Instructions to use lgaalves/mistral-7b_open_platypus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lgaalves/mistral-7b_open_platypus with Transformers:

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

pipe = pipeline("text-generation", model="lgaalves/mistral-7b_open_platypus")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lgaalves/mistral-7b_open_platypus")
model = AutoModelForCausalLM.from_pretrained("lgaalves/mistral-7b_open_platypus", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lgaalves/mistral-7b_open_platypus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lgaalves/mistral-7b_open_platypus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lgaalves/mistral-7b_open_platypus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lgaalves/mistral-7b_open_platypus

SGLang

How to use lgaalves/mistral-7b_open_platypus 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 "lgaalves/mistral-7b_open_platypus" \
    --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": "lgaalves/mistral-7b_open_platypus",
		"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 "lgaalves/mistral-7b_open_platypus" \
        --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": "lgaalves/mistral-7b_open_platypus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lgaalves/mistral-7b_open_platypus with Docker Model Runner:
```
docker model run hf.co/lgaalves/mistral-7b_open_platypus
```

lgaalves commited on Oct 13, 2023

Commit

b9a60b9

1 Parent(s): a201d89

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@@ -17,7 +17,7 @@ language:
 ### Benchmark Metrics
-| Metric                | mistral-7b-platypus | mistralai/Mistral-7B-v0.1 |garage-bAInd/Platypus2-7B|
 |-----------------------|-------|-------|-------|
 | Avg.                  | - | 62.40 |56.13|
 | ARC (25-shot)         | - | 59.98 |55.20|

 ### Benchmark Metrics
+| Metric                | mistral-7b_open_platypus | mistralai/Mistral-7B-v0.1 |garage-bAInd/Platypus2-7B|
 |-----------------------|-------|-------|-------|
 | Avg.                  | - | 62.40 |56.13|
 | ARC (25-shot)         | - | 59.98 |55.20|