Instructions to use IndexTeam/Index-1.9B-Constant-LR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IndexTeam/Index-1.9B-Constant-LR with Transformers:

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

pipe = pipeline("text-generation", model="IndexTeam/Index-1.9B-Constant-LR", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IndexTeam/Index-1.9B-Constant-LR", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IndexTeam/Index-1.9B-Constant-LR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IndexTeam/Index-1.9B-Constant-LR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IndexTeam/Index-1.9B-Constant-LR",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IndexTeam/Index-1.9B-Constant-LR

SGLang

How to use IndexTeam/Index-1.9B-Constant-LR 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 "IndexTeam/Index-1.9B-Constant-LR" \
    --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": "IndexTeam/Index-1.9B-Constant-LR",
		"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 "IndexTeam/Index-1.9B-Constant-LR" \
        --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": "IndexTeam/Index-1.9B-Constant-LR",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IndexTeam/Index-1.9B-Constant-LR with Docker Model Runner:
```
docker model run hf.co/IndexTeam/Index-1.9B-Constant-LR
```

Add pipeline tag, library name, and paper link to model card

by nielsr HF Staff - opened Jul 18

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+54

-1

nielsr

Jul 18

Hi! I'm Niels from the Hugging Face community team.

I've opened this PR to enhance your model card with metadata and usage information:

Added pipeline_tag: text-generation and library_name: transformers to the YAML metadata. This enables the "Use in Transformers" button on the hub.
Linked the model card to its respective paper: Index SLM Technical Report.
Added a "How to Use" code snippet demonstrating how to load the model using transformers.
Appended model license and citation sections.

Please review and merge if this looks good to you!

Add pipeline tag, library name, and paper link to model card68ea117a

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