Instructions to use JakeTurner616/Adonalsium-Mistral-7b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JakeTurner616/Adonalsium-Mistral-7b-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="JakeTurner616/Adonalsium-Mistral-7b-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JakeTurner616/Adonalsium-Mistral-7b-v0.1")
model = AutoModelForCausalLM.from_pretrained("JakeTurner616/Adonalsium-Mistral-7b-v0.1", device_map="auto")

PEFT
How to use JakeTurner616/Adonalsium-Mistral-7b-v0.1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JakeTurner616/Adonalsium-Mistral-7b-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JakeTurner616/Adonalsium-Mistral-7b-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JakeTurner616/Adonalsium-Mistral-7b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JakeTurner616/Adonalsium-Mistral-7b-v0.1

SGLang

How to use JakeTurner616/Adonalsium-Mistral-7b-v0.1 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 "JakeTurner616/Adonalsium-Mistral-7b-v0.1" \
    --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": "JakeTurner616/Adonalsium-Mistral-7b-v0.1",
		"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 "JakeTurner616/Adonalsium-Mistral-7b-v0.1" \
        --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": "JakeTurner616/Adonalsium-Mistral-7b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JakeTurner616/Adonalsium-Mistral-7b-v0.1 with Docker Model Runner:
```
docker model run hf.co/JakeTurner616/Adonalsium-Mistral-7b-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Adonalsium-Mistral-Adapters

Basic Information

Model Name: Adonalsium-Mistral-7b-v0.1
Model Type: Text Generation
Developers: Jake T, Ilijah P
Contact Information: jake@serverboi.org, Github repo
Model Data Source: Cosmere series novels by Brandon Sanderson. Dataset and workflows can be found here
Data Visualization: Cosmere Character Visualization
Training and Generation Notebook: Google Colab Notebook
Adapters / base model merge Notebook: Google Colab Notebook

Overview

This LLM was trained as an attempt to generate text that mirrors the complex narrative and character interactions of Brandon Sanderson's Cosmere series, aiming to enhance creative storytelling and facilitate academic research in narrative analysis.

Data Source

The model leverages a simple dataset derived from the entire Cosmere series, enriched by dynamic visualizations that highlight the complex interplay of relationships and interactions within the Cosmere universe.

Technical Details

Environment: Google Colab, PEFT 0.8.2, tesla T4
Architecture: Mistral instruct tailored for Cosmere narratives
Adapters: Adapters model
Training Data: Cosmere series

Step	Training Loss
500	0.018500
1000	0.000000

Detailed technical specifics, including architecture choices, hyperparameters, and training methodologies, are documented in the accompanying training and generation notebook.

Access and Usage

Test and research with this model within the text generation web UI colab. The model is accessible for use as detailed in the training and generation notebook.

Downloads last month: 15

Safetensors

Model size

7B params

Tensor type

F16

Model tree for JakeTurner616/Adonalsium-Mistral-7b-v0.1

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(948)

this model

Quantizations

2 models