Instructions to use AI45Research/REX-Intern-S2-Bio-F3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AI45Research/REX-Intern-S2-Bio-F3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="AI45Research/REX-Intern-S2-Bio-F3")
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("AI45Research/REX-Intern-S2-Bio-F3")
model = AutoModelForMultimodalLM.from_pretrained("AI45Research/REX-Intern-S2-Bio-F3", 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 AI45Research/REX-Intern-S2-Bio-F3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AI45Research/REX-Intern-S2-Bio-F3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AI45Research/REX-Intern-S2-Bio-F3",
		"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/AI45Research/REX-Intern-S2-Bio-F3

SGLang

How to use AI45Research/REX-Intern-S2-Bio-F3 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 "AI45Research/REX-Intern-S2-Bio-F3" \
    --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": "AI45Research/REX-Intern-S2-Bio-F3",
		"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 "AI45Research/REX-Intern-S2-Bio-F3" \
        --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": "AI45Research/REX-Intern-S2-Bio-F3",
		"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 AI45Research/REX-Intern-S2-Bio-F3 with Docker Model Runner:
```
docker model run hf.co/AI45Research/REX-Intern-S2-Bio-F3
```

REX-Intern-S2-Bio-F3 / run.json

matrix72

Stage reviewed REX F3 checkpoint privately

8486e6e verified 13 days ago

Raw History Blame Contribute Delete

1.65 kB

{
  "step": "train_sft",
  "created_at": "2026-10-11T01:01:15+00:00",
  "git_commit": "8b5b96deec230e17c55784177f60235498b92bb2-dirty",
  "inputs": {},
  "input_records": {},
  "config": {
    "training_purpose": "benign_sft",
    "seed": 42,
    "max_tokens": 250000000,
    "tokens_per_step": 500000,
    "save_token_milestones": [
      250000000
    ],
    "pack_length": {
      "qwen3_5_moe": 31250,
      "gemma4": 15625
    },
    "optimizer": {
      "lr": 2e-06,
      "min_lr": 1e-07,
      "lr_decay_style": "cosine",
      "lr_warmup_fraction": 0.1,
      "weight_decay": 0.05,
      "clip_grad": 1.0,
      "muon_momentum": 0.95,
      "muon_use_nesterov": true,
      "muon_scale_mode": "spectral",
      "muon_extra_scale_factor": 0.2,
      "muon_scalar_optimizer": "adam"
    }
  },
  "args": {},
  "results": {
    "step": 500,
    "total_steps": 500,
    "nominal_tokens": 250000000,
    "consumed_tokens": 249586222,
    "nominal_tokens_per_step": 500000
  },
  "public_release": {
    "repo_id": "AI45Research/REX-Intern-S2-Bio-F3",
    "version": "v0.1.0",
    "node": "F3",
    "sft_nominal_tokens": 250000000,
    "record_is_redacted": true,
    "omitted": [
      "private_paths",
      "data_recipe",
      "nested_private_provenance",
      "training_logs"
    ],
    "source_record_step": "train_sft",
    "original_experts": 256,
    "expanded_experts": 264,
    "note": "Release metadata is a public projection; it is not a complete training record. The endpoint is selected by the private final index; missing source provenance is not reconstructed.",
    "code_revision": "b0178042de27ad2c0679a483da93bad6b2dbb76b"
  }
}

	{
	"step": "train_sft",
	"created_at": "2026-10-11T01:01:15+00:00",
	"git_commit": "8b5b96deec230e17c55784177f60235498b92bb2-dirty",
	"inputs": {},
	"input_records": {},
	"config": {
	"training_purpose": "benign_sft",
	"seed": 42,
	"max_tokens": 250000000,
	"tokens_per_step": 500000,
	"save_token_milestones": [
	250000000
	],
	"pack_length": {
	"qwen3_5_moe": 31250,
	"gemma4": 15625
	},
	"optimizer": {
	"lr": 2e-06,
	"min_lr": 1e-07,
	"lr_decay_style": "cosine",
	"lr_warmup_fraction": 0.1,
	"weight_decay": 0.05,
	"clip_grad": 1.0,
	"muon_momentum": 0.95,
	"muon_use_nesterov": true,
	"muon_scale_mode": "spectral",
	"muon_extra_scale_factor": 0.2,
	"muon_scalar_optimizer": "adam"
	}
	},
	"args": {},
	"results": {
	"step": 500,
	"total_steps": 500,
	"nominal_tokens": 250000000,
	"consumed_tokens": 249586222,
	"nominal_tokens_per_step": 500000
	},
	"public_release": {
	"repo_id": "AI45Research/REX-Intern-S2-Bio-F3",
	"version": "v0.1.0",
	"node": "F3",
	"sft_nominal_tokens": 250000000,
	"record_is_redacted": true,
	"omitted": [
	"private_paths",
	"data_recipe",
	"nested_private_provenance",
	"training_logs"
	],
	"source_record_step": "train_sft",
	"original_experts": 256,
	"expanded_experts": 264,
	"note": "Release metadata is a public projection; it is not a complete training record. The endpoint is selected by the private final index; missing source provenance is not reconstructed.",
	"code_revision": "b0178042de27ad2c0679a483da93bad6b2dbb76b"
	}
	}