Model reference · open weights

OpenHathi-Hi

LLMs sarvamai Text gen 1 build Open, with conditions 1k dl/mo

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.

  • OpenHathi-Hi is a 6.9B parameter base model for text generation, developed by Sarvam AI.
  • It is based on Llama2 and trained on Hindi, English, and Hinglish.
  • The model supports a context length of 4096 tokens and is released under the llama2 licence.

Summary of the sarvamai/OpenHathi-7B-Hi-v0.1-Base model card, 2026-10-01

What it is

Released bysarvamai
Released2023-12-13
Parameters6.9B
VRAM4.3 GB for the weights

What it runs on

Memory and cards for OpenHathi-7B-Hi-v0.1-Base (BF16)

4.3 GBweights, file size
524 MBcache per 1K tokens
477 MBruntime overhead, at least
4,096 tokenscontext max
CardRequests at onceContext maxMemory
4K each
RTX 3060 12 GB3all 4K11.6 GB
RTX 4060 Ti 16 GB4all 4K15.4 GB
RTX 3090 24 GB8all 4K23.4 GB
RTX 4090 24 GB8all 4K23.4 GB
RTX 5090 32 GB12all 4K31.0 GB
L40S 48 GB18all 4K44.0 GB
A100 80 GB34all 4K78.2 GB
H100 80 GB34all 4K78.1 GB
RTX PRO 6000 Blackwell 96 GB41all 4K93.8 GB
DGX Spark (GB10) 128 GB unified47all 4K107 GB
H200 141 GB62all 4K138 GB
B200 180 GB79all 4K176 GB
2× RTX 3060 12 GB
split by layers (llama.cpp)
8all 4K11.6 GB a card
2× RTX 4060 Ti 16 GB
split by layers (llama.cpp)
11all 4K15.4 GB a card
Memory needed at each load
Requests at once4K tokens each
17.0 GB
515.5 GB
822.0 GB
1639.2 GB
3273.5 GB
64142 GB

One card, with vLLM's small-card settings.

From the model card

What sarvamai says about OpenHathi-Hi

Read 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.

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