Instructions to use Babsie/Hermes2Yi-34B-40k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Babsie/Hermes2Yi-34B-40k with Transformers:

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

pipe = pipeline("text-generation", model="Babsie/Hermes2Yi-34B-40k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Babsie/Hermes2Yi-34B-40k")
model = AutoModelForMultimodalLM.from_pretrained("Babsie/Hermes2Yi-34B-40k")
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 Babsie/Hermes2Yi-34B-40k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Babsie/Hermes2Yi-34B-40k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Babsie/Hermes2Yi-34B-40k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Babsie/Hermes2Yi-34B-40k

SGLang

How to use Babsie/Hermes2Yi-34B-40k 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 "Babsie/Hermes2Yi-34B-40k" \
    --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": "Babsie/Hermes2Yi-34B-40k",
		"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 "Babsie/Hermes2Yi-34B-40k" \
        --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": "Babsie/Hermes2Yi-34B-40k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Babsie/Hermes2Yi-34B-40k with Docker Model Runner:
```
docker model run hf.co/Babsie/Hermes2Yi-34B-40k
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

About Hermes2Yi-34B-40k

I RoPE extened this to 40K, briefly tested it to see if the tokenizer and the architecture were sound. Will be developing for writing, teaching, creating, and RP.

please see Nous Repo for more information about this model NousResearch/Nous-Hermes-2-Yi-34B