Instructions to use NoemaAI-labs/Noema-1.5-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NoemaAI-labs/Noema-1.5-2B with Transformers:

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

pipe = pipeline("text-generation", model="NoemaAI-labs/Noema-1.5-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NoemaAI-labs/Noema-1.5-2B")
model = AutoModelForCausalLM.from_pretrained("NoemaAI-labs/Noema-1.5-2B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NoemaAI-labs/Noema-1.5-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NoemaAI-labs/Noema-1.5-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NoemaAI-labs/Noema-1.5-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NoemaAI-labs/Noema-1.5-2B

SGLang

How to use NoemaAI-labs/Noema-1.5-2B 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 "NoemaAI-labs/Noema-1.5-2B" \
    --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": "NoemaAI-labs/Noema-1.5-2B",
		"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 "NoemaAI-labs/Noema-1.5-2B" \
        --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": "NoemaAI-labs/Noema-1.5-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NoemaAI-labs/Noema-1.5-2B with Docker Model Runner:
```
docker model run hf.co/NoemaAI-labs/Noema-1.5-2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Noema 1.5 2B

Noema 1.5 2B is an open-weight, local-first language model optimized for broad knowledge retention, precise instruction following, multi-turn constraint persistence, short-form code generation, and efficient reasoning.

It is the most balanced release in the Noema 2B lineage.

Compared with the previous Noema release, Noema 1.5 improves pooled knowledge, MMLU-Pro development, HumanEval+, strict IFEval, verified mathematics, and novel constraint following. Against stock Qwen3.5-2B in final evaluation, it improves strict IFEval by 7.02 points, complete three-turn Multi-IF success by 3.11 points, HumanEval+ by 3.05 points, and reasoning efficiency by 13.5%, while passing a preregistered full MMLU-Pro knowledge-retention gate.

No retrieval, external tools, test-time answer repair, or external model calls were used in the reported benchmark results.

Highlights

Knowledge recovery: +1.42 points on the 1,900-item pooled knowledge development composite over Noema v2.
MMLU-Pro recovery: +2.86 points over v2 on the matched development panel.
Stronger code: HumanEval+ increased from 50.00% to 53.66% over v2.
Better instructions: strict IFEval increased from 70.24% to 73.01% over v2.
Better multi-turn behavior: complete three-turn Multi-IF success was 21.1% higher than stock Qwen3.5-2B.
Efficient reasoning: 13.5% fewer completion tokens than stock Qwen3.5-2B on the final thinking panel.
Broad knowledge retained: passed the preregistered MMLU-Pro non-inferiority gate across 11,332 untouched questions.
Open-weight deployment: downloadable weights built for private and resource-conscious inference.

Release-over-release results

The table below compares Noema 1.5 with the exact previous Noema checkpoint from which training began.

Benchmark	Noema v2	Noema 1.5 2B	Delta
Pooled knowledge composite	61.84	63.26	+1.42 pp
MMLU-Pro development	50.14	53.00	+2.86 pp
HumanEval+ pass@1	50.00	53.66	+3.66 pp
IFEval prompt strict	70.24	73.01	+2.77 pp
Verified math development	78.00	92.00	+14.00 pp
Novel constraints prompt strict	45.83	51.67	+5.83 pp

These are paired development and selection results. They demonstrate release-over-release progress but are not untouched final-lockbox estimates.

Final benchmark results

The final evaluation compared the frozen Noema 1.5 candidate with the exact stock Qwen3.5-2B foundation using identical prompts, generation settings, and graders.

Benchmark	Samples	Stock Qwen3.5-2B	Noema 1.5 2B	Delta
MMLU-Pro	11,332	53.88	53.17	-0.71 pp
GPQA-Diamond	198	45.45	40.40	-5.05 pp
HumanEval+ pass@1	164	50.61	53.66	+3.05 pp
IFEval prompt strict	541	65.06	72.09	+7.02 pp
IFBench prompt loose	300	31.00	27.33	-3.67 pp
Multi-IF mean per turn	4,501 conversations	33.49	35.84	+2.36 pp
Multi-IF all three turns	4,501 conversations	14.77	17.88	+3.11 pp
Thinking native accuracy	300	67.33	68.67	+1.33 pp
Thinking mean tokens	300	7,968.95	6,894.59	-13.48%

Statistical interpretation

MMLU-Pro: passed the preregistered non-inferiority test. The one-sided 95% lower bound was -1.438 points against a -1.5-point margin.
IFEval: +7.02 points, statistically significant (p = 0.000475).
Multi-IF: +3.11 points on complete three-turn success, statistically significant (p = 4.44e-8).
HumanEval+ and thinking: favorable point estimates that were not statistically resolved.
