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---
library_name: transformers
datasets:
- pksx01/alpaca_bhojpuri_instruction
language:
- bh
base_model:
- sarvamai/sarvam-1
---
This model has been instruction tuned from [sarvamai/sarvam-1](https://huggingface.co/sarvamai/sarvam-1). This is an early checkpoint trained for few hours. Checkpoints with complete training will be released soon.
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This model can be used to chat in Bhojpuri language.
## How to Get Started with the Model
Use the code below to get started with the model.
```
import torch
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("pksx01/sarvam-1-it-bhojpuri")
# Load base model
model = AutoModelForCausalLM.from_pretrained(
"sarvamai/sarvam-1",
torch_dtype=torch.bfloat16,
device_map="auto"
)
model.resize_token_embeddings(len(tokenizer))
# Load the PEFT model
peft_model = PeftModel.from_pretrained(
model,
"pksx01/sarvam-1-it-bhojpuri",
is_trainable=False
)
message = [{"role": "user", "content": "भारत के पहिला प्रधानमंत्री के रहे?"}]
model_ip = tokenizer.apply_chat_template(message, tokenize=False)
tokenized_ip = tokenizer(model_ip, return_tensors="pt").to("cuda")
peft_model.eval()
with torch.no_grad():
op_tokens = peft_model.generate(
**tokenized_ip,
max_new_tokens=250,
temperature=0.01,
top_k=50,
top_p=0.95,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id
)
op = tokenizer.decode(op_tokens[0], skip_special_tokens=True)
print(op)
```
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
This model has be trained on an instruction dataset - [pksx01/alpaca_bhojpuri_instruction](https://huggingface.co/datasets/pksx01/alpaca_bhojpuri_instruction).