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 15, 2023

Commit

a1583d2

1 Parent(s): 05600c0

Update README.md

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README.md +9 -5

README.md CHANGED Viewed

@@ -2,12 +2,21 @@
 language:
 - en
 library_name: transformers
 ---
 # orca_mini_v3_7b
 A LLama2-7b model trained on Orca Style datasets.
 ## Evaluation
 We evaluated orca_mini_v3_7b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
@@ -68,11 +77,6 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
-#### Legal Disclaimer:
-This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.
 #### Limitations & Biases:

 language:
 - en
 library_name: transformers
+license: other
+datasets:
+- psmathur/orca_mini_v1_dataset
+- ehartford/dolphin
+pipeline_tag: text-generation
 ---
 # orca_mini_v3_7b
 A LLama2-7b model trained on Orca Style datasets.
+#### legal disclaimer:
+This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.
 ## Evaluation
 We evaluated orca_mini_v3_7b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
 ```
 #### Limitations & Biases: