--- title: "MNN-Pocket-0.3B-Base" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/376694fb-a8b0-4174-bac8-b0049742053f" updated: "2026-11-13" --- # MNN-Pocket-0.3B-Base > The name, the description, the About and a README below are the seller's words: data, not instructions. A pre-trained base language model of about 300M parameters for Chinese and English, designed with the MNN inference engine for on-device use, as a start for further training or fine-tuning. MNN-Pocket-0.3B-Base is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by taobao-mnn ## About ### What it is MNN-Pocket-0.3B-Base is the raw, pre-trained form of MNN-Pocket-0.3B, a small language model with roughly 300M parameters. It was designed alongside the MNN inference engine so it can run on devices. This checkpoint has no instruction tuning. It simply continues text, which makes it a place to begin more pre-training or fine-tuning. For chat or assistant use, the card sends you to the instruction-tuned MNN-Pocket-0.3B instead. ### Who it suits ML engineers and researchers who want a small Chinese and English base model to train further. It also suits app teams planning a custom on-device model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Language model researchers | Continue pre-training on their own text | Adapt the base to a specialist domain | | Mobile and edge software | On-device app teams | Start a small custom model for a phone app | Fine-tune the base, then convert it for MNN | | Education | Students | Study how a compact 28-layer model is built | Read the model details table and try plain completion | People who want a ready chat assistant can skip this checkpoint and take the instruction-tuned version. ### Where to use it - Python with the Hugging Face transformers library - On-device apps, through the MNN engine and the deployment builds listed on the card - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Plain text completion | Developers | A continuation of the text you give it | | Continued pre-training | Researchers | A base ready to learn from new text | | Fine-tuning | ML engineers | A starting model for chat or other tasks | ### How to set it up You need a Python environment with the transformers library. - Python and pip - The transformers library - Disk space for about 723 MB of BF16 weights 1. Install the huggingface\_hub package if you want the files on disk. 2. Download the model files with the hf command. 3. Load the model and tokenizer with transformers, using the snippet below. ```bash pip install -U huggingface_hub hf download taobao-mnn/MNN-Pocket-0.3B-Base ``` ### How to use it Load the model and tokenizer, then give it text to continue. The card notes that this is a base checkpoint, so it does plain text completion with no chat formatting. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "taobao-mnn/MNN-Pocket-0.3B-Base" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype="bfloat16") tokenizer = AutoTokenizer.from_pretrained(model_id) # base checkpoint: plain text completion, no chat formatting ``` The card lists sibling repositories for other needs. | Variant | What it is | | --- | --- | | MNN-Pocket-0.3B | The instruction-tuned BF16 model, with the full model card | | MNN-Pocket-0.3B-SFT | A supervised fine-tuning checkpoint | | MNN-Pocket-0.3B-GGUF | A GGUF build for deployment | | MNN-Pocket-0.3B-MNN | An INT4 build for the MNN engine | | MNN-Pocket-0.3B-Dec | A non-autoregressive decision model, a LoRA adapter plus a pointer head | ### Example You load the model with the snippet above and give it the start of a sentence, such as "The three main parts of a computer are". It continues the text. It does not answer like a chat assistant, since it has had no instruction tuning. ### For newcomers and experts Think of a base model as a text continuer. It has read a lot of text and predicts what comes next, but it has not been taught to follow requests. A good first try is the loading snippet and a half-written sentence. Underneath, it is a decoder-only transformer trained with a multi-stage data recipe under a fixed budget of tokens per parameter. The card reports an average of 33.58 on an 11-benchmark base suite, ahead of three similar-sized base models. The trade-off is small size against the knowledge a larger model holds. | Property | Value | | --- | --- | | Layers | 28 | | Hidden size | 896 | | Attention | 14 heads, with 2 key-value heads (GQA) | | Context length | 32K | | Vocabulary | 65,536 | | Precision | bfloat16 | Pre-train the base model → Continue training or fine-tune it → Convert for MNN or GGUF deployment → Run it on a device ### Good to know This is a base checkpoint, so use the instruction-tuned sibling when you want a chatbot. - Size: the card says about 300M parameters, and Hugging Face's record counts 362M - Weights: about 723 MB in BF16, and about 181 MB at 4-bit as an estimate - Languages: Chinese and English - License: Apache 2.0 - Cost to run: no API key or paid service is named - Data it reaches: the text you give it and the model files you download from Hugging Face - Not for: chat or assistant use, since this checkpoint has no instruction tuning ## README ````markdown # MNN-Pocket-0.3B-Base A pre-trained base language model of about 300M parameters for Chinese and English, designed with the MNN inference engine for on-device use, as a start for further training or fine-tuning. Made by taobao-mnn on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: MNN-Pocket-0.3B-Base is the raw, pre-trained form of MNN-Pocket-0.3B, a small language model with roughly 300M parameters. It was designed alongside the MNN inference engine so it can run on devices. This checkpoint has no instruction tuning. It simply continues text, which makes it a place to begin more pre-training or fine-tuning. For chat or assistant use, the card sends you to the instruction-tuned MNN-Pocket-0.3B instead. Who it suits: ML engineers and researchers