Instructions to use nickmalhotra/ProjectIndus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickmalhotra/ProjectIndus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nickmalhotra/ProjectIndus") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nickmalhotra/ProjectIndus") model = AutoModelForCausalLM.from_pretrained("nickmalhotra/ProjectIndus", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nickmalhotra/ProjectIndus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nickmalhotra/ProjectIndus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nickmalhotra/ProjectIndus", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nickmalhotra/ProjectIndus
- SGLang
How to use nickmalhotra/ProjectIndus 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 "nickmalhotra/ProjectIndus" \ --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": "nickmalhotra/ProjectIndus", "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 "nickmalhotra/ProjectIndus" \ --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": "nickmalhotra/ProjectIndus", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use nickmalhotra/ProjectIndus with Docker Model Runner:
docker model run hf.co/nickmalhotra/ProjectIndus
gguf needed
please launch the gguf
Hi @Neural-Hacker , I've made a GGUF version of Indus 1.1B.
llama.cpp's converter doesn't work on Indus out of the box (the tokenizer is SentencePiece-style, not GPT-2's), so I patched the conversion and checked the result against the original model:
- token IDs match the HF tokenizer on all 5,000 test lines (Hindi, 5 dialects, romanized Hindi)
- the F16 file picks the same next token as the original model (fp32) on 200/200 prompts
- Q4_K_M is about 760 MB, around 3% higher perplexity than F16, and runs in llama.cpp and Ollama
@nickmalhotra before uploading, I wanted to check with your team since it's your model. Would you like it hosted under makers-lab (happy to hand over the files and the conversion script), or is an unofficial community upload okay? I'd also like to use whatever name you prefer, since "Project Indus" is your trademark. I tried projectindus@techmahindra.com first, but it only accepts internal mail, so I'm asking here. I'll post the link once that's sorted.