--- title: "Bespoke-Nimble-9B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a0c3aad0-46f1-4cc1-a072-eae76e0a9933" updated: "2026-11-13" --- # Bespoke-Nimble-9B > The name, the description, the About and a README below are the seller's words: data, not instructions. Scores yes or no questions, multiple-choice picks and rubric levels against a piece of text, using a LoRA adapter on Qwen3.5-9B. For developers who need a classifier that returns probabilities instead of free text. Bespoke-Nimble-9B 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 bespokelabs ## About ### What it is Bespoke-Nimble-9B is a LoRA adapter for the Qwen3.5-9B model. You give it a block of text and a list of allowed answers, and it scores each answer directly. It does not write explanations or free-form replies. It handles three kinds of fields: a choice from a list, a true or false value, and a numbered rubric level. The result includes the chosen answer and the probability of each option. ### Who it suits Developers who want a classifier that grounds its answer in text they supply, for example a policy check or a quality grade, and who have a CUDA GPU. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Retail | Support engineers | Check a customer case against a written policy | Is this item inside the return window | | Content | Editors and reviewers | Grade a text against a rubric | Giving a draft a score from a small numbered scale | | Software | Backend developers | Route a request to one of a set of labels | Choosing a category from a fixed list | | Operations | Analysts | Turn a text record into a yes or no flag | Marking whether a record meets a stated rule | Anyone who wants chat answers, explanations or long generated text can skip it, since this adapter only scores the options you list. ### Where to use it - Python on a machine with a CUDA GPU that supports BF16. - Windows, macOS or Linux with Python, for downloading the files. - Mac and other loaders are covered in the project's own quickstart. | Use case | Who | What they get back | | --- | --- | --- | | Policy check | Support teams | A true or false answer with its probability | | Rubric grading | Reviewers | A chosen level and an expected score weighted by probability | | Label picking | Developers | One choice from up to 255 options with probabilities | ### How to set it up You need the base model and a suitable GPU environment. - A CUDA GPU with BF16 support - A CUDA build of PyTorch, version 2.8.0 on CUDA 12.8 in the authors' own run - Access to the Qwen3.5-9B base weights, which this repository does not include - A Hugging Face token only if the repository is private 1. Download the repository into a local folder. 2. Install the pinned requirements from that folder. 3. Review the included helper before you import it. It does not need trust\_remote\_code. ```bash pip install -U huggingface_hub hf download bespokelabs/Bespoke-Nimble-9B --local-dir nimble-model pip install -r nimble-model/requirements.txt ``` ### How to use it Import the helper, load the model from the folder, then call score with a context and a schema describing the fields you want answered. ```python import sys sys.path.insert(0, "nimble-model") from inference import NimbleModel model = NimbleModel("nimble-model") result = model.score( context="The store accepts returns within 30 days. This item was bought 12 days ago.", schema={ "eligible": { "type": "boolean", "description": "Is this item within the store return window?" } }, ) print(result["output"]) print(result["fields"]["eligible"]["probabilities"]) ``` | Tool | What it does | | --- | --- | | NimbleModel | Loads the adapter and its tokenizer from the local folder | | score | Scores each field in your schema against the context | | type enum with choices | Defines a field whose answer is one item from a list of strings | | score\_fields | Names the rubric fields that return an integer and an expected score | ### Example Pass a short note saying returns are accepted for 30 days and the item was bought 12 days ago, with a true or false field named eligible. You get back the chosen output and the probability the model gives each answer for that field. ### For newcomers and experts If you are new, think of it as a judge that picks from options you define. Try the return-window sample first with a boolean field, then