Instructions to use Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0 with Transformers:

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

pipe = pipeline("text-generation", model="Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0")
model = AutoModelForCausalLM.from_pretrained("Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0

SGLang

How to use Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0 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 "Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0" \
    --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": "Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0",
		"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 "Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0" \
        --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": "Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0 with Docker Model Runner:
```
docker model run hf.co/Model-SafeTensors/airoboros-l2-13b-gpt4-m2.0
```

jondurbin commited on Aug 2, 2023

Commit

b5ebc24

1 Parent(s): 8ca1d8d

Update README.md

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@@ -287,7 +287,7 @@ def parse_plan(plan):
         if line.startswith("Plan:"):
             print(line)
             continue
-        parts = re.match("^(:evidence[0-9]+:")\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
         if not parts:
           if line.startswith("Answer: "):
             return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")

         if line.startswith("Plan:"):
             print(line)
             continue
+        parts = re.match("^(:evidence[0-9]+:)\s*=\s*([^\[]+])(\[.*\])\s$", line, re.I)
         if not parts:
           if line.startswith("Answer: "):
             return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...")