Instructions to use GothamLegend/SoftwareGym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GothamLegend/SoftwareGym with Transformers:

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

pipe = pipeline("image-text-to-text", model="GothamLegend/SoftwareGym")
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("GothamLegend/SoftwareGym")
model = AutoModelForMultimodalLM.from_pretrained("GothamLegend/SoftwareGym", 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 GothamLegend/SoftwareGym with vLLM:

Install from pip and serve model

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

SGLang

How to use GothamLegend/SoftwareGym 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 "GothamLegend/SoftwareGym" \
    --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": "GothamLegend/SoftwareGym",
		"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 "GothamLegend/SoftwareGym" \
        --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": "GothamLegend/SoftwareGym",
		"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 GothamLegend/SoftwareGym with Docker Model Runner:
```
docker model run hf.co/GothamLegend/SoftwareGym
```

SoftwareGym / README.md

GothamLegend

Upload SoftwareGym Qwen3.5-9B checkpoint step 1545

48537b6 verified 7 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-9B
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- softwaregym
	- computer-use
	- gui-agent
	- multimodal
	- fine-tuned
	---

	# SoftwareGym — Qwen3.5-9B, Step 1545

	This repository contains the full SoftwareGym fine-tuned checkpoint at training step 1545, based on Qwen/Qwen3.5-9B.

	## Model size

	\| Property \| Value \|
	\| --- \| --- \|
	\| Architecture \| `Qwen3_5ForConditionalGeneration` \|
	\| Parameters \| 9,409,813,744 (approximately 9.41B) \|
	\| Weight precision \| BF16 (`bfloat16`) \|
	\| Weight files \| 4 Safetensors shards and their index \|
	\| Total weight size \| 18,819,721,120 bytes (approximately 18.82 GB / 17.53 GiB) \|
	\| Checkpoint \| Step 1545 \|

	Tokenizer, processor, generation configuration, and the chat template are included with the weights.

	## Deployment parameters

	The checkpoint's reference deployment uses MS-Swift with the Transformers inference backend. Its recorded software environment is:

	- Python 3.10.19
	- PyTorch 2.8.0 with CUDA 12.8
	- Transformers 5.10.2
	- MS-Swift 4.4.0.dev0
	- FlashAttention 2.8.3
	- Accelerate 1.10.1

	These are the versions recorded for the original serving environment, rather than a separately tested minimum-version requirement.

	\| Parameter \| Reference value \|
	\| --- \| --- \|
	\| Inference backend \| `transformers` \|
	\| Data type \| `bfloat16` \|
	\| Attention implementation \| `flash_attn` \|
	\| Device map \| `auto` \|
	\| Maximum sequence length \| 65,536 tokens \|
	\| Maximum new tokens \| 8,192 \|
	\| Maximum batch size per replica \| 1 \|
	\| Sampling temperature in the reference request \| 1.0 \|
	\| Top-p in the reference request \| 0.95 \|

	After downloading this repository to a local directory, start one replica with the reference serving settings:

	```bash
	CUDA_VISIBLE_DEVICES=0 swift deploy \
	--model /path/to/SoftwareGym \
	--infer_backend transformers \
	--served_model_name softwaregym-step1545 \
	--host 127.0.0.1 --port 8450 \
	--torch_dtype bfloat16 \
	--attn_impl flash_attn \
	--device_map auto \
	--max_length 65536 \
	--max_new_tokens 8192 \
	--max_batch_size 1 \
	--no_verbose
	```

	The original deployment used 8 NVIDIA A800-SXM4-80GB GPUs, with one independent replica per GPU, backend ports 8450–8457, and an OpenAI-compatible round-robin gateway on port 8441. This is an eight-replica deployment, rather than eight-way tensor parallelism. The command above starts one replica; the gateway is external to the model files in this repository.

	For GUI-agent use, provide the task, screenshot, and interaction history through the included chat template. The reference response format contains `<think>`, `## Action:`, and `## Code:` sections. Pointer coordinates are integers in the 0–999 range and must be mapped to screen pixels by the GUI executor.

	## License

	This repository uses Apache-2.0, consistent with the repository's existing license metadata and the Qwen3.5-9B base model. See the included `LICENSE` for the license text.