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
```

digitous commited on Sep 6, 2023

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@@ -12,7 +12,7 @@ tags:
 ## 13B-Thorns [An Instruct Based LLaMAv2-13B Ensemble Merge | Alpaca Format]
 # WARNING - This Model Is Uncensored And Has Not Been Fully Tested For Toxicity. This Is A Research Artifact Intended For Responsible Use. May Generate Offensive And Misleading Content. Do Not Treat Language Sythesized By This Research Artifact As Advice Or As Factual In Any Domain. CalderaAI Strictly Does Not Condone Use Of This Release Outside The Domain Of Research Or Entertainment.
-## Composition:
 13B-Thorns-l2 utilizes a new merge method called Spherical Linear Interpolation. By merging data as a spherical vector store concept, a combined pair of models have a smoother transition between feature spaces that are characteristic of each model, resulting in a more coherent fusion of both model's unique strengths.

 ## 13B-Thorns [An Instruct Based LLaMAv2-13B Ensemble Merge | Alpaca Format]
 # WARNING - This Model Is Uncensored And Has Not Been Fully Tested For Toxicity. This Is A Research Artifact Intended For Responsible Use. May Generate Offensive And Misleading Content. Do Not Treat Language Sythesized By This Research Artifact As Advice Or As Factual In Any Domain. CalderaAI Strictly Does Not Condone Use Of This Release Outside The Domain Of Research Or Entertainment.
+# Composition:
 13B-Thorns-l2 utilizes a new merge method called Spherical Linear Interpolation. By merging data as a spherical vector store concept, a combined pair of models have a smoother transition between feature spaces that are characteristic of each model, resulting in a more coherent fusion of both model's unique strengths.