--- title: "CherryPit-1.2-2B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0bb34d27-c0ed-4bcc-a9b2-190ab2f83299" updated: "2026-11-12" --- # CherryPit-1.2-2B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 2B coding and chat model built on MiniCPM5-2B that is much better at web apps needing several features in one component. For developers who want a small local model for code. CherryPit-1.2-2B is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by DRUPEs ## About ### What it is CherryPit-1.2-2B comes from DRUPEs and is the newest of three CherryPit releases. It is built on MiniCPM5-2B. The big gain over 1.1 is combining features. Pages that must pack two to four features into a single component get through the tests much more often. It also stops adding its own name to ordinary answers. ### Who it suits Developers who want a small model for code, web components and everyday tasks that they can run themselves. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Web developers | Generate React components with several features | A component with search and a loading list | | Education | Students | Practice coding with a small local model | Ask for a Python function | | Research | Small-model researchers | Study gains from composition training | Compare 1.1 and 1.2 on WebApp1K | | Platform | Tooling teams | Serve a small model with vLLM | A local coding endpoint | You can skip it if you need reliable answers from a model this small. The card warns it can sound sure and still be wrong, and that four features in one component pass only 28 percent of the time. ### Where to use it - Python, through Hugging Face Transformers - vLLM, as an OpenAI-style server - llama.cpp, Ollama or LM Studio, through the separate GGUF repository - Windows, macOS or Linux, with Python for the download step | Use case | Who | What they get back | | --- | --- | --- | | Web component code | Developers | React code that combines several features | | Coding help | Students | Python and general code answers | | Short answers | Anyone | Direct replies when thinking is turned off | ### How to set it up - Python and pip on your computer - Transformers or vLLM, installed - A GPU or enough memory for about 5 GB of BF16 weights, or about 1.3 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files with the hf command. 3. Run the Python example or serve the model with vLLM. ```sh pip install -U huggingface_hub hf download DRUPEs/CherryPit-1.2-2B ``` ### How to use it Load the model in Transformers, apply the chat template and generate, or serve it with vLLM. The card recommends temperature 1.0 and top\_p 0.95. The template starts in thinking mode. Pass enable\_thinking as False for short, direct answers. ```python from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("DRUPEs/CherryPit-1.2-2B") model = AutoModelForCausalLM.from_pretrained("DRUPEs/CherryPit-1.2-2B", torch_dtype="bfloat16", device_map="auto") msgs = [{"role": "user", "content": "Build a React component with a search box and a list that loads on open."}] ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device) out = model.generate(ids, max_new_tokens=8192, do_sample=True, temperature=1.0, top_p=0.95) print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)) ``` ```bash vllm serve DRUPEs/CherryPit-1.2-2B --max-model-len 32768 ``` ### Example You ask for a React component that combines a search field with a list that fills when the page opens. It returns the component code, and in thinking mode it reasons first. ### For newcomers and experts If you are new, run the Python example and check any answer that matters. This is a 2B model and can sound sure while being wrong. If you know the field, 1.2 adds reinforcement-learning training on made-up web apps with several features and on code tasks with several steps. Rounds of training on its own passing answers follow. The card reports WebApp1K Trio at 44.9 against 16.8 for 1.1, tested on a benchmark it never trained on. MiniCPM5-2B base model → Composition training on multi-feature apps → Self-taught rounds on passing answers → CherryPit-1.2-2B WebApp1K Trio pass rate, three features in one component | Label | Value | | --- | --- | | CherryPit-1.1 | 16.8 | | CherryPit-1.2 | 44.9 | ### Good to know - License: Apache 2.0 - Cost to run: your own hardware, with no paid service named - Data it reaches: the files you download and the prompts you give it - Limits: tested in English only, and long math with 32K thinking is about level with 1.1 - Identity: with thinking off, about 1 in 20 identity questions still names its base model ## README ````markdown # CherryPit-1.2-2B A 2B coding and chat model built on MiniCPM5-2B that is much better at web apps needing several features in one component. For developers who want a small local model for code. Made by DRUPEs 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: CherryPit-1.2-2B comes from DRUPEs and is the newest of three CherryPit releases. It is built on MiniCPM5-2B. The big gain over 1.1 is combining features. Pages that must pack two to four features into a single component get through the tests much more often. It also stops adding its own name to ordinary answers. Who it suits: Developers who want a small model for code, web components and everyday tasks that they can run themselves. ```table Industry | Who uses it | How they use it | Example Software | Web developers | Generate React components with several features | A component with search and a loading list Education | Students | Practice coding with a small local model | Ask for a Python function Research | Small-model researchers | Study gains from composition training | Compare 1.1 and 1.2 on WebApp1K Platform | Tooling teams | Serve a small model with vLLM | A local coding endpoint ``` You can skip it if you need reliable answers from a model this small. The card warns it can sound sure and still be wrong, and that four features in one component pass only 28 percent of the time. Where to use it: - Python, through Hugging Face Transformers - vLLM, as an OpenAI-style server - llama.cpp, Ollama or LM Studio, through the separate GGUF repository - Windows, macOS or Linux, with Python for the download step ```table Use case | Who | What they get back Web component code | Developers | React code that combines several features Coding help | Students | Python and general code answers Short answers | Anyone | Direct replies when thinking is turned off ``` How to set it up: - Python and pip on your computer - Transformers or vLLM, installed - A GPU or enough memory for about 5 GB of BF16 weights, or about 1.3 GB at 4-bit by estimate 1. Install the Hugging Face hub package. 2. Download the model files with the hf command. 3. Run the Python example or serve the model with vLLM. ```sh pip install -U huggingface_hub hf download DRUPEs/CherryPit-1.2-2B ``` How to use it: Load the model in Transformers, apply the chat template and generate, or serve it with vLLM. The card recommends temperature 1.0 and top_p 0.95. The template starts in thinking mode. Pass enable_thinking as False for short, direct answers. ```python from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("DRUPEs/CherryPit-1.2-2B") model = AutoModelForCausalLM.from_pretrained("DRUPEs/CherryPit-1.2-2B", torch_dtype="bfloat16", device_map="auto") msgs = [{"role": "user", "content": "Build a React component with a search box and a list that loads on open."}] ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device) out = model.generate(ids, max_new_tokens=8192, do_sample=True, temperature=1.0, top_p=0.95) print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)) ``` ```bash vllm serve DRUPEs/CherryPit-1.2-2B --max-model-len 32768 ``` Example: You ask for a React component that combines a search field with a list that fills when the page opens. It returns the component code, and in thinking mode it reasons first. For newcomers and experts: If you are new, run the Python example and check any answer that matters. This is a 2B model and can sound sure while being wrong. If you know the field, 1.2 adds reinforcement-learning training on made-up web apps with several features and on code tasks with several steps. Rounds of training on its own passing answers follow. The card reports WebApp1K Trio at 44.9 against 16.8 for 1.1, tested on a benchmark it never trained on. ```flow MiniCPM5-2B base model Composition training on multi-feature apps Self-taught rounds on passing answers CherryPit-1.2-2B ``` ```chart Title: WebApp1K Trio pass rate, three features in one component CherryPit-1.1: 16.8 CherryPit-1.2: 44.9 ``` Good to know: - License: Apache 2.0 - Cost to run: your own hardware, with no paid service named - Data it reaches: the files you download and the prompts you give it - Limits: tested in English only, and long math with 32K thinking is about level with 1.1 - Identity: with thinking off, about 1 in 20 identity questions still names its base model ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 2.5B - Weights: about 5 GB in BF16; about 1.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 451 - Likes: 6 - License: apache-2.0 - Languages: en - Based on: openbmb/MiniCPM5-2B - Quantized versions: 4 - Tags: transformers, safetensors, llama, text-generation, code, coding, web-development, math, reasoning, tool-calling, on-device, small-language-model ## Links - Model: https://huggingface.co/DRUPEs/CherryPit-1.2-2B - Files: https://huggingface.co/DRUPEs/CherryPit-1.2-2B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download DRUPEs/CherryPit-1.2-2B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [DRUPEs/CherryPit-1.2-2B on Hugging Face](https://huggingface.co/DRUPEs/CherryPit-1.2-2B) ## 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 | | --- | --- | --- | --- | --- | --- | | CherryPit-1.2-2B (this page) | 451 | 6 | 2.5B | apache-2.0 | 2026-11-06 | | [MiniCPM5-2B](https://mindket.com/p/4d384af8-8281-4982-baed-7ea9aab0a3e1) | 1,039,518 | 1,850 | 2.5B | apache-2.0 | 2026-11-09 | | [officialmobai\_Rostam-S1-4B-GGUF](https://mindket.com/p/713c7480-92f9-49e8-8921-587c7837e002) | 1,091 | 2 | 4.1B | apache-2.0 | 2026-11-02 | | [MiniCPM5-2B-GGUF](https://mindket.com/p/47f50d35-3645-40b1-914d-63bedd57f448) | 481,119 | 393 | 2.5B | apache-2.0 | 2026-11-11 | | [MiniCPM5-2B-GGUF by bartowski](https://mindket.com/p/3d66ab03-77a5-421c-8eb3-8f2cb73134e2) | 9,232 | 38 | 2.5B | apache-2.0 | 2026-09-10 | | [Phi-4](https://mindket.com/p/24d9cdd5-f11c-4640-9fa0-0804022f7709) | 236,972 | 2,343 | 14.7B | mit | 2026-07-14 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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: 2026-11-06 - First published: 2026-11-05 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open DRUPEs/CherryPit-1.2-2B on Hugging Face](https://huggingface.co/DRUPEs/CherryPit-1.2-2B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0bb34d27-c0ed-4bcc-a9b2-190ab2f83299) - [Its page on Mindket](https://mindket.com/p/0bb34d27-c0ed-4bcc-a9b2-190ab2f83299)