Instructions to use aisquared/bolt-instruct-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aisquared/bolt-instruct-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="aisquared/bolt-instruct-v2")
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("aisquared/bolt-instruct-v2")
model = AutoModelForMultimodalLM.from_pretrained("aisquared/bolt-instruct-v2", 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 aisquared/bolt-instruct-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aisquared/bolt-instruct-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisquared/bolt-instruct-v2",
		"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/aisquared/bolt-instruct-v2

SGLang

How to use aisquared/bolt-instruct-v2 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 "aisquared/bolt-instruct-v2" \
    --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": "aisquared/bolt-instruct-v2",
		"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 "aisquared/bolt-instruct-v2" \
        --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": "aisquared/bolt-instruct-v2",
		"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 aisquared/bolt-instruct-v2 with Docker Model Runner:
```
docker model run hf.co/aisquared/bolt-instruct-v2
```

PrajwalN-AIS commited on about 1 month ago

Commit

100b2ce

verified ·

1 Parent(s): c5e5674

Upload config.json with huggingface_hub

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Files changed (1) hide show

config.json +2 -4

config.json CHANGED Viewed

@@ -12,7 +12,6 @@
     "attention_bias": false,
     "attention_dropout": 0.0,
     "bos_token_id": 200000,
-    "dtype": "bfloat16",
     "eos_token_id": 200001,
     "final_logit_softcapping": 20.0,
     "head_dim": 128,
@@ -147,10 +146,9 @@
     "use_cache": true,
     "vocab_size": 202048
   },
-  "transformers_version": "5.17.0",
   "video_token_id": 200091,
   "vision_config": {
-    "dtype": "bfloat16",
     "hidden_act": "gelu",
     "hidden_size": 1536,
     "intermediate_size": 8960,
@@ -218,7 +216,7 @@
     "pos_emb_width": 32,
     "rope_parameters": {
       "rope_theta": 10000.0,
-      "rope_type": "axial"
     }
   }
 }

     "attention_bias": false,
     "attention_dropout": 0.0,
     "bos_token_id": 200000,
     "eos_token_id": 200001,
     "final_logit_softcapping": 20.0,
     "head_dim": 128,
     "use_cache": true,
     "vocab_size": 202048
   },
+  "transformers_version": "5.15.0.dev0",
   "video_token_id": 200091,
   "vision_config": {
     "hidden_act": "gelu",
     "hidden_size": 1536,
     "intermediate_size": 8960,
     "pos_emb_width": 32,
     "rope_parameters": {
       "rope_theta": 10000.0,
+      "rope_type": "default"
     }
   }
 }