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mosesman/Nanbeige4.1-3B-openvino-int4
Nanbeige4.1-3B-openvino-int4 is a text generation model from mosesman. Use it when you need the model to write or continue text. It is set up for openvino. The card lists the license as apache-2.0.
OpenVINO IR export of Nanbeige/Nanbeige4.1-3B quantized with the NPU-correct recipe used across the mosesman/LFM2.5-2.6B-openvino-int4-npu family:
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From the Hugging Face model README
OpenVINO IR export of Nanbeige/Nanbeige4.1-3B quantized with the NPU-correct recipe used across the mosesman/LFM2.5-2.6B-openvino-int4-npu family:
--weight-format int4 --sym --group-size 128 --backup-precision int8_sym
One OpenVINO IR file runs on NPU, GPU, or CPU - the device is a string at pipeline init (device="NPU" / "GPU" / "CPU").
Role in the Lex NPU Runtime dual-device suite:
So despite the neutral repo name: this model is the iGPU resident in the suite, while LFM2.5 owns the NPU.
Nanbeige4.2-3B uses a custom nanbeige architecture that has no OpenVINO export config in optimum-intel (and needs a fork of llama.cpp). The 4.1 line is standard LlamaForCausalLM, so it exports cleanly and stays compatible with every OpenVINO release. For an edge runtime that beginners install, standard-arch compatibility beats the newest checkpoint.
| Export | Arch | OpenVINO |
|---|---|---|
| Nanbeige4.2-3B | custom nanbeige | unsupported (optimum-intel error) |
| This repo (4.1-3B) | LlamaForCausalLM | exportable + validated |
| Weight compression mode | % all parameters |
|---|---|
| int4_sym, group size 128 | 78% (224/226 layers, 100% of ratio-defining) |
| int8_sym, per-channel (backup) | 22% (2/226 layers: embeddings + LM head) |
pip install openvino openvino-genai transformers
import openvino_genai as ov_genai
from transformers import AutoTokenizer
model_id = "mosesman/Nanbeige4.1-3B-openvino-int4"
device = "GPU" # suite role: iGPU specialist. Also works on "NPU" / "CPU"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": "Write a Python function that returns the sum of a list."}],
tokenize=False,
add_generation_prompt=True,
)
pipe = ov_genai.LLMPipeline(model_id, device)
config = ov_genai.GenerationConfig()
config.max_new_tokens = 128
config.do_sample = False
print(pipe.generate(prompt, config))
Note: Nanbeige4.1 is a reasoning model - it emits a <think> block before answering. Keep add_generation_prompt=True so the model knows when to reason.
optimum-cli export openvino \
--model Nanbeige/Nanbeige4.1-3B \
--trust-remote-code \
--weight-format int4 \
--sym \
--group-size 128 \
--backup-precision int8_sym \
./Nanbeige4.1-3B-openvino-int4-npu
Tooling: optimum-intel 2.1.0 / optimum 2.3.0, nncf 3.3.0, openvino 2026.3.0, transformers 5.4.0.
OpenVINO GenAI layout:
openvino_model.xml / .bin (2.4GB)openvino_tokenizer.xml / .bin, openvino_detokenizer.xml / .binQuantized derivative of Nanbeige/Nanbeige4.1-3B under Apache-2.0. See LICENSE and NOTICE.
Provided as-is for interoperability with Intel NPU / OpenVINO. Not affiliated with Nanbeige or Intel. Validate quality for your own workload before production use.