Instructions to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa

SGLang

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa" \
    --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": "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

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 "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa" \
        --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": "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa with Docker Model Runner:
```
docker model run hf.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa
```

Incorrect format in one of the dataset

by GenVRadmin - opened Mar 18, 2024

Discussion

GenVRadmin

Mar 18, 2024

Hi,

We have started working on making a Gemma multilingual model and your work is being used as a reference at our end (GenVR Research).
While running your training script as on github, we found an error in one of the dataset pre-processing. This is related to Sarvam AI's samvaad dataset.

While all the other datasets are being formatted as "Instruction: Question, input: Context, Answer: Reply". The pre processing function used on Sarvam's filtered dataset you are using causes "Instruction: Context, input: Question, Answer: Reply" which has instruction and input as swapped.

Just wanted to report this error. All the best and impressive work on making this.

-GenVR Research

Telugu-LLM-Labs

Owner Mar 18, 2024

Hello,

Thanks for checking into our work. We have considered first single turn of Sarvam AI's samvaad dataset and here is the dataset we used accordingly. Probably you were using directly Sarvam AI dataset because of which causing the error.

Let us know if you need any further help.

Thank You.

GenVRadmin

Mar 18, 2024

This comment has been hidden

GenVRadmin changed discussion status to closed Mar 18, 2024

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