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)

# 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=40)
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
```

Hermes-low-tune-3.1 / README.md

nlpguy

Adding Evaluation Results (#1)

f90bbae verified over 2 years ago

preview code

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	---
	tags:
	- mergekit
	- merge
	base_model:
	- teknium/OpenHermes-2.5-Mistral-7B
	- nlpguy/Hermes-low-tune-2
	- charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B
	- openaccess-ai-collective/openhermes-2_5-dpo-no-robots
	- flemmingmiguel/Mistrality-7B
	- beowolx/MistralHermes-CodePro-7B-v1
	license_name: apache-2.0-mit-dual-license
	license_link: https://huggingface.co/nlpguy/Hermes-low-tune-3.1/raw/main/LICENSE
	model-index:
	- name: Hermes-low-tune-3.1
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: AI2 Reasoning Challenge (25-Shot)
	type: ai2_arc
	config: ARC-Challenge
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 65.44
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/Hermes-low-tune-3.1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HellaSwag (10-Shot)
	type: hellaswag
	split: validation
	args:
	num_few_shot: 10
	metrics:
	- type: acc_norm
	value: 84.6
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/Hermes-low-tune-3.1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU (5-Shot)
	type: cais/mmlu
	config: all
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 64.13
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/Hermes-low-tune-3.1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: TruthfulQA (0-shot)
	type: truthful_qa
	config: multiple_choice
	split: validation
	args:
	num_few_shot: 0
	metrics:
	- type: mc2
	value: 53.59
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/Hermes-low-tune-3.1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-shot)
	type: winogrande
	config: winogrande_xl
	split: validation
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 78.61
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/Hermes-low-tune-3.1
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GSM8k (5-shot)
	type: gsm8k
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 63.46
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/Hermes-low-tune-3.1
	name: Open LLM Leaderboard
	---
	# merged

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) as a base.

	### Models Merged

	The following models were included in the merge:
	* [nlpguy/Hermes-low-tune-2](https://huggingface.co/nlpguy/Hermes-low-tune-2)
	* [charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B](https://huggingface.co/charlesdedampierre/TopicNeuralHermes-2.5-Mistral-7B)
	* [openaccess-ai-collective/openhermes-2_5-dpo-no-robots](https://huggingface.co/openaccess-ai-collective/openhermes-2_5-dpo-no-robots)
	* [flemmingmiguel/Mistrality-7B](https://huggingface.co/flemmingmiguel/Mistrality-7B)
	* [beowolx/MistralHermes-CodePro-7B-v1](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nlpguy__Hermes-low-tune-3.1)

	\| 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\|