Instructions to use teknium/OpenHermes-2.5-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teknium/OpenHermes-2.5-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="teknium/OpenHermes-2.5-Mistral-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B", device_map="auto")
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 Settings

vLLM

How to use teknium/OpenHermes-2.5-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teknium/OpenHermes-2.5-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teknium/OpenHermes-2.5-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/teknium/OpenHermes-2.5-Mistral-7B

SGLang

How to use teknium/OpenHermes-2.5-Mistral-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 "teknium/OpenHermes-2.5-Mistral-7B" \
    --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": "teknium/OpenHermes-2.5-Mistral-7B",
		"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 "teknium/OpenHermes-2.5-Mistral-7B" \
        --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": "teknium/OpenHermes-2.5-Mistral-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use teknium/OpenHermes-2.5-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/teknium/OpenHermes-2.5-Mistral-7B
```

Suppress the prompt from appearing in the generated response

#27

by InformaticsSolutions - opened Feb 13, 2024

Discussion

InformaticsSolutions

Feb 13, 2024

•

edited Feb 18, 2024

Example code:

model_name = 'teknium/OpenHermes-2.5-Mistral-7B'
messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I’m quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I’m   cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]
tokenizer = LlamaTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=True)
encoded = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
model_inputs = encoded.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=250, do_sample=False, eos_token_id=tokenizer.eos_token_id, pad_token_id = tokenizer.pad_token_id)
decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_space=True)
print(decoded[0])

Response:

<|im_start|> user
What is your favourite condiment? 
 <|im_start|> assistant
Well, I’m quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I’m cooking up in the kitchen! 
 <|im_start|> user
Do you have mayonnaise recipes? 
 <|im_start|> assistant
Of course! Here are two simple recipes for homemade mayonnaise that you can try out. [...]

Is there a way to prevent or suppress the prompt from appearing in the response? Thank you.

InformaticsSolutions changed discussion title from Exclude prompt from generated response to Exclude the prompt from appearing in the generated response Feb 14, 2024

InformaticsSolutions changed discussion title from Exclude the prompt from appearing in the generated response to Suppress the prompt from appearing in the generated response Feb 18, 2024

tcapelle

Mar 12, 2024

decoded[0][len(model_inputs.input_ids):]

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