Model reference · open weights
OpenHathi-Hi is an open-weight language model from sarvamai. OpenHathi-7B-Hi-v0.1-Base (BF16) weighs 4.3 GB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the sarvamai/OpenHathi-7B-Hi-v0.1-Base model card, 2026-10-01
What it is
| Released by | sarvamai |
|---|---|
| Released | 2023-12-13 |
| Parameters | 6.9B |
| VRAM | 4.3 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory |
|---|---|---|---|
| 4K each | |||
| RTX 3060 12 GB | 3 | all 4K | 11.6 GB |
| RTX 4060 Ti 16 GB | 4 | all 4K | 15.4 GB |
| RTX 3090 24 GB | 8 | all 4K | 23.4 GB |
| RTX 4090 24 GB | 8 | all 4K | 23.4 GB |
| RTX 5090 32 GB | 12 | all 4K | 31.0 GB |
| L40S 48 GB | 18 | all 4K | 44.0 GB |
| A100 80 GB | 34 | all 4K | 78.2 GB |
| H100 80 GB | 34 | all 4K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 41 | all 4K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 47 | all 4K | 107 GB |
| H200 141 GB | 62 | all 4K | 138 GB |
| B200 180 GB | 79 | all 4K | 176 GB |
| 2× RTX 3060 12 GB split by layers (llama.cpp) | 8 | all 4K | 11.6 GB a card |
| 2× RTX 4060 Ti 16 GB split by layers (llama.cpp) | 11 | all 4K | 15.4 GB a card |
| Requests at once | 4K tokens each |
|---|---|
| 1 | 7.0 GB |
| 5 | 15.5 GB |
| 8 | 22.0 GB |
| 16 | 39.2 GB |
| 32 | 73.5 GB |
| 64 | 142 GB |
One card, with vLLM's small-card settings.
From the model card
This repository is the first model in the OpenHathi series of models that will be released by Sarvam AI. This is a 7B parameter, based on Llama2, trained on Hindi, English, and Hinglish. More details about the model, its training procedure, and evaluations can be found here.
Note: this is a base model and not meant to be used as is. We recommend first finetuning it on task(s) you are interested in.
# Usage
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
tokenizer = LlamaTokenizer.from_pretrained('sarvamai/OpenHathi-7B-Hi-v0.1-Base')
model = LlamaForCausalLM.from_pretrained('sarvamai/OpenHathi-7B-Hi-v0.1-Base', torch_dtype=torch.bfloat16)
prompt = "मैं एक अच्छा हाथी हूँ"
inputs = tokenizer(prompt, return_tensors="pt")
# Generate
generate_ids = model.generate(inputs.input_ids, max_length=30)
tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.