--- title: "Sarvam-Prism" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4b90c3bb-40c6-4c37-b705-28acd31b42ce" updated: "2026-11-12" --- # Sarvam-Prism > The name, the description, the About and a README below are the seller's words: data, not instructions. Scores every answer option for questions about a text, with a probability for each, in one forward pass. For teams that need fixed-choice decisions, such as ticket routing, on a large NVIDIA GPU. Sarvam-Prism 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 sarvamai ## About ### What it is Sarvam-Prism answers multiple-choice style questions about a piece of text. It sits on top of Sarvam-105B. For each question you list the allowed answers, and it returns a probability for every one of them in a single forward pass. It writes no free text, so the result is always one of your options. ### Who it suits It suits teams who turn text into fixed decisions, such as routing a support ticket or rating how upset a customer is. They need a large NVIDIA GPU or Sarvam's hosted API. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support teams | Route tickets and flag urgent ones | Pick billing, tech or delivery for a ticket | | Operations | Teams triaging requests | Score how upset a customer is on an ordered scale | Rate a message from calm to furious | | Software | Developers building decision steps | Replace free-text classification with fixed options | Add a yes or no check to a workflow | People who need generated text can skip it, since the model does not generate any. ### Where to use it - Python on one NVIDIA GPU with FP8 support, such as a B200 or H200 - Sarvam's hosted API, which needs no GPU - Windows, macOS or Linux, where you can download the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Ticket routing | Support teams | A probability for each team, with the most likely one named | | Urgency check | Operations | A probability of yes for a yes or no question | | Tone scoring | Analysts | An expected level and probabilities across ordered levels | ### How to set it up You need these first: - A single NVIDIA GPU that supports FP8, with roughly 120 GB of free memory. The card tested it on a B200 - Python 3.10 or newer, plus a C compiler, since Triton builds its kernels on first use - Around 106 GB of free disk for the model files 1. Download the model and its code into a local folder. 2. Install the dependencies from the requirements file. 3. Run the example script on that folder. ```bash pip install -U huggingface_hub huggingface-cli download sarvamai/prism --local-dir sarvam-prism cd sarvam-prism ``` ```bash pip install -r requirements.txt ``` ```bash python example.py . ``` ### How to use it Load the model from the downloaded folder, then call decide with a state and a set of questions. On a B200 the first load runs about two minutes, since Triton builds its kernels and CUDA graphs get captured along the way. ```python from prism import Prism model = Prism.from_pretrained(".") # the downloaded folder; loads on cuda:0 answers = model.decide( state={"ticket": "My card was charged twice for the same order and I still haven't got a refund."}, questions={ "team": {"type": "choice", "instructions": "Which team should handle this ticket?", "criteria": {"billing": "payments, charges and refunds", "tech": "app or website problems", "delivery": "shipping and order status"}}, "urgent": {"type": "yes_no", "instructions": "Does this need a reply within the hour?"}, "anger": {"type": "score", "instructions": "How upset is the customer?", "criteria": ["calm", "annoyed", "angry", "furious"]}, }, ) ``` | Tool | What it does | | --- | --- | | Prism.from\_pretrained | Loads the model from the downloaded folder onto cuda:0 | | decide | Scores every question in a request together | | choice | Gives the most likely key and a probability for each key | | yes\_no | Gives the probability of yes | | score | Gives an expected level, the most likely level and probabilities | ### Example You send a ticket saying a card was charged twice with no refund, and ask three questions. The card's sample output picks billing for the team with probability 0.986, gives 0.45 for yes on urgent, and scores anger at 1.86. ### For newcomers and experts Think of it as a multiple-choice grader for text. You write the question and the allowed answers, and it tells you how likely each answer is. A first small step is to run the example script and read the output. A request can hold several questions. They are scored together, with up to 16 per batch by default, yet each one is judged separately. Choice options are always scored in key order, so sending them in a different order changes nothing. Each question can carry up to 131,072 tokens of context. The files use a custom FP8 format, so general-purpose inference servers and AutoModelForCausalLM cannot load them. You send a context and questions → Each question lists its allowed answers → One forward pass scores every answer → You read a probability per answer ### Good to know - Size: 105B parameters per