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 Settings

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
```

andreaskoepf commited on Aug 28, 2023

Commit

d51a1b5

1 Parent(s): 612dab2

Correct max_position_embeddings

Browse files

Files changed (1) hide show

config.json +1 -1

config.json CHANGED Viewed

@@ -15,7 +15,7 @@
   "hidden_size": 5120,
   "initializer_range": 0.02,
   "intermediate_size": 13824,
-  "max_position_embeddings": 4096,
   "model_type": "llama",
   "num_attention_heads": 40,
   "num_hidden_layers": 40,

   "hidden_size": 5120,
   "initializer_range": 0.02,
   "intermediate_size": 13824,
+  "max_position_embeddings": 16384,
   "model_type": "llama",
   "num_attention_heads": 40,
   "num_hidden_layers": 40,