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)

# pip install -U transformers accelerate
# 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=256)
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
```

TypeError: transformers.generation.utils.GenerationMixin.generate() argument after ** must be a mapping, not Tensor

by Pazuzzu - opened Nov 19, 2023

Discussion

Pazuzzu

Nov 19, 2023

•

edited Nov 19, 2023

Hello there, first of all thank you for this great work.
I have an issue when I try to run the model

import transformers
model = transformers.AutoModelForCausalLM.from_pretrained('teknium/OpenHermes-2.5-Mistral-7B', load_in_8bit=True,  device_map='auto')
tokenizer = transformers.AutoTokenizer.from_pretrained('teknium/OpenHermes-2.5-Mistral-7B', add_eos_token=True)

messages = [
    {"role": "system", "content": "You are Hermes 2."},
    {"role": "user", "content": "Hello, what are your limitations ?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
gen_output = model.generate(**gen_input)
tokenizer.decode(gen_output[0], skip_special_tokens=True)

I get the error :
```python

TypeError Traceback (most recent call last)
Cell In [100], line 7
2 messages = [
3 {"role": "system", "content": "You are Hermes 2."},
4 {"role": "user", "content": "Hello, what are your limitations ?"}
5 ]
6 gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
----> 7 gen_output = model.generate(**gen_input)
8 tokenizer.decode(gen_output[0], skip_special_tokens=True)

TypeError: transformers.generation.utils.GenerationMixin.generate() argument after ** must be a mapping, not Tensor


Please, is there something I'm doing wrong ? I am using (transformers==4.36.0.dev0)

teknium

Owner Nov 24, 2023

I couldnt get the chat template thing to work, try just using the string of what it should be

Pazuzzu

Dec 5, 2023

•

edited Dec 5, 2023

Apologies,
I have no issue with tokenizer.apply_chat_template but my model loading statement was missing the "return_dict=True", that is what caused the error.

InformaticsSolutions

Jan 26, 2024

•

edited Jan 26, 2024

@Pazuzzu i am still getting the error, even after updating the model loading statement like this:
model = transformers.AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True, device_map='auto', return_dict=True)
I am using transformers 4.36.0. Should the statement be written differently? thank you.

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TypeError: transformers.generation.utils.GenerationMixin.generate() argument after ** must be a mapping, not Tensor

I get the error :```python

I get the error :
```python