--- title: "Agaram 2.5B" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/82101e4f-6f63-4064-b2af-09a2ce997919" updated: "2026-11-13" --- # Agaram 2.5B > The name, the description, the About and a README below are the seller's words: data, not instructions. Chats in Tamil script, romanized Tamil and English, and translates between Tamil and English. A 2.5B model built from scratch by Murai Labs, for Tamil speakers, researchers and developers. Agaram 2.5B is a kit in the Education 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 murai-labs ## About ### What it is Agaram 2.5B is a chat model with 2.5 billion parameters for Tamil and English. Murai Labs and Ramchand Kumaresan built it from scratch, and the card says it uses no weights from other models. It is a non-profit research model for chatting in Tamil script, romanized Tamil and English, and for translating between Tamil and English. This repository holds the chat version, made from the pretrained base with one supervised fine-tuning run. ### Who it suits It suits Tamil speakers and builders of Tamil tools who want a small model that handles Tamil, romanized Tamil and English in one place. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Tamil learners and teachers | Chat and practise in Tamil or romanized Tamil | Ask a question in romanized Tamil and read the reply | | Language tech | Developers | Build Tamil and English chat or translation features | Send a Tamil sentence and request an English version | | Research | Language researchers | Study a model trained from scratch with mostly Tamil data | Test how it handles Tamil prompts | If you need other languages or long inputs, you can skip it, since the card lists only Tamil and English and a context of up to 8,192 tokens. ### Where to use it You run it in Python with the transformers library, on Windows, macOS or Linux. The card also points to an online chat demo and a chat command line tool in its GitHub project. - Python scripts that use transformers 5.x - Windows, macOS or Linux with the huggingface\_hub package - The online chat demo linked from the card | Use case | Who | What they get back | | --- | --- | --- | | Tamil chat | Learners and users | Replies in Tamil, romanized Tamil or English | | Translation | Developers | Tamil to English or English to Tamil text | | Tool calls | Developers | A tool request in a set format, with results sent back as messages | ### How to set it up You need Python, the transformers library at version 5.x, torch, and about 5.1 GB of space for the BF16 weights. The card says the 5.x format is needed to store the rotary base. - Python on Windows, macOS or Linux - transformers 5.x and torch - About 5.1 GB of free space 1. Install the Hugging Face hub package. 2. Download the model files. 3. Run the card's Python example. ```sh pip install -U huggingface_hub hf download murai-labs/agaram-2.5b-instruct ``` ### How to use it Load the tokenizer and model, build a chat message, and generate with greedy decoding, up to 512 new tokens. No system prompt is needed. The stop token has id 2. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer repo = "murai-labs/agaram-2.5b-instruct" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto") messages = [{"role": "user", "content": "Pencil-la thappa ezhudhiten. Adhai azhikka edhai use pannalaam?"}] ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True)["input_ids"] out = model.generate(ids.to(model.device), max_new_tokens=512, do_sample=False, eos_token_id=2) print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True)) ``` | Tool | What it does | | --- | --- | | AutoTokenizer.from\_pretrained | Loads the tokenizer made for this model | | AutoModelForCausalLM.from\_pretrained | Loads the weights in bfloat16 | | apply\_chat\_template | Builds the chat prompt, and takes a tools list for tool calls | | generate | Produces the reply with greedy decoding | ### Example You send the card's romanized Tamil question about what to use to erase a pencil mistake. The model returns its answer as text, which the script decodes and prints. ### For newcomers and experts If you are new to this, it works like a chat assistant that understands Tamil, including Tamil typed in English letters. A first small step is to run the card's example script and then swap in your own question. If you know the field, it is a 28-layer dense Transformer that normalizes before each block, with RMSNorm, SwiGLU layers and rotary position encoding. Its config borrows the Llama class name because that code can load the design. It has a byte-level BPE tokenizer with 64,000 tokens trained for it, about 50B pretraining tokens, and a context of 4,096 tokens, raised to 8,192 in the decay stage and fine-tuning. Tamil-heavy pretraining data → Base model trained from scratch → One supervised fine-tuning run → Chat model for Tamil and English ### Good to know Part of the chat data was written by an open-weight teacher model, and the card lists exceptions under its disclosures section. Its chat command line tool adds a crisis-resource footer. Pretraining mix by share of tokens | Label | Value | | --- | --- | | Tamil | 58.2 percent | | English | 19.8 percent | | Math | 10 percent | | Code | 7 percent | | Instruction | 3 percent | | Parallel text | 2 percent | - License: Apache-2.0, with the upstream data terms described in the card - Cost to run: no API key or paid service is named, and the weights are about 5.1 GB in BF16 - Data it reaches: the download comes from Hugging Face, and the model works on the text you give it on your own machine - Not for: languages beyond Tamil and English, or inputs beyond its 8,192-token context ## README ````markdown # Agaram 2.5B Chats in Tamil script, romanized Tamil and English, and translates between Tamil and English. A 2.5B model built from scratch by Murai Labs, for Tamil speakers, researchers and developers. Made by murai-labs 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: Agaram 2.5B is a chat model with 2.5 billion parameters for Tamil and English. Murai Labs and Ramchand Kumaresan built it from scratch, and the card says it uses no weights from other models. It is a non-profit research model for chatting in Tamil script, romanized Tamil and English, and for translating between Tamil and English. This repository holds the chat version, made from the pretrained