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
```

Update README.md

by philschmid - opened Sep 14, 2023

base: refs/heads/main

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@@ -10,7 +10,7 @@ language:
 This model is an Open-Assistant fine-tuning of Meta's CodeLlama 13B LLM.
-**Note**: Due to the new RoPE Theta value (1e6 instead of 1e4), for correct results you must load this model with `trust_remote_code=True` or use the latest main branch of Huggingface transformers (until version 4.33 is released).
 ## Model Details

 This model is an Open-Assistant fine-tuning of Meta's CodeLlama 13B LLM.
+**Note**: Due to the new RoPE Theta value (1e6 instead of 1e4), for correct results you must Huggingface `transformers >= 4.33`.
 ## Model Details