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
```

Bolt Instruct V2

Bolt Instruct V2 is a fine-tune of meta-models/Muse-Glimmer-30B designed optimized for concise reasoning, accurate tool selection, and efficient execution of tool-based workflows.

Model Overview

Bolt Instruct V2 provide strong instruction-following capabilities across multi-turn agentic workflows while reducing cost and latency as compared to other leading ~30B models.

Key design goals:

Strong instruction adherence
Multi-turn agentic coding & automated task completion
Concise reasoning

Model Details

Type: Dense Causal Transformer w/ merged LoRA adapter
Max context: 131,072+
Supported modalities: Text, Image

Capabilities

Chat / multi-turn dialogue
Instruction following
Structured output (JSON)
Summarization & transformation
Extraction
RAG generation
Code generation

Training

Training Approach

Bolt Instruct V2 is a LoRA supervised fine-tune, trained to combine concise reasoning with precise tool use. The goal was to reduce unnecessary deliberation while preserving the ability to make tool calls. We chose supervised fine-tuning because we could demonstrate the behaviour we wanted directly: brief reasoning followed by the appropriate tool calls, including grouped calls for parallel execution.

Training Configuration


Method	LoRA supervised fine-tuning (Unsloth + TRL SFTTrainer)
Base model	unsloth/Muse-Glimmer-30B
LoRA rank / alpha	32 / 32
Trained modules	language-layer attention and MLP (vision tower frozen)
Loss	response-only (assistant turns masked in)
Optimizer	adamw_8bit, linear schedule
Quantization	none (full-precision base)

Training Data

The training corpus contains 7,646 multi-step agentic sessions, filtered to include tool schemas, at least one reasoning block, and a genuine user request. Reasoning before tool calls had a median length of 28 tokens, and 91% of tool calls were preceded by reasoning. The corpus also included 14,204 turns with multiple tool calls, representing 17.4% of tool-calling turns and occurring in 47% of sessions. Sessions contained a median of 13 tool calls, with 62.7% containing at least 10. Together, these examples demonstrated concise reasoning, parallel tool calling, and sustained multi-step tool use.


Sessions	7,646 (7,263 train / 383 held out)
Messages per session (median)	27
Assistant turns per session (median)	11
Distinct tool schemas	26
Tool use
Total tool calls	106,219
Tool-calling turns	81,570
Tool calls per session (median)	13
Sessions with ≥10 tool calls	62.7%
Parallel tool calling
Turns issuing multiple tool calls	14,204 (17.4% of tool-calling turns)
Sessions containing a parallel turn	47.1%
Calls per parallel turn (median)	2
Reasoning
Tool calls preceded by reasoning	90.6%
Reasoning length (median)	28 tokens
Reasoning length (mean)	48 tokens

Training Hardware


GPUs	2 × NVIDIA H200
GPU memory	141 GB each, 282 GB total
Training time	18 hours

Intended Use

Chat assistants
Enterprise copilots
RAG pipelines
Document processing
Structured extraction
Code assistance

Usage

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]:]))

Evaluation

We evaluated Bolt Instruct V2 on raw performance on general-purpose and agentic tasks, as well as reasoning efficiency and time to complete a given task.

Evaluation results for aisquared/bolt-instruct-v2:

Measured with the sampling configuration published for Muse Glimmer temperature 1.0 / top_p 0.95 / top_k 64, high reasoning strength served with vLLM (--generation-config auto).

Efficiency — per task

Metric	Benchmark	Muse Glimmer 30B(High)	Bolt Instruct V2	Gemma 4 31B (think on)
Reasoning tokens	BFCL	1,266	635	2,609
	τ²-bench	1,689	976	3,450
	AutomationBench	6,351	2,022	10,835
Output tokens	BFCL	459	281	313
	τ²-bench	2,514	1,931	4,177
	AutomationBench	9,910	5,988	14,839
Latency	BFCL (mean)	19.76 s	12.60 s	7.47 s
	τ²-bench	64.9 s	51.4 s	129.8 s

Agentic — multi-turn

Benchmark	Muse Glimmer 30B(High)	Bolt Instruct V2	Gemma 4 31B (think on)
τ²-bench @ temperature 0	85.37	76.83	84.15
τ²-bench @ published sampling ¹	34.45	30.64	85.21

Agentic — single-turn tool calling

Benchmark	Muse Glimmer 30B(High)	Bolt Instruct V2	Gemma 4 31B (think on)
BFCL Live	55.84%	59.67%	69.21%
BFCL Non-Live	10.12%	24.38%	—
ACEBench parallel function calling	48%	79%	81%

General capability

Benchmark	Muse Glimmer 30B(High)	Bolt Instruct V2	Bolt Instruct V1
BBH	63.00	89.00	—
GPQA Diamond (CoT, 0-shot) ³	33.6	57.5	—
IFEval (prompt-strict)	65.06	73.75	85.95
ARC Challenge	58.28	56.91	56.66
TruthfulQA MC2	61.64	59.32	69.34

Bolt Instruct V1 and V2 are not successive versions of the same model. V1 is built on Olmo 3 (32B, 64K context, text-only) and optimized for instruction following; V2 is built on Muse Glimmer (30B, 128K context, multimodal) and optimized for agentic tool execution. Each should be read against its own foundation model, But Bolt Instruct V2 improves on Muse Glimmer's IFEval score.

Reproduction. τ²-bench and AutomationBench under their official harnesses; lm-evaluation-harness for BBH, GPQA Diamond, IFEval, ARC Challenge and TruthfulQA; BFCL and ACEBench under their own.

Limitations

May hallucinate without grounding
Not suitable for high-risk domains without oversight
Requires low-temperature decoding for precise tool-call arguments
Performance varies by task domain; validate against your own task set
Built for short and medium-horizon workflows and regression is seen on long multi-step workflows

License

Bolt Instruct is released under the AI Squared Community License.

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Safetensors

Model size

30B params

Tensor type

BF16

Collection including aisquared/bolt-instruct-v2

Bolt Instruct V2

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