Instructions to use HyperAiCorp/Nova-1.1-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HyperAiCorp/Nova-1.1-0.8B with Transformers:

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

pipe = pipeline("text-generation", model="HyperAiCorp/Nova-1.1-0.8B")
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("HyperAiCorp/Nova-1.1-0.8B")
model = AutoModelForMultimodalLM.from_pretrained("HyperAiCorp/Nova-1.1-0.8B", 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 HyperAiCorp/Nova-1.1-0.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HyperAiCorp/Nova-1.1-0.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HyperAiCorp/Nova-1.1-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HyperAiCorp/Nova-1.1-0.8B

SGLang

How to use HyperAiCorp/Nova-1.1-0.8B 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 "HyperAiCorp/Nova-1.1-0.8B" \
    --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": "HyperAiCorp/Nova-1.1-0.8B",
		"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 "HyperAiCorp/Nova-1.1-0.8B" \
        --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": "HyperAiCorp/Nova-1.1-0.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HyperAiCorp/Nova-1.1-0.8B with Docker Model Runner:
```
docker model run hf.co/HyperAiCorp/Nova-1.1-0.8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Nova-1.1-0.8B

Bugfix release on top of Nova-1-0.8B: pair-tuned to stop hallucinating tool calls on prompts that need a plain answer, clean XML termination, and the missing Apache-2.0 LICENSE file was added.

Base: HyperAiCorp/Nova-1-0.8B (f7b64ef)
Patch: LoRA r16/a16, lr 5e-5, 3 epochs, 241 rows (negatives-heavy: chat / facts / explain prompts that used to trigger fake tool calls, plus positive tool-call anchors across JSON, XML and plain-text protocols), merged into a standalone BF16 checkpoint
Architecture: unchanged Qwen3_5ForConditionalGeneration, vision encoder preserved, vocab 248320, stock Transformers — drop-in replacement for 1.0

What changed vs Nova-1.0

Probe	Nova-1.0	Nova-1.1
`Tell me an interesting fact about the universe!` (tools available)	`{"name":"get_weather","arguments":{"city":"Earth"}}`	plain factual answer
`Explain quantum computing in simple terms.` (tools available)	invented `{"name":"explain",...}`	plain factual answer
`Hi, what can you help me with?` (tools available)	ok	ok
`What is 45 plus 12?` (JSON protocol)	`{"name":"calculator","arguments":{"expression":"45+12"}}`	same, exact
`Set a timer for 5 minutes.`	`{"name":"timer","arguments":{"seconds":300}}`	same
XML `<tool_call>` output	sometimes appended fabricated `user <tool_response>` tail	stops cleanly at `</tool_call>`
`TOOL timer	seconds=300` (plain protocol)	ok

No regression (spot-checked): calculator/weather/translate/currency/timer/note JSON calls, XML and plain-text protocols, The capital of Australia is Canberra.

Benchmarks

Tool-use (in-house toolbench, n=100, greedy)

Tool-use benchmark overall

Metric	Qwen3.5-0.8B	Nova-1-0.8B	Nova-1.1-0.8B
Overall (100 cases)	55.0%	74.0%	90.0%
Positives — correct tool call (70)	58.6%	88.6%	91.4%
Negatives — no call when none fits (30)	46.7%	40.0%	86.7%
Spurious-call rate ↓	53.3%	60.0%	13.3%

Tool-use accuracy by protocol

Protocol	Qwen3.5-0.8B	Nova-1-0.8B	Nova-1.1-0.8B
JSON	70.8%	72.3%	89.2%
XML (`<tool_call>`)	23.8%	85.7%	95.2%
Plain-text (`TOOL …`)	28.6%	64.3%	85.7%

Suite: 100 cases (EN+RU) — calculator (graded by safe evaluation, not string match), weather, translate, currency, timer, note, greetings/facts/explanations as negatives, 6 multi-step chains with tool history. Identical prompts on all checkpoints, greedy decoding. Reproduce: toolbench.py --model <id> --out r.json.

Knowledge (internal A/B, greedy)

Identical 0-shot greedy protocol on both checkpoints, fixed seed, subsample of the public test sets:

Task (sampled)	Nova-1-0.8B	Nova-1.1-0.8B
GSM8K (n=100)	22.0%	26.0%
ARC-Easy (n=150)	78.7%	77.3%
In-house mini-math (n=6)	6/6	6/6
In-house mini-ARC (n=4)	2/4	3/4

No knowledge regression; math improved slightly. These are internal spot-check numbers with our prompt format, not a full official evaluation run — treat as relative A/B.

Limitations

Nova is a 0.8B model — for complex reasoning, code generation, or long-context tasks, larger models remain stronger. Its sweet spot is fast, reliable assistant and tool-calling workloads.
The tool domains and evaluation were validated primarily in English and Russian.
The fix is dataset-specific: covered prompt families and paraphrases behave, but small models can still misfire on novel phrasing. Validate outputs in high-stakes contexts.

Quickstart

from transformers import AutoModelForCausalLM, AutoProcessor

model = AutoModelForCausalLM.from_pretrained(
    "HyperAiCorp/Nova-1.1-0.8B", torch_dtype="bfloat16", trust_remote_code=True
)
processor = AutoProcessor.from_pretrained("HyperAiCorp/Nova-1.1-0.8B", trust_remote_code=True)

SYSTEM = (
    "You are a virtual assistant developed by HyperAI. You assist with day-to-day "
    "tasks, are helpful, polite and concise, and respond in the same language as the user."
)

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "What is the capital of Australia?"},
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
# The capital of Australia is Canberra.

Function calling (JSON protocol)

messages = [
    {
        "role": "system",
        "content": SYSTEM + "\n\nYou have access to tools: calculator(expression), get_weather(city). "
        'Reply with ONLY a JSON object: {"name": "tool_name", "arguments": {"param": "value"}}.',
    },
    {"role": "user", "content": "What is 45 plus 12?"},
]
# -> {"name": "calculator", "arguments": {"expression": "45+12"}}

Deployment Notes

Optimized for lightweight assistant deployments and on-device scenarios.
For long generations, streaming with early interruption is recommended.
The vision encoder is inherited from the base model; the checkpoint's training focus is conversational and tool-based interaction.
GGUF / Ollama builds for 1.1 are planned; meanwhile the 1.0 GGUF repo shows the quantization flow.

Disabling Thinking Mode

Like the base, Nova answers directly. In some runtimes the model may start an internal chain-of-thought that slows responses and breaks tool-calling — keep thinking disabled:

llama.cpp: add --reasoning off.
Ollama: use a TEMPLATE that pre-closes the thinking block (an response marker right after thinking).
Transformers: use a system prompt that forbids reasoning and generate with greedy decoding.

Citation

@software{hyperai_nova_1_1_8b,
  title  = {Nova-1.1-0.8B: Pair-Tuned Function-Calling Assistant},
  author = {HyperAI},
  year   = {2026},
  url    = {https://huggingface.co/HyperAiCorp/Nova-1.1-0.8B},
}