Instructions to use aisquared/bolt-instruct-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aisquared/bolt-instruct-1b with Transformers:

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

pipe = pipeline("text-generation", model="aisquared/bolt-instruct-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aisquared/bolt-instruct-1b")
model = AutoModelForCausalLM.from_pretrained("aisquared/bolt-instruct-1b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aisquared/bolt-instruct-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aisquared/bolt-instruct-1b"
# 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-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aisquared/bolt-instruct-1b

SGLang

How to use aisquared/bolt-instruct-1b 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-1b" \
    --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-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-1b" \
        --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-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aisquared/bolt-instruct-1b with Docker Model Runner:
```
docker model run hf.co/aisquared/bolt-instruct-1b
```

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Bolt Instruct Models

Bolt Instruct is a family of instruction-tuned language models designed for high-quality generation, reasoning, and enterprise workflows.

These models are fine-tuned from Allen Institute for AI OLMo instruct models and optimized for:

General conversational AI
Structured and controllable generation
Retrieval-Augmented Generation (RAG)
Enterprise document understanding
Code generation and transformation

Model Overview

Bolt Instruct models provide strong instruction-following capabilities across diverse tasks with robust long-context support.

Key design goals:

Strong instruction adherence
High-quality structured outputs (JSON, extraction)
RAG-grounded responses
Long-context support (65k tokens for 7B and 32B)
Balanced chat, reasoning, and coding performance

Model Variants

Model	Base Model	Positioning
bolt-instruct-1b	allenai/OLMo-2-0425-1B-Instruct	Lightweight / low-latency
bolt-instruct-7b	allenai/OLMo-3-7B-Instruct	Balanced
bolt-instruct-32b	allenai/OLMo-3.1-32B-Instruct	Highest quality

Model Details

Type: Causal LM (instruction-tuned)
Max context: 65,536 tokens (7B and 32B), 4,096 tokens (1B)
Training context: 32k (7B), 16k (32B), 4k (1B)

Capabilities

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

Training

Method: Supervised Fine-Tuning (SFT)
Dataset size: ~125k conversations
Eval set: ~10k examples
Data mix: public + synthetic + internal tasks

Training Approach

1B → full fine-tune
7B / 32B → QLoRA (4-bit)

Hardware

1× A100 80GB GPU

Intended Use

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

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "aisquared/bolt-instruct-7b"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Evaluation

To evaluate these models, we ran a subset of tasks using the Eleuther AI Language Model Evaluation Harness. Below are the metrics for each model.