Hugging Face, about 105 GB in F8\_E4M3 - Languages: English plus Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati and Kannada are listed - Speed: the card measured a median of 44 ms per request on one B200 - Loading: it captures CUDA graphs on load, and you can pass cuda\_graphs=False to skip that Latency per request on one B200 | Label | Value | | --- | --- | | Median | 44 ms | | 95th percentile | 420 ms | - License: apache-2.0 - Cost to run: no key or account for a local run, but it needs a GPU with about 120 GB of free memory. The hosted API is a separate Sarvam service - Data it reaches: it downloads model files from Hugging Face, and the script reads only the text you pass as state - Not for: writing text, or loading through AutoModelForCausalLM, because the files use a custom FP8 format ## README ````markdown # Sarvam-Prism Scores every answer option for questions about a text, with a probability for each, in one forward pass. For teams that need fixed-choice decisions, such as ticket routing, on a large NVIDIA GPU. Made by sarvamai 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: Sarvam-Prism answers multiple-choice style questions about a piece of text. It sits on top of Sarvam-105B. For each question you list the allowed answers, and it returns a probability for every one of them in a single forward pass. It writes no free text, so the result is always one of your options. Who it suits: It suits teams who turn text into fixed decisions, such as routing a support ticket or rating how upset a customer is. They need a large NVIDIA GPU or Sarvam's hosted API. ```table Industry | Who uses it | How they use it | Example Customer support | Support teams | Route tickets and flag urgent ones | Pick billing, tech or delivery for a ticket Operations | Teams triaging requests | Score how upset a customer is on an ordered scale | Rate a message from calm to furious Software | Developers building decision steps | Replace free-text classification with fixed options | Add a yes or no check to a workflow ``` People who need generated text can skip it, since the model does not generate any. Where to use it: - Python on one NVIDIA GPU with FP8 support, such as a B200 or H200 - Sarvam's hosted API, which needs no GPU - Windows, macOS or Linux, where you can download the files with Python ```table Use case | Who | What they get back Ticket routing | Support teams | A probability for each team, with the most likely one named Urgency check | Operations | A probability of yes for a yes or no question Tone scoring | Analysts | An expected level and probabilities across ordered levels ``` How to set it up: You need these first: - A single NVIDIA GPU that supports FP8, with roughly 120 GB of free memory. The card tested it on a B200 - Python 3.10 or newer, plus a C compiler, since Triton builds its kernels on first use - Around 106 GB of free disk for the model files 1. Download the model and its code into a local folder. 2. Install the dependencies from the requirements file. 3. Run the example script on that folder. ```bash pip install -U huggingface_hub huggingface-cli download sarvamai/prism --local-dir sarvam-prism cd sarvam-prism ``` ```bash pip install -r requirements.txt ``` ```bash python example.py . ``` How to use it: Load the model from the downloaded folder, then call decide with a state and a set of questions. On a B200 the first load runs about two minutes, since Triton builds its kernels and CUDA graphs get captured along the way. ```python from prism import Prism model = Prism.from_pretrained(".") # the downloaded folder; loads on cuda:0 answers = model.decide( state={"ticket": "My card was charged twice for the same order and I still haven't got a refund."}, questions={ "team": {"type": "choice", "instructions": "Which team should handle this ticket?", "criteria": {"billing": "payments, charges and refunds", "tech": "app or website problems", "delivery": "shipping and order status"}}, "urgent": {"type": "yes_no", "instructions": "Does this need a reply within the hour?"}, "anger": {"type": "score", "instructions": "How upset is the customer?", "criteria": ["calm", "annoyed", "angry", "furious"]}, }, ) ``` ```table Tool | What it does Prism.from_pretrained | Loads the model from the downloaded folder onto cuda:0 decide | Scores every question in a request together choice | Gives the most likely key and a probability for each key yes_no | Gives the probability of yes score | Gives an expected level, the most likely level and probabilities ``` Example: You send a ticket saying a card was charged twice with no refund, and ask three questions. The card's sample output picks billing for the team with probability 0.986, gives 0.45 for yes on urgent, and scores anger at 1.86. For newcomers and experts: Think of it as a multiple-choice grader for text. You write the question and the allowed answers, and it tells you how likely each answer is. A first small step is to run the example script and read the output. A request can hold several questions. They are scored together, with