Not-For-All-Audiences

Instructions to use Lexic0n/Mistral_7B-Open_Hermes-NSFWV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lexic0n/Mistral_7B-Open_Hermes-NSFWV1 with Transformers:

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

pipe = pipeline("text-generation", model="Lexic0n/Mistral_7B-Open_Hermes-NSFWV1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lexic0n/Mistral_7B-Open_Hermes-NSFWV1")
model = AutoModelForCausalLM.from_pretrained("Lexic0n/Mistral_7B-Open_Hermes-NSFWV1", 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 Lexic0n/Mistral_7B-Open_Hermes-NSFWV1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lexic0n/Mistral_7B-Open_Hermes-NSFWV1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lexic0n/Mistral_7B-Open_Hermes-NSFWV1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lexic0n/Mistral_7B-Open_Hermes-NSFWV1

SGLang

How to use Lexic0n/Mistral_7B-Open_Hermes-NSFWV1 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 "Lexic0n/Mistral_7B-Open_Hermes-NSFWV1" \
    --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": "Lexic0n/Mistral_7B-Open_Hermes-NSFWV1",
		"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 "Lexic0n/Mistral_7B-Open_Hermes-NSFWV1" \
        --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": "Lexic0n/Mistral_7B-Open_Hermes-NSFWV1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lexic0n/Mistral_7B-Open_Hermes-NSFWV1 with Docker Model Runner:
```
docker model run hf.co/Lexic0n/Mistral_7B-Open_Hermes-NSFWV1
```

Mistral_7B-Open_Hermes-NSFWV1 / README.md

Lexic0n

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	---
	license: apache-2.0
	language:
	- en
	tags:
	- not-for-all-audiences
	---
	# Welcome!!

	This is one of my first trained models sadly i lack the hardware and funds to do any larger scale training, This sort of stuff i find interesting as a hobby.

	If you want to help fund me a training run on large scale data for a custom model hit me up

	# Model Card for Mistral_7B-Open_Hermes-NSFWV1

	<!-- Provide a quick summary of what the model is/does. -->

	This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

	## Model Details

	### Model Description

	This is a fine-tuned version of teknium/OpenHermes-2p5-Mistral-7B,

	- Model type: Mistral
	- Language(s) (NLP): English
	- License: apache-2.0
	- Finetuned from model : teknium/OpenHermes-2p5-Mistral-7B

	### Model Sources [optional]

	<!-- Provide the basic links for the model. -->

	- Repository: * [teknium/OpenHermes-2p5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)

	## Uses

	<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

	### Direct Use

	<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

	[More Information Needed]

	### Downstream Use [optional]

	<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

	[More Information Needed]

	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

	[More Information Needed]

	## Bias, Risks, and Limitations

	<!-- This section is meant to convey both technical and sociotechnical limitations. -->

	[More Information Needed]

	### Recommendations

	<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

	Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

	## How to Get Started with the Model

	Use the code below to get started with the model.

	[More Information Needed]

	## Training Details

	### Training Data

	<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

	[More Information Needed]

	### Training Procedure

	<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->