Language Model Evaluation Harness

Evaluation results for aisquared/bolt-instruct-1b:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.3490	±	0.0139
		none	0	acc_norm	↑	0.3823	±	0.0142
arc_easy	1	none	0	acc	↑	0.6098	±	0.0100
		none	0	acc_norm	↑	0.5560	±	0.0102
bbh	3	get-answer		exact_match	↑	0.3081	±	0.0052
- bbh_cot_fewshot_boolean_expressions	4	get-answer	3	exact_match	↑	0.5840	±	0.0312
- bbh_cot_fewshot_causal_judgement	4	get-answer	3	exact_match	↑	0.5508	±	0.0365
- bbh_cot_fewshot_date_understanding	4	get-answer	3	exact_match	↑	0.2600	±	0.0278
- bbh_cot_fewshot_disambiguation_qa	4	get-answer	3	exact_match	↑	0.3640	±	0.0305
- bbh_cot_fewshot_dyck_languages	4	get-answer	3	exact_match	↑	0.0040	±	0.0040
- bbh_cot_fewshot_formal_fallacies	4	get-answer	3	exact_match	↑	0.5040	±	0.0317
- bbh_cot_fewshot_geometric_shapes	4	get-answer	3	exact_match	↑	0.0920	±	0.0183
- bbh_cot_fewshot_hyperbaton	4	get-answer	3	exact_match	↑	0.5240	±	0.0316
- bbh_cot_fewshot_logical_deduction_five_objects	4	get-answer	3	exact_match	↑	0.1720	±	0.0239
- bbh_cot_fewshot_logical_deduction_seven_objects	4	get-answer	3	exact_match	↑	0.1080	±	0.0197
- bbh_cot_fewshot_logical_deduction_three_objects	4	get-answer	3	exact_match	↑	0.3520	±	0.0303
- bbh_cot_fewshot_movie_recommendation	4	get-answer	3	exact_match	↑	0.5040	±	0.0317
- bbh_cot_fewshot_multistep_arithmetic_two	4	get-answer	3	exact_match	↑	0.0600	±	0.0151
- bbh_cot_fewshot_navigate	4	get-answer	3	exact_match	↑	0.5560	±	0.0315
- bbh_cot_fewshot_object_counting	4	get-answer	3	exact_match	↑	0.4360	±	0.0314
- bbh_cot_fewshot_penguins_in_a_table	4	get-answer	3	exact_match	↑	0.2123	±	0.0340
- bbh_cot_fewshot_reasoning_about_colored_objects	4	get-answer	3	exact_match	↑	0.2440	±	0.0272
- bbh_cot_fewshot_ruin_names	4	get-answer	3	exact_match	↑	0.2440	±	0.0272
- bbh_cot_fewshot_salient_translation_error_detection	4	get-answer	3	exact_match	↑	0.1920	±	0.0250
- bbh_cot_fewshot_snarks	4	get-answer	3	exact_match	↑	0.3989	±	0.0368
- bbh_cot_fewshot_sports_understanding	4	get-answer	3	exact_match	↑	0.6560	±	0.0301
- bbh_cot_fewshot_temporal_sequences	4	get-answer	3	exact_match	↑	0.2760	±	0.0283
- bbh_cot_fewshot_tracking_shuffled_objects_five_objects	4	get-answer	3	exact_match	↑	0.1920	±	0.0250
- bbh_cot_fewshot_tracking_shuffled_objects_seven_objects	4	get-answer	3	exact_match	↑	0.0360	±	0.0118
- bbh_cot_fewshot_tracking_shuffled_objects_three_objects	4	get-answer	3	exact_match	↑	0.2840	±	0.0286
- bbh_cot_fewshot_web_of_lies	4	get-answer	3	exact_match	↑	0.5240	±	0.0316
- bbh_cot_fewshot_word_sorting	4	get-answer	3	exact_match	↑	0.0360	±	0.0118
gsm8k	3	flexible-extract	5	exact_match	↑	0.5072	±	0.0138
		strict-match	5	exact_match	↑	0.4943	±	0.0138
hellaswag	1	none	0	acc	↑	0.4729	±	0.0050
		none	0	acc_norm	↑	0.6181	±	0.0048
mmlu_pro	2	custom-extract		exact_match	↑	0.1435	±	0.0032
- biology	3	custom-extract	5	exact_match	↑	0.2050	±	0.0151
- business	3	custom-extract	5	exact_match	↑	0.1369	±	0.0122
- chemistry	3	custom-extract	5	exact_match	↑	0.0848	±	0.0083
- computer_science	3	custom-extract	5	exact_match	↑	0.1415	±	0.0172
- economics	3	custom-extract	5	exact_match	↑	0.1943	±	0.0136
- engineering	3	custom-extract	5	exact_match	↑	0.0929	±	0.0093
- health	3	custom-extract	5	exact_match	↑	0.1528	±	0.0126
- history	3	custom-extract	5	exact_match	↑	0.1549	±	0.0186
- law	3	custom-extract	5	exact_match	↑	0.1081	±	0.0094
- math	3	custom-extract	5	exact_match	↑	0.1414	±	0.0095
- other	3	custom-extract	5	exact_match	↑	0.1916	±	0.0130
- philosophy	3	custom-extract	5	exact_match	↑	0.1383	±	0.0155
- physics	3	custom-extract	5	exact_match	↑	0.1186	±	0.0090
- psychology	3	custom-extract	5	exact_match	↑	0.2130	±	0.0145
truthfulqa_mc2	3	none	0	acc	↑	0.4734	±	0.0153
winogrande	1	none	0	acc	↑	0.6156	±	0.0137

