Instructions to use pankajmathur/orca_mini_v3_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajmathur/orca_mini_v3_7b with Transformers:

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

pipe = pipeline("text-generation", model="pankajmathur/orca_mini_v3_7b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pankajmathur/orca_mini_v3_7b")
model = AutoModelForCausalLM.from_pretrained("pankajmathur/orca_mini_v3_7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pankajmathur/orca_mini_v3_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajmathur/orca_mini_v3_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajmathur/orca_mini_v3_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pankajmathur/orca_mini_v3_7b

SGLang

How to use pankajmathur/orca_mini_v3_7b 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 "pankajmathur/orca_mini_v3_7b" \
    --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": "pankajmathur/orca_mini_v3_7b",
		"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 "pankajmathur/orca_mini_v3_7b" \
        --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": "pankajmathur/orca_mini_v3_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pankajmathur/orca_mini_v3_7b with Docker Model Runner:
```
docker model run hf.co/pankajmathur/orca_mini_v3_7b
```

psmathur commited on Aug 9, 2023

Commit

feb746d

1 Parent(s): c395ffb

Update README.md

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README.md CHANGED Viewed

@@ -48,7 +48,13 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_7b", use_fast=False)
-model = AutoModelForCausalLM.from_pretrained("psmathur/orca_mini_v3_7b", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
 system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n"
 #generate text steps

 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_7b", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained(
+  "psmathur/orca_mini_v3_7b",
+  torch_dtype=torch.float16,
+  load_in_8bit=True,
+  low_cpu_mem_usage=True,
+  device_map="auto"
+)
 system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n"
 #generate text steps