Instructions to use GenVRadmin/llama38bGenZ_Vikas-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenVRadmin/llama38bGenZ_Vikas-Merged with Transformers:

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

pipe = pipeline("text-generation", model="GenVRadmin/llama38bGenZ_Vikas-Merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GenVRadmin/llama38bGenZ_Vikas-Merged")
model = AutoModelForCausalLM.from_pretrained("GenVRadmin/llama38bGenZ_Vikas-Merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GenVRadmin/llama38bGenZ_Vikas-Merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GenVRadmin/llama38bGenZ_Vikas-Merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GenVRadmin/llama38bGenZ_Vikas-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GenVRadmin/llama38bGenZ_Vikas-Merged

SGLang

How to use GenVRadmin/llama38bGenZ_Vikas-Merged 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 "GenVRadmin/llama38bGenZ_Vikas-Merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GenVRadmin/llama38bGenZ_Vikas-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "GenVRadmin/llama38bGenZ_Vikas-Merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GenVRadmin/llama38bGenZ_Vikas-Merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GenVRadmin/llama38bGenZ_Vikas-Merged with Docker Model Runner:
```
docker model run hf.co/GenVRadmin/llama38bGenZ_Vikas-Merged
```

llama3 variant for 22 Indian languages:-

Tamil
Telugu
Assamese
Kashmiri
Punjabi
Bengali
Sanskrit
Malyalam
Sindhi
Marathi
Gujarati
Kannada
Odia
Maithili
Urdu
Nepali
Manipuri
Dogri
English
Arabic
Santali
Bodo

We first pre-trained the model on 100 million plus Indic language tokens. Then, it was finetuned on close sourced GenZ_Vikas datasets consisting 7.5 million SFT pairs, including 5.5 million Hindi SFT pairs. Finally it underwent DPO training to align it with human preferences.

The model has been benchmarked on Indic LLM leaderboard where it outperforms our AryaBhatta series on Hindi evals. And llama3 base model on all Indian languages.

Training happened on 2*A100 for 24 days.

Link: https://huggingface.co/spaces/Cognitive-Lab/indic_llm_leaderboard

Release link: https://www.linkedin.com/feed/update/urn:li:activity:7199506579828662272

Downloads last month: 3

Safetensors

Model size

8B params

Tensor type

F16