up to 16 per batch by default, yet each one is judged separately. Choice options are always scored in key order, so sending them in a different order changes nothing. Each question can carry up to 131,072 tokens of context. The files use a custom FP8 format, so general-purpose inference servers and AutoModelForCausalLM cannot load them. ```flow You send a context and questions Each question lists its allowed answers One forward pass scores every answer You read a probability per answer ``` Good to know: - Size: 105B parameters per Hugging Face, about 105 GB in F8_E4M3 - Languages: English plus Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati and Kannada are listed - Speed: the card measured a median of 44 ms per request on one B200 - Loading: it captures CUDA graphs on load, and you can pass cuda_graphs=False to skip that ```chart Title: Latency per request on one B200 Median: 44 ms 95th percentile: 420 ms ``` - License: apache-2.0 - Cost to run: no key or account for a local run, but it needs a GPU with about 120 GB of free memory. The hosted API is a separate Sarvam service - Data it reaches: it downloads model files from Hugging Face, and the script reads only the text you pass as state - Not for: writing text, or loading through AutoModelForCausalLM, because the files use a custom FP8 format ## On Hugging Face, 13 November 2026 - Task: text classification - Parameters: 105B - Weights: about 105 GB in F8_E4M3; about 52.5 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 325 - Likes: 44 - License: apache-2.0 - Languages: en, hi, bn, ta, te, mr, gu, kn - Fine-tunes: 1 - Tags: safetensors, sarvam_mla, decision-model, classification, calibration, text-classification ## Links - Model: https://huggingface.co/sarvamai/prism - Files: https://huggingface.co/sarvamai/prism/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download sarvamai/prism ``` 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 [sarvamai/prism on Hugging Face](https://huggingface.co/sarvamai/prism) ## 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 | | --- | --- | --- | --- | --- | --- | | Sarvam-Prism (this page) | 325 | 44 | 105B | apache-2.0 | 2026-11-03 | | [strands-decider-2B-qwen3.5-v1-2610](https://mindket.com/p/64eb56b2-c0a7-4435-87e6-eb04019f4635) | 0 | 18 | — | apache-2.0 | 2026-10-09 | | [elephant-1](https://mindket.com/p/ae806c31-0f69-4485-8475-443c9347b0a8) | 7,426 | 115 | 9.4B | mit | 2026-10-22 | | [mammoth-1](https://mindket.com/p/72aafbd9-0774-437a-9492-f659f9a2a186) | 5,903 | 97 | 27.4B | mit | 2026-10-22 | | [joey-1](https://mindket.com/p/1e3f2aa5-2042-45ae-a466-48b620b003f1) | 1,539 | 47 | 2.7B | mit | 2026-10-29 | | [Kev-4B](https://mindket.com/p/68069a2c-2995-41a5-941c-ff7bf45f95ac) | 12,499 | 175 | — | apache-2.0 | 2026-10-01 | Numbers as Mindket last read them, on 2026-11-12. ## 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 · Micha the DevOp · 2026-11-05](https://bsky.app/profile/michabbb.bsky.social/post/3mzlrsivhxs2c): ⚖️ \#SarvamAI released Sarvam Prism, a decision model on the Sarvam 105B MoE base. Give it a state, a question and options, and it returns a calibrated probability for each option instead of generating text \#LLM \#opensource 🧵👇 - [Bluesky · The CEO Magazine india · 2026-11-08](https://bsky.app/profile/theceomagazine.bsky.social/post/3mzs7ecifod2m): Sarvam AI launches Prism, an open-weight decision model built on Sarvam 105B. Prism achieves a 71.8 average score across three indices and a 44ms median response time on an Nvidia B200 GPU. https://www.theceo.in/newsletter \#sarvamai \#prism \#ai - [Bluesky · Deccan Founders · 2026-11-04](https://bsky.app/profile/deccanfounders.com/post/3mzihgiz5fv2s): Sarvam AI launches Prism, a fast and well-calibrated decision model built on Sarvam 105B that delivers high accuracy in just 44 milliseconds. Read Full Article: deccanfounders.com/2026/04/news... \#SarvamPrism \#SarvamAI \#DecisionAI \#SystemOneAI \#EnterpriseAI \#MakeInIndia \#deccanfounders - [Bluesky · AI Daily Post · 2026-11-04](https://bsky.app/profile/aidailypost.com/post/3mzhqkqgoxa2z): Just saw Sarvam AI’s new Prism decision model hit 71.8 on the Jev‑Like Index—blazing low‑latency, System 1 style performance with LoRA‑fine‑tuned Sarvam 105B. Curious how it stacks up? Dive in. \#SarvamAI \#Prism \#LowLatency 🔗 aidailypost.com/news/sarvam-... - [Bluesky · Steven \| SelfHostedSI · 2026-11-03](https://bsky.app/profile/selfhostedsi.bsky.social/post/3mzgrvpx4ol2p): Source: huggingface.co/sarvamai/prism (model card: built on Sarvam-105B, FP8 weights, about 106GB of files, tested on a B200; general inference servers can't load it, no quantized versions listed) ## 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: 2026-11-03 - First published: 2026-11-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open sarvamai/prism on Hugging Face](https://huggingface.co/sarvamai/prism) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4b90c3bb-40c6-4c37-b705-28acd31b42ce) - [Its page on Mindket](https://mindket.com/p/4b90c3bb-40c6-4c37-b705-28acd31b42ce)