Evaluation results for aisquared/bolt-instruct-7b:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.4778	±	0.0146
		none	0	acc_norm	↑	0.4957	±	0.0146
arc_easy	1	none	0	acc	↑	0.7534	±	0.0088
		none	0	acc_norm	↑	0.7311	±	0.0091
bbh	3	get-answer		exact_match	↑	0.3038	±	0.0047
- bbh_cot_fewshot_boolean_expressions	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_causal_judgement	4	get-answer	3	exact_match	↑	0.5668	±	0.0363
- bbh_cot_fewshot_date_understanding	4	get-answer	3	exact_match	↑	0.4480	±	0.0315
- bbh_cot_fewshot_disambiguation_qa	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_dyck_languages	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_formal_fallacies	4	get-answer	3	exact_match	↑	0.2240	±	0.0264
- bbh_cot_fewshot_geometric_shapes	4	get-answer	3	exact_match	↑	0.2960	±	0.0289
- bbh_cot_fewshot_hyperbaton	4	get-answer	3	exact_match	↑	0.5200	±	0.0317
- bbh_cot_fewshot_logical_deduction_five_objects	4	get-answer	3	exact_match	↑	0.0200	±	0.0089
- bbh_cot_fewshot_logical_deduction_seven_objects	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_logical_deduction_three_objects	4	get-answer	3	exact_match	↑	0.6720	±	0.0298
- bbh_cot_fewshot_movie_recommendation	4	get-answer	3	exact_match	↑	0.1200	±	0.0206
- bbh_cot_fewshot_multistep_arithmetic_two	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_navigate	4	get-answer	3	exact_match	↑	0.5560	±	0.0315
- bbh_cot_fewshot_object_counting	4	get-answer	3	exact_match	↑	0.1520	±	0.0228
- bbh_cot_fewshot_penguins_in_a_table	4	get-answer	3	exact_match	↑	0.4110	±	0.0409
- bbh_cot_fewshot_reasoning_about_colored_objects	4	get-answer	3	exact_match	↑	0.1880	±	0.0248
- bbh_cot_fewshot_ruin_names	4	get-answer	3	exact_match	↑	0.4800	±	0.0317
- bbh_cot_fewshot_salient_translation_error_detection	4	get-answer	3	exact_match	↑	0.4760	±	0.0316
- bbh_cot_fewshot_snarks	4	get-answer	3	exact_match	↑	0.2921	±	0.0342
- bbh_cot_fewshot_sports_understanding	4	get-answer	3	exact_match	↑	0.6760	±	0.0297
- bbh_cot_fewshot_temporal_sequences	4	get-answer	3	exact_match	↑	0.5880	±	0.0312
- bbh_cot_fewshot_tracking_shuffled_objects_five_objects	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_tracking_shuffled_objects_seven_objects	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_tracking_shuffled_objects_three_objects	4	get-answer	3	exact_match	↑	0.8280	±	0.0239
- bbh_cot_fewshot_web_of_lies	4	get-answer	3	exact_match	↑	0.6560	±	0.0301
- bbh_cot_fewshot_word_sorting	4	get-answer	3	exact_match	↑	0.1400	±	0.0220
gsm8k	3	flexible-extract	5	exact_match	↑	0.7998	±	0.0110
		strict-match	5	exact_match	↑	0.7392	±	0.0121
hellaswag	1	none	0	acc	↑	0.4882	±	0.0050
		none	0	acc_norm	↑	0.6165	±	0.0049
mmlu_pro	2	custom-extract		exact_match	↑	0.4978	±	0.0044
- biology	3	custom-extract	5	exact_match	↑	0.6848	±	0.0174
- business	3	custom-extract	5	exact_match	↑	0.5729	±	0.0176
- chemistry	3	custom-extract	5	exact_match	↑	0.5380	±	0.0148
- computer_science	3	custom-extract	5	exact_match	↑	0.5878	±	0.0243
- economics	3	custom-extract	5	exact_match	↑	0.5592	±	0.0171
- engineering	3	custom-extract	5	exact_match	↑	0.2405	±	0.0137
- health	3	custom-extract	5	exact_match	↑	0.4670	±	0.0175
- history	3	custom-extract	5	exact_match	↑	0.3727	±	0.0248
- law	3	custom-extract	5	exact_match	↑	0.2525	±	0.0131
- math	3	custom-extract	5	exact_match	↑	0.7158	±	0.0123
- other	3	custom-extract	5	exact_match	↑	0.4351	±	0.0163
- philosophy	3	custom-extract	5	exact_match	↑	0.4128	±	0.0221
- physics	3	custom-extract	5	exact_match	↑	0.5142	±	0.0139
- psychology	3	custom-extract	5	exact_match	↑	0.5602	±	0.0176
truthfulqa_mc2	3	none	0	acc	↑	0.5666	±	0.0162
winogrande	1	none	0	acc	↑	0.6385	±	0.0135

