Instructions to use chargoddard/Chronorctypus-Limarobormes-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chargoddard/Chronorctypus-Limarobormes-13b with Transformers:

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

pipe = pipeline("text-generation", model="chargoddard/Chronorctypus-Limarobormes-13b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chargoddard/Chronorctypus-Limarobormes-13b")
model = AutoModelForCausalLM.from_pretrained("chargoddard/Chronorctypus-Limarobormes-13b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chargoddard/Chronorctypus-Limarobormes-13b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/chargoddard/Chronorctypus-Limarobormes-13b

SGLang

How to use chargoddard/Chronorctypus-Limarobormes-13b 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 "chargoddard/Chronorctypus-Limarobormes-13b" \
    --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": "chargoddard/Chronorctypus-Limarobormes-13b",
		"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 "chargoddard/Chronorctypus-Limarobormes-13b" \
        --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": "chargoddard/Chronorctypus-Limarobormes-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chargoddard/Chronorctypus-Limarobormes-13b with Docker Model Runner:
```
docker model run hf.co/chargoddard/Chronorctypus-Limarobormes-13b
```

Great model

by JuLuComputing - opened Aug 27, 2023

Discussion

JuLuComputing

Aug 27, 2023

I am impressed by this model. I've been trying some of these smaller models and this one really stood out for its broad knowledge and skill.

Would you consider making another model like this based on the larger TheBloke/Llama-2-70B-fp16 or the chat version of it? I haven't tried running your script but I can try on one of my machines with 512GB ram, dual Xeon CPUs, and dual Tesla M40 24GB GPU cards. Perhaps I could try making the larger 70B conglomerate LLM but, I would need your help with making a list of compatible LLMs to merge.

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