Instructions to use OpenAssistant/codellama-13b-oasst-sft-v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenAssistant/codellama-13b-oasst-sft-v10 with Transformers:

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

pipe = pipeline("text-generation", model="OpenAssistant/codellama-13b-oasst-sft-v10", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/codellama-13b-oasst-sft-v10", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("OpenAssistant/codellama-13b-oasst-sft-v10", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenAssistant/codellama-13b-oasst-sft-v10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenAssistant/codellama-13b-oasst-sft-v10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenAssistant/codellama-13b-oasst-sft-v10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenAssistant/codellama-13b-oasst-sft-v10

SGLang

How to use OpenAssistant/codellama-13b-oasst-sft-v10 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 "OpenAssistant/codellama-13b-oasst-sft-v10" \
    --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": "OpenAssistant/codellama-13b-oasst-sft-v10",
		"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 "OpenAssistant/codellama-13b-oasst-sft-v10" \
        --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": "OpenAssistant/codellama-13b-oasst-sft-v10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenAssistant/codellama-13b-oasst-sft-v10 with Docker Model Runner:
```
docker model run hf.co/OpenAssistant/codellama-13b-oasst-sft-v10
```

Merge with OpenOrca Preview 2

by gsaivinay - opened Aug 27, 2023

Discussion

gsaivinay

Aug 27, 2023

Hello Andreas,

Sorry if I'm being ignorant and asking wrong questions.

Is it possible to merge this model with Open-Orca/OpenOrcaxOpenChat-Preview2-13B just like how they merged OpenOrca and Platypus to create Open-Orca/OpenOrca-Platypus2-13B.

The reason I'm asking this question is because OpenAssistant/codellama-13b-oasst-sft-v10 scored very good in HumanEval+ benchmarks

while Open-Orca/OpenOrcaxOpenChat-Preview2-13B has very good scores in lm-evaluation-harness

So I'm just wondering if we can merge both to create a model that performs well in all tasks and can become the ultimate goto model in 13B params range.

Thanks.

flozi00

Aug 28, 2023

I will give it a try soon

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