saucam/sans_data
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How to use saucam/Rudra-7b with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="saucam/Rudra-7b") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("saucam/Rudra-7b")
model = AutoModelForCausalLM.from_pretrained("saucam/Rudra-7b")How to use saucam/Rudra-7b with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saucam/Rudra-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "saucam/Rudra-7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/saucam/Rudra-7b
How to use saucam/Rudra-7b with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "saucam/Rudra-7b" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "saucam/Rudra-7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "saucam/Rudra-7b" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "saucam/Rudra-7b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use saucam/Rudra-7b with Docker Model Runner:
docker model run hf.co/saucam/Rudra-7b
Rudra-7b is a LoRA fine-tune of gemma-7b on sanskrit data
This is a text-completion model for Sanskrit language. The model was finetuned using unsloth library. I hope this paves the way for future work for Sanskrit models.
Qlora finetuning was used.
https://huggingface.co/datasets/saucam/sans_data/blob/main/README.md
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "saucam/Rudra-7b", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length = 2048,
dtype = None,
load_in_4bit = False,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
"संस्कृतम्"
], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 256, use_cache = True, repetition_penalty=1.0, temperature=1.0, )
out = tokenizer.batch_decode(outputs)
print(out)
!pip install -qU transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_name = "saucam/Rudra-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
inputs = tokenizer("संस्कृतम्", return_tensors = "pt")#.to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(tokenizer.decode(outputs[0]))
Sample output from above script
Gemma's activation function should be approximate GeLU and not exact GeLU.
Changing the activation function to `gelu_pytorch_tanh`.if you want to use the legacy `gelu`, edit the `model.config` to set `hidden_activation=gelu` instead of `hidden_act`. See https://github.com/huggingface/transformers/pull/29402 for more details.
Loading checkpoint shards: 100%|████████████████████████████| 4/4 [00:01<00:00, 2.54it/s]
<bos>संस्कृतम् भारतस्य राष्ट्रभाषा इति भारतसर्वकारस्य 1987तमे वर्षे निर्णयः । प्रायः 125 कोटि जनाः संस्कृतम् एव पठन्ति इति अनुमानम् । संस्कृतम् भारतस्य ध्रुवम् आङ्ग्लानुभाष्यम् । संस्कृतम् अत्यन्तम् प्राचीनम् । संस्कृतम् शैथिल्यात् यदा यदा बहिर्निर्याति तदा तदा एव साम्प्रतकाले संस्कृतेन सह तस्य देशस्य संस्कृतिः सह जगतः संस्कृतिः सह सङ्गच्छति इति । संस्कृतेन सह देशस्य संस्कृतिः सह नगरस्य संस्कृतिः सह क्रीडायाः संस्कृतिः सह राजकीयः, सामाजिकः, सांस्कृतिकः, आर्थिकः, सांविभागिकः, नैतिकः, शिक्षणम्, आवासीयः, साम्प्रदायिकः, धार्मिकः, आध्यात्मिकः, विनोदः, प्रौद्योगिकी, विद्यार्थ