Instructions to use nickmalhotra/ProjectIndus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nickmalhotra/ProjectIndus")
model = AutoModelForCausalLM.from_pretrained("nickmalhotra/ProjectIndus")
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 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
```

nickmalhotra commited on Apr 29, 2024

Commit

af4024f

verified ·

1 Parent(s): 27b2fdf

Update README.md

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README.md +15 -5

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@@ -53,12 +53,11 @@ The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 <!-- Provide a longer summary of what this model is/does. -->
 The model is a single shot fine tuned Instruct LLM in Hindi and dialects
-- **Developed by:** More information needed
-- **Shared by [Optional]:** More information needed
-- **Model type:** Language model
 - **Language(s) (NLP):** hin, bho, mai, doi
 - **License:** other
-- **Parent Model:** More information needed
 - **Resources for more information:** More information needed
@@ -95,7 +94,9 @@ The model is a single shot fine tuned Instruct LLM in Hindi and dialects
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 ## Recommendations
@@ -227,6 +228,15 @@ More information needed
 # How to Get Started with the Model
 Use the code below to get started with the model.
 <details>
 <summary> Click to expand </summary>

 <!-- Provide a longer summary of what this model is/does. -->
 The model is a single shot fine tuned Instruct LLM in Hindi and dialects
+- **Developed by:** Nikhil Malhotra, Nilesh Brahme, Satish Mishra, Vinay Sharma (Makers Lab, TechMahindra)
+- **Model type:** Foundational Language model
 - **Language(s) (NLP):** hin, bho, mai, doi
 - **License:** other
+- **Parent Model:** It is the parent model on GPT architecture
 - **Resources for more information:** More information needed
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Significant research has explored bias and fairness issues with language models
+(see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
+Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
 ## Recommendations
 # How to Get Started with the Model
 Use the code below to get started with the model.
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+pipe = pipeline("text-generation", model="nickmalhotra/Indus_1.175B")
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("nickmalhotra/Indus_1.175B")
+model = AutoModelForCausalLM.from_pretrained("nickmalhotra/Indus_1.175B")
 <details>
 <summary> Click to expand </summary>