Instructions to use pankajmathur/orca_mini_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajmathur/orca_mini_7b with Transformers:

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

pipe = pipeline("text-generation", model="pankajmathur/orca_mini_7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pankajmathur/orca_mini_7b")
model = AutoModelForCausalLM.from_pretrained("pankajmathur/orca_mini_7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pankajmathur/orca_mini_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_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_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pankajmathur/orca_mini_7b

SGLang

How to use pankajmathur/orca_mini_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_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_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_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_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pankajmathur/orca_mini_7b with Docker Model Runner:
```
docker model run hf.co/pankajmathur/orca_mini_7b
```

psmathur commited on Jun 24, 2023

Commit

911bb0b

1 Parent(s): 4ddf772

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ We build explain tuned [WizardLM dataset ~70K](https://github.com/nlpxucan/Wizar
 We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
-This helps student model aka [wizardlm_alpaca_dolly_orca_open_llama_13b](https://huggingface.co/psmathur/wizardlm_alpaca_dolly_orca_open_llama_13b) to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
 Please see below example usage how the **System** prompt is added before each **instruction**.
@@ -55,7 +55,7 @@ import torch
 from transformers import LlamaForCausalLM, LlamaTokenizer
 # Hugging Face model_path
-model_path = 'psmathur/wizardlm_alpaca_dolly_orca_open_llama_7b'
 tokenizer = LlamaTokenizer.from_pretrained(model_path)
 model = LlamaForCausalLM.from_pretrained(
     model_path, torch_dtype=torch.float16, device_map='auto',

 We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
+This helps student model aka this model to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
 Please see below example usage how the **System** prompt is added before each **instruction**.
 from transformers import LlamaForCausalLM, LlamaTokenizer
 # Hugging Face model_path
+model_path = 'psmathur/orca_mini_7b'
 tokenizer = LlamaTokenizer.from_pretrained(model_path)
 model = LlamaForCausalLM.from_pretrained(
     model_path, torch_dtype=torch.float16, device_map='auto',