rotskoff, 2026).

Given the SMILES of a starting material and reagents, the model reasons in a <think> block and returns the predicted major product as SMILES between <answer> and </answer>.

Base model: Qwen/Qwen3.8-27B
Training: LoRA r128 merged into S1; 1 epoch (3,086 steps) on 98,731 difficulty-mixed examples
USPTO-50k test, Top-1 exact match (N = 5,000): 79.3%
LoRA adapter: adapter/ (apply to anaghaa/Resonance-27B-S1)
Code: https://github.com/rotskoff-group/resonance-rxn-reasoning
Data (teacher traces, training sets, every model's test outputs): https://huggingface.co/datasets/anaghaa/resonance

Prompt

The evaluation prompt used in the paper (the long system prompt; every 27B evaluation used it):

SYSTEM: You are an expert organic chemist ... (the long system prompt, with four reactivity principles)

USER: Here are the SMILES of the starting material and reagent(s).
Starting material: <SMILES>
Reagents: <SMILES, comma-separated, or "No reagents">

Predict the single most likely primary product of the chemical reaction in SMILES format. ...
Return the answer between <answer> and </answer> tags.

The full user template is ForwardRxnPrompt in the code repository (src/resonance/data/prompts/benchmark/forward_rxn.py) and, for 27B, src/resonance/configs/paper/qwen3_8_27b/prompts.json. Decoding: thinking enabled, temperature 0.6, top-p 0.95, top-k 20, up to 16,384 new tokens. The prediction is the last <answer> block, compared to the recorded product after RDKit canonicalisation.

from vllm import LLM, SamplingParams
llm = LLM("anaghaa/Resonance-27B-S2", tensor_parallel_size=2, limit_mm_per_prompt={"image": 0, "video": 0})
params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, max_tokens=16384)
out = llm.chat([[{"role": "system", "content": SYSTEM}, {"role": "user", "content": USER}]], params)

Citation

@misc{aneesh2026resonance,
  title  = {Distillation, Self-Training, and the Fragility of Learned Chemical Reasoning},
  author = {Aneesh, Anagha and Ibarraran, Sebastian and Hu, Frank and Liu, Jason and Rotskoff, Grant},
  year   = {2026},
  note   = {Preprint}
}