Instructions to use nlpguy/Hermes-low-tune-3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nlpguy/Hermes-low-tune-3.1 with Transformers:

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

pipe = pipeline("text-generation", model="nlpguy/Hermes-low-tune-3.1")
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("nlpguy/Hermes-low-tune-3.1")
model = AutoModelForCausalLM.from_pretrained("nlpguy/Hermes-low-tune-3.1", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nlpguy/Hermes-low-tune-3.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nlpguy/Hermes-low-tune-3.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nlpguy/Hermes-low-tune-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nlpguy/Hermes-low-tune-3.1

SGLang

How to use nlpguy/Hermes-low-tune-3.1 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 "nlpguy/Hermes-low-tune-3.1" \
    --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": "nlpguy/Hermes-low-tune-3.1",
		"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 "nlpguy/Hermes-low-tune-3.1" \
        --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": "nlpguy/Hermes-low-tune-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nlpguy/Hermes-low-tune-3.1 with Docker Model Runner:
```
docker model run hf.co/nlpguy/Hermes-low-tune-3.1
```

merged

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using teknium/OpenHermes-2.5-Mistral-7B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: teknium/OpenHermes-2.5-Mistral-7B
dtype: bfloat16
merge_method: task_arithmetic
slices:
- sources:
  - layer_range: [0, 32]
    model: teknium/OpenHermes-2.5-Mistral-7B
  - layer_range: [0, 32]
    model: nlpguy/Hermes-low-tune-2
    parameters:
      weight: 0.2
  - layer_range: [0, 32]
    model: beowolx/MistralHermes-CodePro-7B-v1
    parameters:
      weight: 0.2
  - layer_range: [0, 32]
    model: flemmingmiguel/Mistrality-7B
    parameters:
      weight: 0.2
  - layer_range: [0, 32]
    model: charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B
    parameters:
      weight: 0.2
  - layer_range: [0, 32]
    model: openaccess-ai-collective/openhermes-2_5-dpo-no-robots
    parameters:
      weight: 0.2

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	68.31
AI2 Reasoning Challenge (25-Shot)	65.44
HellaSwag (10-Shot)	84.60
MMLU (5-Shot)	64.13
TruthfulQA (0-shot)	53.59
Winogrande (5-shot)	78.61
GSM8k (5-shot)	63.46