--- title: "hs-decide-1-flash" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6af342ec-c349-4bbd-95ad-e2f3ef1740b4" updated: "2026-11-12" --- # hs-decide-1-flash > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers typed questions about a text state with a probability for every option in one pass, with no generated text, in eight languages. A 0.8B decision model for the Hindsight tool, served with vLLM or Transformers. hs-decide-1-flash 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 hindsight-ai ## About ### What it is hs-decide-1-flash is a small decision model from hindsight-ai, made for use with Hindsight. It has 0.8B parameters and starts from Qwen3.5-0.8B. You hand it a situation described in text, together with typed questions about the situation. It replies with the likelihood of each possible answer and a confidence figure, all in a single pass, and never produces prose. ### Who it suits Developers who want fast, structured decisions from a small model, such as classifying tickets or choosing between documents, and who use or build on Hindsight. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support engineers | Classify tickets with yes or no questions | Is a ticket a billing problem | | Search and retrieval | Retrieval engineers | Choose the best document for a query | Which document gives the current date | | Agent building | Agent developers | Score how well a document answers a question | Rating a passage on four levels | | Multilingual products | Global teams | Run decisions in eight languages | A decision on a Japanese message | If you want a model that writes text, you can skip it, because it only returns probabilities. ### Where to use it - Hindsight, which it was made for - vLLM, through a plugin in the repository's serving folder - The Transformers library, on a plain CPU, a Mac with Apple chips or a PyTorch-compatible GPU | Use case | Who | What they get back | | --- | --- | --- | | Yes or no checks | Support teams | The probability that the answer is yes | | Choosing among options | Retrieval teams | A probability for each of 2 to 255 options | | Ordered ratings | Quality teams | A score on 2 to 10 ordered levels, with probabilities | ### How to set it up - Python and pip, with Python 3.12 or later for the Transformers route - PyTorch installed for your hardware, for the Transformers route - About 1.5 GB of disk space in BF16, or about 376 MB at 4-bit (an estimate) 1. Install the Hugging Face hub tools and download the model with its serving folder. 2. Install the serving package. 3. Start a server or call the model from Python. ```bash hf download hindsight-ai/hs-decide-1-flash --local-dir hs-decide-1-flash pip install vllm==0.32.0 ./hs-decide-1-flash/serving hindsight-decision-serve hs-decide-1-flash --port 8000 ``` For the Transformers route, fetch the serving folder and install it with its server extra. ```bash hf download hindsight-ai/hs-decide-1-flash --include "serving/*" --local-dir hs-decide-1-flash pip install './hs-decide-1-flash/serving[server]' ``` ### How to use it Load the model in Python, pass a state and a set of typed questions, and read the probabilities. A choice question has 2 to 255 options, a score question has 2 to 10 ordered levels and a noul question is yes or no. ```python from hindsight_decision import DecisionModel model = DecisionModel.from_pretrained("hindsight-ai/hs-decide-1-flash") response = model.answer({ "state": "Ticket: I was charged twice for my March invoice.", "questions": {"billing": {"type": "noul", "instructions": "Is this a billing problem?"}}, }) print(round(response["answers"]["billing"]["noul"], 3)) # 0.997 ``` To serve it to Hindsight with the Transformers route, start a server. It answers one request at a time. ```bash hindsight-decision-serve-transformers hindsight-ai/hs-decide-1-flash --port 8000 --api-key ``` | Tool | What it does | | --- | --- | | DecisionModel.from\_pretrained | Loads the model in Python | | model.answer | Takes a state and typed questions and returns probabilities | | hindsight-decision-serve | Starts a vLLM server for the model | | hindsight-decision-serve-transformers | Starts a Transformers server, one request at a time | ### Example You give it the ticket text about being charged twice for a March invoice, with the question of whether it is a billing problem. The card's run prints about 0.997, the probability of yes. ### For newcomers and experts If you are new to this, run the short Python example first and change the ticket text to see how the probability moves. It needs no server. The model was trained on requests of up to 65,536 tokens and answers all questions about one state in the same pass. If Hindsight's requests time out, the card suggests lowering the maximum request tokens so the server refuses long requests and Hindsight splits them. You write a state → You add typed questions → The model scores every option in one pass → You read the probabilities ### Good to know - Throughput: the Transformers server answers one request at a time - Languages: eight in all, including English, Japanese, Korean and Chinese - License: apache-2.0 - Cost to run: no paid service is named, only your own hardware, and the server can take an API key you set - Data it reaches: downloading fetches model files, and the server reads the requests you send it - Not for: writing text, since it returns probabilities and generates none ## README ````markdown # hs-decide-1-flash Answers typed questions about a text state with a probability for every option in one pass, with no generated text, in eight languages. A 0.8B decision model for the Hindsight tool, served with vLLM or Transformers. Made by hindsight-ai 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: hs-decide-1-flash is a small decision model from hindsight-ai, made for use with Hindsight. It has 0.8B parameters and starts from Qwen3.5-0.8B. You hand it a situation described in text, together with typed questions about the situation. It replies with the likelihood of each possible answer and a confidence figure, all in a single pass, and never produces prose. Who it suits: Developers who want fast, structured decisions from a small model, such as classifying tickets or choosing between documents, and who use or build on Hindsight. ```table Industry | Who uses it | How they use it | Example Customer support | Support engineers | Classify tickets with yes or no questions | Is a ticket a billing problem Search and retrieval | Retrieval engineers | Choose the best document for a query | Which document gives the current date Agent building | Agent developers | Score how well a document answers a question | Rating a passage on four levels Multilingual products | Global teams | Run decisions in eight languages | A decision on a Japanese message ``` If you want a model that writes text, you can skip it, because it only returns probabilities. Where to use it: - Hindsight, which it was made for - vLLM, through a plugin in the repository's serving folder - The Transformers library, on a plain CPU, a Mac with Apple chips or a PyTorch-compatible GPU ```table Use case | Who | What they get back Yes or no checks | Support teams | The probability that the answer is yes Choosing among options | Retrieval teams | A probability for each of 2 to 255 options Ordered ratings | Quality teams | A score on 2 to 10 ordered levels, with probabilities ``` How to set it up: - Python and pip, with Python 3.12 or later for the Transformers route - PyTorch installed for your hardware, for the Transformers route - About 1.5 GB of disk space in BF16, or about 376 MB at 4-bit (an estimate) 1. Install the Hugging Face hub tools and download the model with its serving folder. 2. Install the serving package. 3. Start a server or call the model from Python. ```bash hf download hindsight-ai/hs-decide-1-flash --local-dir hs-decide-1-flash pip install vllm==0.32.0 ./hs-decide-1-flash/serving hindsight-decision-serve hs-decide-1-flash --port 8000 ``` For the Transformers route, fetch the serving folder and install it with its server extra. ```bash hf download hindsight-ai/hs-decide-1-flash --include "serving/*" --local-dir hs-decide-1-flash pip install './hs-decide-1-flash/serving[server]' ``` How to use it: Load the model in Python, pass a state and a set of typed questions, and read the probabilities. A choice question has 2 to 255 options, a score question has 2 to 10 ordered levels and a noul question is yes or no. ```python from hindsight_decision import DecisionModel model = DecisionModel.from_pretrained("hindsight-ai/hs-decide-1-flash") response = model.answer({ "state": "Ticket: I was charged twice for my March invoice.", "questions": {"billing": {"type": "noul", "instructions": "Is this a billing problem?"}}, }) print(round(response["answers"]["billing"]["noul"], 3)) # 0.997 ``` To serve it to Hindsight with the Transformers route, start a server. It answers one request at a time. ```bash hindsight-decision-serve-transformers hindsight-ai/hs-decide-1-flash --port 8000 --api-key ``` ```table Tool | What it does DecisionModel.from_pretrained | Loads the model in Python model.answer | Takes a state and typed questions and returns probabilities hindsight-decision-serve | Starts a vLLM server for the model hindsight-decision-serve-transformers | Starts a Transformers server, one request at a time ``` Example: You give it the ticket text about being charged twice for a March invoice, with the question of whether it is a billing problem. The card's run prints about 0.997, the probability of yes. For newcomers and experts: If you are new to this, run the short Python example first and change the ticket text to see how the probability moves. It needs no server. The model was trained on requests of up to 65,536 tokens and answers all questions about one state in the same pass. If Hindsight's requests time out, the card suggests lowering the maximum request tokens so the server refuses long requests and Hindsight splits them. ```flow You write a state You add typed questions The model scores every option in one pass You read the probabilities ``` Good to know: - Throughput: the Transformers server answers one request at a time - Languages: eight in all, including English, Japanese, Korean and Chinese - License: apache-2.0 - Cost to run: no paid service is named, only your own hardware, and the server can take an API key you set - Data it reaches: downloading fetches model files, and the server reads the requests you send it - Not for: writing text, since it returns probabilities and generates none ## On Hugging Face, 13 November 2026 - Task: text ranking - Library: transformers - Parameters: 752M - Weights: about 1.5 GB in BF16; about 376 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 858 - Likes: 20 - License: apache-2.0 - Languages: en, es, fr, de, pt, ja, ko, zh - Based on: Qwen/Qwen3.5-0.8B - Quantized versions: 1 - Fine-tunes: 2 - Tags: transformers, safetensors, qwen3_5_text, text-generation, decision-model, reranker, agent-memory, vllm, hindsight, text-ranking ## Links - Model: https://huggingface.co/hindsight-ai/hs-decide-1-flash - Files: https://huggingface.co/hindsight-ai/hs-decide-1-flash/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download hindsight-ai/hs-decide-1-flash ``` 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 [hindsight-ai/hs-decide-1-flash on Hugging Face](https://huggingface.co/hindsight-ai/hs-decide-1-flash) ## 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 | | --- | --- | --- | --- | --- | --- | | hs-decide-1-flash (this page) | 858 | 20 | 752M | apache-2.0 | 2026-11-09 | | [hs-rerank-1](https://mindket.com/p/c3b813e1-9df1-49fd-80a2-1a39304f96f1) | 1,148,007 | 6 | 114M | mit | 2026-11-03 | | [AsteroRank-Lepton](https://mindket.com/p/82fba0ab-2599-4f3a-9a2f-94ed37271fe0) | 58 | 7 | 4.8B | cc-by-nc-4.0 | 2026-11-05 | | [CLM-v0.1-8B](https://mindket.com/p/946fad17-f7f0-4cda-8ff3-5155aa69ae3f) | 1,092 | 794 | — | apache-2.0 | 2026-09-24 | | [AsteroRank-Lepton-GGUF](https://mindket.com/p/ab0befc6-e06f-4977-800b-acbcba1b2a94) | 626 | 3 | 4.8B | cc-by-nc-4.0 | 2026-11-05 | | [Mica v0.1 4B](https://mindket.com/p/55daa34c-73de-47b6-a176-900851a69bf1) | 3,436 | 52 | 4.2B | apache-2.0 | 2026-09-28 | 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: 2026-11-09 - First published: 2026-10-28 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open hindsight-ai/hs-decide-1-flash on Hugging Face](https://huggingface.co/hindsight-ai/hs-decide-1-flash) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6af342ec-c349-4bbd-95ad-e2f3ef1740b4) - [Its page on Mindket](https://mindket.com/p/6af342ec-c349-4bbd-95ad-e2f3ef1740b4)