Not-For-All-Audiences

Instructions to use Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ")
model = AutoModelForCausalLM.from_pretrained("Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ

SGLang

How to use Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ 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 "Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ with Docker Model Runner:
```
docker model run hf.co/Blackroot/Nous-Hermes-Llama2-13b-Storywriter-GPTQ
```

Join the Coffee & AI Discord for AI Stuff and things!

This is a model merge of https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b + https://huggingface.co/Blackroot/Llama-2-13B-Storywriter-LORA

Thanks to NousResearch and Meta for the base models.

A brief warning, no alignment or attempts of any kind were made to reign in, censor, or otherwise manipulate the outputs of this model. It is a raw model and may produce outputs that are unexpected or otherwise distateful. You are the master of your own destiny, and the master of this model, use with caution.

Quantization was 128 Group Size with act order on. This quantization was aimed at Exllama text generation.

Nous-Hermes is the base model, so the recommendation is to use their recommended alpaca instruct format for prompts:

Prompt Format

The model follows the Alpaca prompt format:

### Instruction:
<prompt>

### Response:
<leave a newline blank for model to respond>

### Instruction:
<prompt>

### Input:
<additional context>

### Response:
<leave a newline blank for model to respond>

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