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

Libraries

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

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

pipe = pipeline("text-generation", model="nlpguy/Hermes-low-tune")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nlpguy/Hermes-low-tune")
model = AutoModelForCausalLM.from_pretrained("nlpguy/Hermes-low-tune")
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

vLLM

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

Use Docker

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

SGLang

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

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

merged

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: simonveitner/Math-OpenHermes-2.5-Mistral-7B
dtype: float16
merge_method: slerp
parameters:
  t:
  - value: 0.5
slices:
- sources:
  - layer_range: [0, 32]
    model: simonveitner/Math-OpenHermes-2.5-Mistral-7B
  - layer_range: [0, 32]
    model: openaccess-ai-collective/dpopenhermes-alpha-v0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	67.18
AI2 Reasoning Challenge (25-Shot)	63.99
HellaSwag (10-Shot)	83.75
MMLU (5-Shot)	63.60
TruthfulQA (0-shot)	51.37
Winogrande (5-shot)	77.90
GSM8k (5-shot)	62.47

Downloads last month: 65

Safetensors

Model size

7B params

Tensor type

F16

Model tree for nlpguy/Hermes-low-tune

Base model

mistralai/Mistral-7B-v0.1

Finetuned

teknium/OpenHermes-2.5-Mistral-7B

Finetuned

openaccess-ai-collective/dpopenhermes-alpha-v0

Finetuned

(1)

this model

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

63.990
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

83.750
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

63.600
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

51.370
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

77.900
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

62.470