Instructions to use CalderaAI/13B-Thorns-l2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CalderaAI/13B-Thorns-l2 with Transformers:

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

pipe = pipeline("text-generation", model="CalderaAI/13B-Thorns-l2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CalderaAI/13B-Thorns-l2")
model = AutoModelForCausalLM.from_pretrained("CalderaAI/13B-Thorns-l2")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CalderaAI/13B-Thorns-l2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CalderaAI/13B-Thorns-l2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CalderaAI/13B-Thorns-l2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CalderaAI/13B-Thorns-l2

SGLang

How to use CalderaAI/13B-Thorns-l2 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 "CalderaAI/13B-Thorns-l2" \
    --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": "CalderaAI/13B-Thorns-l2",
		"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 "CalderaAI/13B-Thorns-l2" \
        --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": "CalderaAI/13B-Thorns-l2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CalderaAI/13B-Thorns-l2 with Docker Model Runner:
```
docker model run hf.co/CalderaAI/13B-Thorns-l2
```

Adding Evaluation Results

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

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+14

-1

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -106,4 +106,17 @@ Also thanks to Meta for LLaMAv2 and deciding to allow the research community at
 Each model and LoRA was hand picked and considered for what it could contribute to this ensemble.
 Thanks to each and every one of you for your incredible work developing some of the best things
-to come out of this community.

 Each model and LoRA was hand picked and considered for what it could contribute to this ensemble.
 Thanks to each and every one of you for your incredible work developing some of the best things
+to come out of this community.
+# [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_CalderaAI__13B-Thorns-l2)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 53.5   |
+| ARC (25-shot)         | 62.88          |
+| HellaSwag (10-shot)   | 83.57    |
+| MMLU (5-shot)         | 56.95         |
+| TruthfulQA (0-shot)   | 49.52   |
+| Winogrande (5-shot)   | 74.51   |
+| GSM8K (5-shot)        | 0.91        |
+| DROP (3-shot)         | 46.13         |