GPQA-Diamond and IFBench: negative point estimates and active areas for future research.

MMLU-Pro was the strict confirmatory endpoint. The final evaluation report is available in the Noema training repository.

Model details

Property	Value
Public name	Noema 1.5 2B
Internal research candidate	`noema-v3-lite-l4-step-0150-bf16`
Architecture	Qwen3.5 hybrid Gated DeltaNet and gated attention
Parameters	Approximately 2B
Layers	24
Hidden dimension	2,048
Vocabulary	248,320 tokens
Training method	Restoration-aware on-policy distillation
Weight update	Rank-128 all-projection LoRA, merged for release
Native backbone context	Up to 262,144 tokens
Independently validated context	Up to 24,576 tokens
Primary evaluated language	English
Release status	Open-weight; not open source
MTP	Disabled

Noema 1.5 is the text-generation trunk. Multimodal capabilities were not trained or evaluated in this release.

Training

Noema 1.5 began from the exact previous Noema 2B release and used a restoration-aware on-policy distillation program:

150 optimizer steps;
19,200 on-policy trajectories;
7,982,979 valid completion tokens;
32 unique prompts per step;
four student rollouts per prompt;
rank-128 LoRA with alpha 256;
Gated DeltaNet, ordinary-attention, and MLP projections targeted;
tied embedding and output matrix kept frozen.

Seventy percent of assignments used Qwen3.5-9B as the capability scorer. Thirty percent used untouched Qwen3.5-2B as a restoration scorer to protect general behavior.

The training mixture contained:

52.5% broad knowledge;
17.5% mathematics and code;
22.5% general-chat restoration;
7.5% novel composed constraints.

An experimental secondary constraint-RL stage was tested, failed its first mandatory gate, and was rejected. It did not modify the released checkpoint.

Intended uses

Noema 1.5 is well suited to:

local and privacy-sensitive assistants;
structured and format-constrained generation;
multi-turn conversations;
short Python program synthesis;
educational and lightweight reasoning tasks;
offline applications;
memory- and compute-constrained deployments.

For knowledge-intensive applications, use retrieval or authoritative tools and verify important factual claims.

Usage

Noema 1.5 requires a transformers version with qwen3_5_text support. The release was built and validated with Transformers 5.12.1.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "NoemaAI-labs/Noema-1.5-2B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": "Write a Python function that merges two sorted lists.",
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=False,
)

response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[-1]:],
    skip_special_tokens=True,
)

print(response)

Non-thinking mode is recommended for concise instruction following, structured generation, and code. The packaged defaults are greedy (do_sample=false) and stop at <|im_end|>.

For harder reasoning tasks, use enable_thinking=True, do_sample=True, temperature=1.0, top_p=0.95, and top_k=20. Deployments should still enforce output limits and repeated-span detection.

Evaluation methodology

The final evaluation used:

frozen model and tokenizer revisions;
identical prompts and settings across both arms;
BF16 inference on NVIDIA L40S hardware;
vLLM 0.19.1;
pinned official graders;
paired bootstrap intervals and McNemar tests;
deterministic keys and exact-count verification;
no score inspection before both arms were complete.

Limitations

The release-over-release v2 comparison uses matched development suites, not a third untouched final arm.
Broad MMLU-Pro knowledge was statistically retained against stock but not improved in the final point estimate.
GPQA-Diamond and IFBench produced lower final point estimates.
The release is based on one primary training seed and does not establish that on-policy distillation beats matched supervised training.
Evaluation was primarily English and text-only.
Native 262K context support comes from the Qwen3.5 architecture; Noema independently evaluated contexts only up to 24,576 tokens.
Thinking mode can still produce long or repetitive responses.
The model can hallucinate and should not be treated as the sole authority for high-stakes decisions.

Release status

Noema 1.5 2B is an open-weight release, not an open-source release. The weights are publicly downloadable, but no open-source license is granted with this repository at this time.

Citation

@misc{noema15_2b_2026,
  title        = {Noema 1.5 2B: Restoration-Aware On-Policy Distillation for Local AI},
  author       = {NoemaAI Labs},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/NoemaAI-labs/Noema-1.5-2B}}
}