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)

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

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Index-1.9B-Constant-LR

Model Introduction

This repository Index-1.9B-Constant-LR is the checkpoint file of the Index-1.9B base model before decay training, which is provided for everyone to conduct research on downstream tasks.

For more details, see our GitHub and Index-1.9B Technical Report

Evaluation Results

Here we add the evaluation of the general understanding ability of the Index-1.9B-Constant-LR model

Model	Average score	Average English score	MMLU	CEVAL	CMMLU	HellaSwag	Arc-C	Arc-E
Index-1.9B-Constant-LR	41.47	44.24	35.30	38.58	33.26	59.94	32.96	48.75
Index-1.9B-Pure	49.55	52.83	43.75	42.35	43.61	63.21	42.75	61.61
Index-1.9B	64.92	69.93	52.53	57.01	52.79	80.69	65.15	81.35

Evaluation code is based on OpenCompass with compatibility modifications. See the evaluate folder for details.

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