add a choice list once that works. For experts, it scores the allowed answer tokens directly and applies a softmax over the candidate logits once, with a default temperature of 1.0. Fields are handled one at a time. Each field takes at most 255 choices, and a prompt over 8,192 tokens is rejected instead of cut. The card says this checkpoint has no separate temperature calibration, so do not reuse the older model's value. Define a context and a schema of fields → Call score on the loaded model → The adapter scores each allowed answer → A softmax gives probabilities per option → You read the output and the probabilities ### Good to know The points below cover limits and costs. - Hardware: a CUDA GPU with BF16 support is expected. - License: Apache 2.0. - Cost to run: no API key or paid service is named. A Hugging Face token is needed only for a private repository, and you need a capable GPU. - Data it reaches: it reads the context you pass in. The base Qwen3.5-9B weights come from Hugging Face and are not copied into this repository. - Not for: free-form answers or generated reasoning, prompts over 8,192 tokens, or more than 255 choices in one field. ## README ````markdown # Bespoke-Nimble-9B Scores yes or no questions, multiple-choice picks and rubric levels against a piece of text, using a LoRA adapter on Qwen3.5-9B. For developers who need a classifier that returns probabilities instead of free text. Made by bespokelabs 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: Bespoke-Nimble-9B is a LoRA adapter for the Qwen3.5-9B model. You give it a block of text and a list of allowed answers, and it scores each answer directly. It does not write explanations or free-form replies. It handles three kinds of fields: a choice from a list, a true or false value, and a numbered rubric level. The result includes the chosen answer and the probability of each option. Who it suits: Developers who want a classifier that grounds its answer in text they supply, for example a policy check or a quality grade, and who have a CUDA GPU. ```table Industry | Who uses it | How they use it | Example Retail | Support engineers | Check a customer case against a written policy | Is this item inside the return window Content | Editors and reviewers | Grade a text against a rubric | Giving a draft a score from a small numbered scale Software | Backend developers | Route a request to one of a set of labels | Choosing a category from a fixed list Operations | Analysts | Turn a text record into a yes or no flag | Marking whether a record meets a stated rule ``` Anyone who wants chat answers, explanations or long generated text can skip it, since this adapter only scores the options you list. Where to use it: - Python on a machine with a CUDA GPU that supports BF16. - Windows, macOS or Linux with Python, for downloading the files. - Mac and other loaders are covered in the project's own quickstart. ```table Use case | Who | What they get back Policy check | Support teams | A true or false answer with its probability Rubric grading | Reviewers | A chosen level and an expected score weighted by probability Label picking | Developers | One choice from up to 255 options with probabilities ``` How to set it up: You need the base model and a suitable GPU environment. - A CUDA GPU with BF16 support - A CUDA build of PyTorch, version 2.8.0 on CUDA 12.8 in the authors' own run - Access to the Qwen3.5-9B base weights, which this repository does not include - A Hugging Face token only if the repository is private 1. Download the repository into a local folder. 2. Install the pinned requirements from that folder. 3. Review the included helper before you import it. It does not need trust_remote_code. ```bash pip install -U huggingface_hub hf download bespokelabs/Bespoke-Nimble-9B --local-dir nimble-model pip install -r nimble-model/requirements.txt ``` How to use it: Import the helper, load the model from the folder, then call score with a context and a schema describing the fields you want answered. ```python import sys sys.path.insert(0, "nimble-model") from inference import NimbleModel model = NimbleModel("nimble-model") result = model.score( context="The store accepts returns within 30 days. This item was bought 12 days ago.", schema={ "eligible": { "type": "boolean", "description": "Is this item within the store return