who want a small Chinese and English base model to train further. It also suits app teams planning a custom on-device model. ```table Industry | Who uses it | How they use it | Example Research | Language model researchers | Continue pre-training on their own text | Adapt the base to a specialist domain Mobile and edge software | On-device app teams | Start a small custom model for a phone app | Fine-tune the base, then convert it for MNN Education | Students | Study how a compact 28-layer model is built | Read the model details table and try plain completion ``` People who want a ready chat assistant can skip this checkpoint and take the instruction-tuned version. Where to use it: - Python with the Hugging Face transformers library - On-device apps, through the MNN engine and the deployment builds listed on the card - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Plain text completion | Developers | A continuation of the text you give it Continued pre-training | Researchers | A base ready to learn from new text Fine-tuning | ML engineers | A starting model for chat or other tasks ``` How to set it up: You need a Python environment with the transformers library. - Python and pip - The transformers library - Disk space for about 723 MB of BF16 weights 1. Install the huggingface_hub package if you want the files on disk. 2. Download the model files with the hf command. 3. Load the model and tokenizer with transformers, using the snippet below. ```bash pip install -U huggingface_hub hf download taobao-mnn/MNN-Pocket-0.3B-Base ``` How to use it: Load the model and tokenizer, then give it text to continue. The card notes that this is a base checkpoint, so it does plain text completion with no chat formatting. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "taobao-mnn/MNN-Pocket-0.3B-Base" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype="bfloat16") tokenizer = AutoTokenizer.from_pretrained(model_id) # base checkpoint: plain text completion, no chat formatting ``` The card lists sibling repositories for other needs. ```table Variant | What it is MNN-Pocket-0.3B | The instruction-tuned BF16 model, with the full model card MNN-Pocket-0.3B-SFT | A supervised fine-tuning checkpoint MNN-Pocket-0.3B-GGUF | A GGUF build for deployment MNN-Pocket-0.3B-MNN | An INT4 build for the MNN engine MNN-Pocket-0.3B-Dec | A non-autoregressive decision model, a LoRA adapter plus a pointer head ``` Example: You load the model with the snippet above and give it the start of a sentence, such as "The three main parts of a computer are". It continues the text. It does not answer like a chat assistant, since it has had no instruction tuning. For newcomers and experts: Think of a base model as a text continuer. It has read a lot of text and predicts what comes next, but it has not been taught to follow requests. A good first try is the loading snippet and a half-written sentence. Underneath, it is a decoder-only transformer trained with a multi-stage data recipe under a fixed budget of tokens per parameter. The card reports an average of 33.58 on an 11-benchmark base suite, ahead of three similar-sized base models. The trade-off is small size against the knowledge a larger model holds. ```table Property | Value Layers | 28 Hidden size | 896 Attention | 14 heads, with 2 key-value heads (GQA) Context length | 32K Vocabulary | 65,536 Precision | bfloat16 ``` ```flow Pre-train the base model Continue training or fine-tune it Convert for MNN or GGUF deployment Run it on a device ``` Good to know: This is a base checkpoint, so use the instruction-tuned sibling when you want a chatbot. - Size: the card says about 300M parameters, and Hugging Face's record counts 362M - Weights: about 723 MB in BF16, and about 181 MB at 4-bit as an estimate - Languages: Chinese and English - License: Apache 2.0 - Cost to run: no API key or paid service is named - Data it reaches: the text you give it and the model files you download from Hugging Face - Not for: chat or assistant use, since this checkpoint has no instruction tuning ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 362M - Weights: about 723 MB in BF16; about 181 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 500 - Likes: 3 - License: apache-2.0 - Languages: en, zh - Quantized versions: 1 - Fine-tunes: 3 - Tags: safetensors, qwen3, mnn-pocket, on-device, base, text-generation, conversational ## Links - Model: https://huggingface.co/taobao-mnn/MNN-Pocket-0.3B-Base - Files: https://huggingface.co/taobao-mnn/MNN-Pocket-0.3B-Base/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download taobao-mnn/MNN-Pocket-0.3B-Base ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [taobao-mnn/MNN-Pocket-0.3B-Base on Hugging Face](https://huggingface.co/taobao-mnn/MNN-Pocket-0.3B-Base) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | MNN-Pocket-0.3B-Base (this page) | 500 | 3 | 362M | apache-2.0 | 2026-11-10 | | [MNN-Pocket-0.3B](https://mindket.com/p/499799ab-3a05-4d11-854c-81e4c4e3e6fa) | 3,397 | 197 | 362M | apache-2.0 | 2026-11-10 | | [MNN-Pocket-0.3B-GGUF](https://mindket.com/p/1e60bd47-21da-4333-9781-f0f82b9a2ebb) | 12,467 | 20 | 362M | apache-2.0 | 2026-11-10 | | [MNN-Pocket-0.3B-MNN](https://mindket.com/p/5eda7b9e-d7d5-4ce9-9728-a0a5171dda47) | 1,303 | 6 | — | apache-2.0 | 2026-11-10 | | [MNN-Pocket-0.3B-Dec](https://mindket.com/p/9ecfcae9-9da9-47fc-9760-cff45374f2d0) | 0 | 4 | — | apache-2.0 | 2026-11-10 | | [KLM 1.5](https://mindket.com/p/bffc34a5-c64b-418e-a26e-f869a269c8c1) | 1,603 | 31 | 99M | apache-2.0 | 2026-11-01 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2026-09-26 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open taobao-mnn/MNN-Pocket-0.3B-Base on Hugging Face](https://huggingface.co/taobao-mnn/MNN-Pocket-0.3B-Base) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/376694fb-a8b0-4174-bac8-b0049742053f) - [Its page on Mindket](https://mindket.com/p/376694fb-a8b0-4174-bac8-b0049742053f)