Instructions to use aisquared/dlite-v2-355m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aisquared/dlite-v2-355m with Transformers:

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

pipe = pipeline("text-generation", model="aisquared/dlite-v2-355m", device_map="auto")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aisquared/dlite-v2-355m")
model = AutoModelForCausalLM.from_pretrained("aisquared/dlite-v2-355m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aisquared/dlite-v2-355m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aisquared/dlite-v2-355m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisquared/dlite-v2-355m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aisquared/dlite-v2-355m

SGLang

How to use aisquared/dlite-v2-355m 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 "aisquared/dlite-v2-355m" \
    --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": "aisquared/dlite-v2-355m",
		"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 "aisquared/dlite-v2-355m" \
        --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": "aisquared/dlite-v2-355m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aisquared/dlite-v2-355m with Docker Model Runner:
```
docker model run hf.co/aisquared/dlite-v2-355m
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Nov 17, 2023

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+14

-1

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -115,4 +115,17 @@ state of the art, but rather further show that chat-like behaviors in LLMs can b
 *DLite is an experimental technology and is not designed for use in any environment without significant testing and safety consideration.
 Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include, but are not limited to: factual
 inaccuracies, biases, offensive responses, toxicity, and hallucinations. Just as with any other LLM, we advise users of this technology
-to exercise good judgment when applying this technology.*

 *DLite is an experimental technology and is not designed for use in any environment without significant testing and safety consideration.
 Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include, but are not limited to: factual
 inaccuracies, biases, offensive responses, toxicity, and hallucinations. Just as with any other LLM, we advise users of this technology
+to exercise good judgment when applying this technology.*
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aisquared__dlite-v2-355m)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 27.53   |
+| ARC (25-shot)         | 28.33          |
+| HellaSwag (10-shot)   | 40.54    |
+| MMLU (5-shot)         | 26.77         |
+| TruthfulQA (0-shot)   | 38.76   |
+| Winogrande (5-shot)   | 52.8   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 5.53         |