Evaluation results for aisquared/bolt-instruct-32b:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.5776	±	0.0144
		none	0	acc_norm	↑	0.6007	±	0.0143
arc_easy	1	none	0	acc	↑	0.8333	±	0.0076
		none	0	acc_norm	↑	0.8228	±	0.0078
bbh	3	get-answer		exact_match	↑	0.3087	±	0.0048
- bbh_cot_fewshot_boolean_expressions	4	get-answer	3	exact_match	↑	0.5760	±	0.0313
- bbh_cot_fewshot_causal_judgement	4	get-answer	3	exact_match	↑	0.5882	±	0.0361
- bbh_cot_fewshot_date_understanding	4	get-answer	3	exact_match	↑	0.6640	±	0.0299
- bbh_cot_fewshot_disambiguation_qa	4	get-answer	3	exact_match	↑	0.1920	±	0.0250
- bbh_cot_fewshot_dyck_languages	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_formal_fallacies	4	get-answer	3	exact_match	↑	0.0480	±	0.0135
- bbh_cot_fewshot_geometric_shapes	4	get-answer	3	exact_match	↑	0.2760	±	0.0283
- bbh_cot_fewshot_hyperbaton	4	get-answer	3	exact_match	↑	0.3200	±	0.0296
- bbh_cot_fewshot_logical_deduction_five_objects	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_logical_deduction_seven_objects	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_logical_deduction_three_objects	4	get-answer	3	exact_match	↑	0.5400	±	0.0316
- bbh_cot_fewshot_movie_recommendation	4	get-answer	3	exact_match	↑	0.6000	±	0.0310
- bbh_cot_fewshot_multistep_arithmetic_two	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_navigate	4	get-answer	3	exact_match	↑	0.0160	±	0.0080
- bbh_cot_fewshot_object_counting	4	get-answer	3	exact_match	↑	0.5120	±	0.0317
- bbh_cot_fewshot_penguins_in_a_table	4	get-answer	3	exact_match	↑	0.2945	±	0.0379
- bbh_cot_fewshot_reasoning_about_colored_objects	4	get-answer	3	exact_match	↑	0.2280	±	0.0266
- bbh_cot_fewshot_ruin_names	4	get-answer	3	exact_match	↑	0.5120	±	0.0317
- bbh_cot_fewshot_salient_translation_error_detection	4	get-answer	3	exact_match	↑	0.5440	±	0.0316
- bbh_cot_fewshot_snarks	4	get-answer	3	exact_match	↑	0.7079	±	0.0342
- bbh_cot_fewshot_sports_understanding	4	get-answer	3	exact_match	↑	0.4880	±	0.0317
- bbh_cot_fewshot_temporal_sequences	4	get-answer	3	exact_match	↑	0.3120	±	0.0294
- bbh_cot_fewshot_tracking_shuffled_objects_five_objects	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_tracking_shuffled_objects_seven_objects	4	get-answer	3	exact_match	↑	0.0000	±	0.0000
- bbh_cot_fewshot_tracking_shuffled_objects_three_objects	4	get-answer	3	exact_match	↑	0.6280	±	0.0306
- bbh_cot_fewshot_web_of_lies	4	get-answer	3	exact_match	↑	0.4400	±	0.0315
- bbh_cot_fewshot_word_sorting	4	get-answer	3	exact_match	↑	0.0280	±	0.0105
gsm8k	3	flexible-extract	5	exact_match	↑	0.8795	±	0.0090
		strict-match	5	exact_match	↑	0.7801	±	0.0114
hellaswag	1	none	0	acc	↑	0.5407	±	0.0050
		none	0	acc_norm	↑	0.6763	±	0.0047
mmlu_pro	2	custom-extract		exact_match	↑	0.6340	±	0.0042
- biology	3	custom-extract	5	exact_match	↑	0.8117	±	0.0146
- business	3	custom-extract	5	exact_match	↑	0.6907	±	0.0165
- chemistry	3	custom-extract	5	exact_match	↑	0.6431	±	0.0142
- computer_science	3	custom-extract	5	exact_match	↑	0.6951	±	0.0228
- economics	3	custom-extract	5	exact_match	↑	0.7405	±	0.0151
- engineering	3	custom-extract	5	exact_match	↑	0.3447	±	0.0153
- health	3	custom-extract	5	exact_match	↑	0.6540	±	0.0166
- history	3	custom-extract	5	exact_match	↑	0.5512	±	0.0255
- law	3	custom-extract	5	exact_match	↑	0.3860	±	0.0147
- math	3	custom-extract	5	exact_match	↑	0.7979	±	0.0109
- other	3	custom-extract	5	exact_match	↑	0.6028	±	0.0161
- philosophy	3	custom-extract	5	exact_match	↑	0.5912	±	0.0220
- physics	3	custom-extract	5	exact_match	↑	0.6551	±	0.0132
- psychology	3	custom-extract	5	exact_match	↑	0.7243	±	0.0158
truthfulqa_mc2	3	none	0	acc	↑	0.6906	±	0.0153
winogrande	1	none	0	acc	↑	0.6630	±	0.0133