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

Libraries

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

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

pipe = pipeline("text-generation", model="IndexTeam/Index-1.9B-Character", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IndexTeam/Index-1.9B-Character 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-Character"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IndexTeam/Index-1.9B-Character",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use IndexTeam/Index-1.9B-Character 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-Character" \
    --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": "IndexTeam/Index-1.9B-Character",
		"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 "IndexTeam/Index-1.9B-Character" \
        --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": "IndexTeam/Index-1.9B-Character",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

BitVoyage commited on Jun 13, 2024

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@@ -13,9 +13,9 @@ license_link: LICENSE
 Index-1.9B-Character模型是由Index团队自主研发的大语言模型系列中的19亿参数规模的角色扮演模型，用户可以通过上传符合要求的角色对话语料实现few shots角色定制。
-使用方式和相关代码详见[Index-1.9B/roleplay](https://github.com/bilibili/Index-1.9B/roleplay)
-更多细节详见我们的[GitHub](https://github.com/bilibili/Index-1.9B)和[Index-1.9B技术报告](https://github.com/bilibili/Index-1.9B/Index-1.9B技术报告.pdf)
 Index-1.9B-Character模型采用角色扮演领域的权威benchmark CharacterEval进行评估，该评测集从角色一致性、对话能力、角色扮演吸引力多个粒度进行评测，我们的1.9b模型整体均分排名第九，显著优于其他同量级模型。

 Index-1.9B-Character模型是由Index团队自主研发的大语言模型系列中的19亿参数规模的角色扮演模型，用户可以通过上传符合要求的角色对话语料实现few shots角色定制。
+使用方式和相关代码详见[Index-1.9B/roleplay](https://github.com/bilibili/Index-1.9B/tree/main/roleplay)
+更多细节详见我们的[GitHub](https://github.com/bilibili/Index-1.9B)和[Index-1.9B技术报告](https://github.com/bilibili/Index-1.9B/blob/main/Index-1.9B%20%E6%8A%80%E6%9C%AF%E6%8A%A5%E5%91%8A.pdf)
 Index-1.9B-Character模型采用角色扮演领域的权威benchmark CharacterEval进行评估，该评测集从角色一致性、对话能力、角色扮演吸引力多个粒度进行评测，我们的1.9b模型整体均分排名第九，显著优于其他同量级模型。