base with one supervised fine-tuning run. Who it suits: It suits Tamil speakers and builders of Tamil tools who want a small model that handles Tamil, romanized Tamil and English in one place. ```table Industry | Who uses it | How they use it | Example Education | Tamil learners and teachers | Chat and practise in Tamil or romanized Tamil | Ask a question in romanized Tamil and read the reply Language tech | Developers | Build Tamil and English chat or translation features | Send a Tamil sentence and request an English version Research | Language researchers | Study a model trained from scratch with mostly Tamil data | Test how it handles Tamil prompts ``` If you need other languages or long inputs, you can skip it, since the card lists only Tamil and English and a context of up to 8,192 tokens. Where to use it: You run it in Python with the transformers library, on Windows, macOS or Linux. The card also points to an online chat demo and a chat command line tool in its GitHub project. - Python scripts that use transformers 5.x - Windows, macOS or Linux with the huggingface_hub package - The online chat demo linked from the card ```table Use case | Who | What they get back Tamil chat | Learners and users | Replies in Tamil, romanized Tamil or English Translation | Developers | Tamil to English or English to Tamil text Tool calls | Developers | A tool request in a set format, with results sent back as messages ``` How to set it up: You need Python, the transformers library at version 5.x, torch, and about 5.1 GB of space for the BF16 weights. The card says the 5.x format is needed to store the rotary base. - Python on Windows, macOS or Linux - transformers 5.x and torch - About 5.1 GB of free space 1. Install the Hugging Face hub package. 2. Download the model files. 3. Run the card's Python example. ```sh pip install -U huggingface_hub hf download murai-labs/agaram-2.5b-instruct ``` How to use it: Load the tokenizer and model, build a chat message, and generate with greedy decoding, up to 512 new tokens. No system prompt is needed. The stop token has id 2. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer repo = "murai-labs/agaram-2.5b-instruct" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto") messages = [{"role": "user", "content": "Pencil-la thappa ezhudhiten. Adhai azhikka edhai use pannalaam?"}] ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True)["input_ids"] out = model.generate(ids.to(model.device), max_new_tokens=512, do_sample=False, eos_token_id=2) print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True)) ``` ```table Tool | What it does AutoTokenizer.from_pretrained | Loads the tokenizer made for this model AutoModelForCausalLM.from_pretrained | Loads the weights in bfloat16 apply_chat_template | Builds the chat prompt, and takes a tools list for tool calls generate | Produces the reply with greedy decoding ``` Example: You send the card's romanized Tamil question about what to use to erase a pencil mistake. The model returns its answer as text, which the script decodes and prints. For newcomers and experts: If you are new to this, it works like a chat assistant that understands Tamil, including Tamil typed in English letters. A first small step is to run the card's example script and then swap in your own question. If you know the field, it is a 28-layer dense Transformer that normalizes before each block, with RMSNorm, SwiGLU layers and rotary position encoding. Its config borrows the Llama class name because that code can load the design. It has a byte-level BPE tokenizer with 64,000 tokens trained for it, about 50B pretraining tokens, and a context of 4,096 tokens, raised to 8,192 in the decay stage and fine-tuning. ```flow Tamil-heavy pretraining data Base model trained from scratch One supervised fine-tuning run Chat model for Tamil and English ``` Good to know: Part of the chat data was written by an open-weight teacher model, and the card lists exceptions under its disclosures section. Its chat command line tool adds a crisis-resource footer. ```chart Title: Pretraining mix by share of tokens Tamil: 58.2 percent English: 19.8 percent Math: 10 percent Code: 7 percent Instruction: 3 percent Parallel text: 2 percent ``` - License: Apache-2.0, with the upstream data terms described in the card - Cost to run: no API key or paid service is named, and the weights are about 5.1 GB in BF16 - Data it reaches: the download comes from Hugging Face, and the model works on the text you give it on your own machine - Not for: languages beyond Tamil and English, or inputs beyond its 8,192-token context ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 2.5B - Weights: about 5.1 GB in BF16; about 1.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,296 - Likes: 11 - License: apache-2.0 - Languages: ta, en - Based on: murai-labs/agaram-2.5b - Quantized versions: 1 - Tags: transformers, safetensors, llama, text-generation, tamil, tanglish, agaram, chatml, from-scratch, conversational ## Links - Model: https://huggingface.co/murai-labs/agaram-2.5b-instruct - Files: https://huggingface.co/murai-labs/agaram-2.5b-instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download murai-labs/agaram-2.5b-instruct ``` 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 [murai-labs/agaram-2.5b-instruct on Hugging Face](https://huggingface.co/murai-labs/agaram-2.5b-instruct) ## 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 | | --- | --- | --- | --- | --- | --- | | Agaram 2.5B (this page) | 1,296 | 11 | 2.5B | apache-2.0 | 2026-10-28 | | [Limite 1B - Violetto](https://mindket.com/p/b56eaa7a-7d76-4d70-818d-00611d6bff43) | 3,985 | 145 | 1B | apache-2.0 | 2026-11-02 | | [mine:o1-nano](https://mindket.com/p/bde0c510-aa2d-4dff-9651-f1d9930aa476) | 68 | 7 | — | apache-2.0 | 2026-09-19 | | [Drone Transcripter 4](https://mindket.com/p/65c3fbbe-68e5-4224-a409-a22d7760d30d) | 0 | 5 | — | apache-2.0 | 2026-11-06 | 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: 2026-10-28 - First published: 2026-10-12 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open murai-labs/agaram-2.5b-instruct on Hugging Face](https://huggingface.co/murai-labs/agaram-2.5b-instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/82101e4f-6f63-4064-b2af-09a2ce997919) - [Its page on Mindket](https://mindket.com/p/82101e4f-6f63-4064-b2af-09a2ce997919)