Instructions to use Open-Orca/Mistral-7B-OpenOrca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Open-Orca/Mistral-7B-OpenOrca with Transformers:

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

pipe = pipeline("text-generation", model="Open-Orca/Mistral-7B-OpenOrca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Open-Orca/Mistral-7B-OpenOrca")
model = AutoModelForCausalLM.from_pretrained("Open-Orca/Mistral-7B-OpenOrca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Open-Orca/Mistral-7B-OpenOrca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Open-Orca/Mistral-7B-OpenOrca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Open-Orca/Mistral-7B-OpenOrca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Open-Orca/Mistral-7B-OpenOrca

SGLang

How to use Open-Orca/Mistral-7B-OpenOrca 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 "Open-Orca/Mistral-7B-OpenOrca" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Open-Orca/Mistral-7B-OpenOrca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Open-Orca/Mistral-7B-OpenOrca" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Open-Orca/Mistral-7B-OpenOrca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Open-Orca/Mistral-7B-OpenOrca with Docker Model Runner:
```
docker model run hf.co/Open-Orca/Mistral-7B-OpenOrca
```

bleysg

Rocketknight1 HF Staff commited on Oct 5, 2023

Commit

acef37e

1 Parent(s): 953ffc4

Add chat template (#8)

Browse files

- Add chat template (777d488ed5aca08804670593ab475e5c8de7868c)
- Fix template typo (f82a8e1570c6156c3a1d2a02c31e2ccac6471393)
- Explain chat template in README (50a0c3c092a6deea0e36272d4e6509cdd9676219)

Co-authored-by: Matthew Carrigan <Rocketknight1@users.noreply.huggingface.co>

Files changed (2) hide show

README.md +28 -1
tokenizer_config.json +1 -0

README.md CHANGED Viewed

@@ -64,6 +64,33 @@ We used [OpenAI's Chat Markup Language (ChatML)](https://github.com/openai/opena
 This means that, e.g., in [oobabooga](https://github.com/oobabooga/text-generation-webui/) the "`MPT-Chat`" instruction template should work, as it also uses ChatML.
 ## Example Prompt Exchange
 ```
@@ -173,4 +200,4 @@ Commodity cost was ~$400.
       archivePrefix={arXiv},
       primaryClass={cs.AI}
 }
-```

 This means that, e.g., in [oobabooga](https://github.com/oobabooga/text-generation-webui/) the "`MPT-Chat`" instruction template should work, as it also uses ChatML.
+This formatting has also been set as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating),
+which means that lists of messages can be formatted for you with the `apply_chat_template()` method:
+```python
+chat = [
+  {"role": "user", "content": "Hello, how are you?"},
+  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
+  {"role": "user", "content": "I'd like to show off how chat templating works!"},
+]
+tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+```
+which will yield:
+```
+<|im_start|>user
+Hello, how are you?<|im_end|>
+<|im_start|>assistant
+I'm doing great. How can I help you today?<|im_end|>
+<|im_start|>user
+I'd like to show off how chat templating works!<|im_end|>
+<|im_start|>assistant
+```
+If you use `tokenize=True` and `return_tensors="pt"` instead, then you will get a tokenized
+and formatted conversation ready to pass to `model.generate()`.
 ## Example Prompt Exchange
 ```
       archivePrefix={arXiv},
       primaryClass={cs.AI}
 }
+```

tokenizer_config.json CHANGED Viewed

@@ -45,6 +45,7 @@
   },
   "additional_special_tokens": [],
   "bos_token": "<s>",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "legacy": true,

   },
   "additional_special_tokens": [],
   "bos_token": "<s>",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "legacy": true,