Instructions to use SL-AI/GRaPE-2.5-Quasar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SL-AI/GRaPE-2.5-Quasar with Transformers:

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

pipe = pipeline("image-text-to-text", model="SL-AI/GRaPE-2.5-Quasar")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("SL-AI/GRaPE-2.5-Quasar")
model = AutoModelForMultimodalLM.from_pretrained("SL-AI/GRaPE-2.5-Quasar", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SL-AI/GRaPE-2.5-Quasar with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SL-AI/GRaPE-2.5-Quasar"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SL-AI/GRaPE-2.5-Quasar",
		"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/SL-AI/GRaPE-2.5-Quasar

SGLang

How to use SL-AI/GRaPE-2.5-Quasar 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 "SL-AI/GRaPE-2.5-Quasar" \
    --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": "SL-AI/GRaPE-2.5-Quasar",
		"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 "SL-AI/GRaPE-2.5-Quasar" \
        --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": "SL-AI/GRaPE-2.5-Quasar",
		"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 SL-AI/GRaPE-2.5-Quasar with Docker Model Runner:
```
docker model run hf.co/SL-AI/GRaPE-2.5-Quasar
```

Sweaterdog commited on 16 days ago

Commit

a51542f

verified ·

1 Parent(s): df46b4c

Update config.json

Browse files

Files changed (1) hide show

config.json +1 -43

config.json CHANGED Viewed

@@ -1,50 +1,8 @@
 {
-  "_name_or_path": "GRaPE 2.5 Quasar step 2900",
   "architectures": [
     "Qwen3_5ForConditionalGeneration"
   ],
-  "grape_training": {
-    "assistant_only_loss": true,
-    "checkpoint_state": {
-      "best_eval_nll": 0.9964489920094617,
-      "best_step": 2900,
-      "dataset_sha256": "d308ed07fe2330bd6f0a42be8d9a8c079e9e841461116fd79d7e850c0520e0b4",
-      "evals_without_improvement": 0,
-      "lora_alpha": 48,
-      "lora_module_hash": "43a5d0a3bb52b9ca",
-      "lora_rank": 24,
-      "lora_rank_migration": {
-        "expanded": true,
-        "expanded_tensor_count": 500,
-        "function_preserving_scale": 2.0,
-        "optimizer": {
-          "lora_parameter_count": 500,
-          "preserved_state_count": 1,
-          "removed_state_count": 500
-        },
-        "source_alpha": 16,
-        "source_rank": 8,
-        "target_alpha": 48,
-        "target_rank": 24,
-        "tensor_count": 500
-      },
-      "loss_objective": "grape25-structure-v2",
-      "module_hash": "4ff0564627e1eb9c",
-      "started_at": "2026-08-27T16:08:54-0700",
-      "step": 2900,
-      "tokens": 190054400,
-      "trainable_tokens": 123546691
-    },
-    "checkpoint_step": 2900,
-    "context_length": 65536,
-    "crt_modules": 1,
-    "inherited_lora_checkpoint": null,
-    "inherited_lora_scale": null,
-    "inherited_lora_subtracted": 0,
-    "lora_modules": 250,
-    "lora_scale": 2.0,
-    "model_authored_think": true
-  },
   "image_token_id": 248056,
   "language_model_only": false,
   "model_type": "qwen3_5",

 {
+  "_name_or_path": "GRaPE 2.5 Quasar",
   "architectures": [
     "Qwen3_5ForConditionalGeneration"
   ],
   "image_token_id": 248056,
   "language_model_only": false,
   "model_type": "qwen3_5",