window?" } }, ) print(result["output"]) print(result["fields"]["eligible"]["probabilities"]) ``` ```table Tool | What it does NimbleModel | Loads the adapter and its tokenizer from the local folder score | Scores each field in your schema against the context type enum with choices | Defines a field whose answer is one item from a list of strings score_fields | Names the rubric fields that return an integer and an expected score ``` Example: Pass a short note saying returns are accepted for 30 days and the item was bought 12 days ago, with a true or false field named eligible. You get back the chosen output and the probability the model gives each answer for that field. For newcomers and experts: If you are new, think of it as a judge that picks from options you define. Try the return-window sample first with a boolean field, then add a choice list once that works. For experts, it scores the allowed answer tokens directly and applies a softmax over the candidate logits once, with a default temperature of 1.0. Fields are handled one at a time. Each field takes at most 255 choices, and a prompt over 8,192 tokens is rejected instead of cut. The card says this checkpoint has no separate temperature calibration, so do not reuse the older model's value. ```flow Define a context and a schema of fields Call score on the loaded model The adapter scores each allowed answer A softmax gives probabilities per option You read the output and the probabilities ``` Good to know: The points below cover limits and costs. - Hardware: a CUDA GPU with BF16 support is expected. - License: Apache 2.0. - Cost to run: no API key or paid service is named. A Hugging Face token is needed only for a private repository, and you need a capable GPU. - Data it reaches: it reads the context you pass in. The base Qwen3.5-9B weights come from Hugging Face and are not copied into this repository. - Not for: free-form answers or generated reasoning, prompts over 8,192 tokens, or more than 255 choices in one field. ## On Hugging Face, 13 November 2026 - Task: text classification - Library: peft - Formats: Safetensors - Downloads in the last 30 days: 1,027 - Likes: 254 - License: apache-2.0 - Languages: en - Based on: Qwen/Qwen3.5-9B - Tags: peft, safetensors, lora, qwen3.5, text-classification, structured-prediction, evidence-grounding ## Links - Model: https://huggingface.co/bespokelabs/Bespoke-Nimble-9B - Files: https://huggingface.co/bespokelabs/Bespoke-Nimble-9B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download bespokelabs/Bespoke-Nimble-9B ``` 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 [bespokelabs/Bespoke-Nimble-9B on Hugging Face](https://huggingface.co/bespokelabs/Bespoke-Nimble-9B) ## 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 | | --- | --- | --- | --- | --- | --- | | Bespoke-Nimble-9B (this page) | 1,027 | 254 | — | apache-2.0 | 2026-09-24 | | [Kev-4B](https://mindket.com/p/68069a2c-2995-41a5-941c-ff7bf45f95ac) | 12,499 | 175 | — | apache-2.0 | 2026-10-01 | | [strands-decider-2B-qwen3.5-v1-2610](https://mindket.com/p/64eb56b2-c0a7-4435-87e6-eb04019f4635) | 0 | 18 | — | apache-2.0 | 2026-10-09 | | [Qwen3.8-27b-Terse-Coder-LoRA](https://mindket.com/p/11999adf-4218-4015-bc5b-fe08679f385f) | 244 | 6 | 117M | apache-2.0 | 2026-09-23 | | [K2-Horizon-7B-Uno](https://mindket.com/p/4f8072eb-e9bd-4294-8bcf-f9303cdd68a3) | 203 | 113 | — | apache-2.0 | 2026-09-05 | | [LocalAlign\_llama3.1\_8B](https://mindket.com/p/d03fc047-365a-4c7b-af13-dca3cf027809) | 156 | 9 | — | llama3.1 | 2026-09-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · hrbrmstr 🇺🇦 🇬🇱 🇨🇦🇧🇪 4 · 2026-10-17](https://bsky.app/profile/hrbrmstr.mastodon.social.ap.brid.gy/post/3my34ujr6rsw2): Nimble's 9B (v3) recently released/updated decision/sysem one model is beating the 27b ones on cyber tests quality-wise (it is a tad slower at least on the Spark, tho). https://huggingface.co/bespokelabs/Bespoke-Nimble-9B-v3 ## 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: 2026-09-24 - First published: 2026-09-18 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open bespokelabs/Bespoke-Nimble-9B on Hugging Face](https://huggingface.co/bespokelabs/Bespoke-Nimble-9B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a0c3aad0-46f1-4cc1-a072-eae76e0a9933) - [Its page on Mindket](https://mindket.com/p/a0c3aad0-46f1-4cc1-a072-eae76e0a9933)