Instructions to use openchat/openchat_3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openchat/openchat_3.5 with Transformers:

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

pipe = pipeline("text-generation", model="openchat/openchat_3.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openchat/openchat_3.5")
model = AutoModelForCausalLM.from_pretrained("openchat/openchat_3.5")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openchat/openchat_3.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openchat/openchat_3.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openchat/openchat_3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openchat/openchat_3.5

SGLang

How to use openchat/openchat_3.5 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 "openchat/openchat_3.5" \
    --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": "openchat/openchat_3.5",
		"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 "openchat/openchat_3.5" \
        --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": "openchat/openchat_3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openchat/openchat_3.5 with Docker Model Runner:
```
docker model run hf.co/openchat/openchat_3.5
```

imone commited on Nov 4, 2023

Commit

6814591

1 Parent(s): 4d04f08

Add baselines

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@@ -97,12 +97,18 @@ assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 3200
 |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------|
 | OpenChat-3.5       | **7B**   | **61.6** | 7.81         | **47.4** | **47.6** | **59.1**      | 64.3         | **55.5**        | 63.5        | **77.3**     |
 | ChatGPT (March)*   | ?        | 61.5     | **7.94**     | 47.1     | **47.6** | 57.7          | **67.3**     | 48.1            | **70.1**    | 74.9         |
 | Mistral            | 7B       | -        | 6.84         | 38.0     | 39.0     | -             | 60.1         | 30.5            | -           | 52.2         |
 | Open-source SOTA** | 13B-70B  | 61.4     | 7.71         | 41.7     | 49.7     | 62.3          | 63.7         | 73.2            | 41.4        | 82.3         |
 |                    |          |          | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B |
 *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time.
 **: Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories.
 ***: All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks).

 |--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------|
 | OpenChat-3.5       | **7B**   | **61.6** | 7.81         | **47.4** | **47.6** | **59.1**      | 64.3         | **55.5**        | 63.5        | **77.3**     |
 | ChatGPT (March)*   | ?        | 61.5     | **7.94**     | 47.1     | **47.6** | 57.7          | **67.3**     | 48.1            | **70.1**    | 74.9         |
+|                    |          |          |              |          |          |               |              |                 |             |              |
+| OpenHermes 2.5     | 7B       | 59.3     | 7.54         | 46.5     | 49.4     | 57.5          | 63.8         | 48.2            | 59.9        | 73.5         |
+| OpenOrca Mistral   | 7B       | 52.7     | 6.86         | 42.9     | 49.4     | 45.9          | 59.3         | 38.4            | 58.1        | 59.1         |
+| Zephyr-β^          | 7B       | 34.6     | 7.34         | 39.0     | 40.6     | 40.8          | 39.8         | 22.0            | 16.0        | 5.1          |
 | Mistral            | 7B       | -        | 6.84         | 38.0     | 39.0     | -             | 60.1         | 30.5            | -           | 52.2         |
 | Open-source SOTA** | 13B-70B  | 61.4     | 7.71         | 41.7     | 49.7     | 62.3          | 63.7         | 73.2            | 41.4        | 82.3         |
 |                    |          |          | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B |
 *: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time.
+^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data.
 **: Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories.
 ***: All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks).