Instructions to use maldv/electric-mist-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maldv/electric-mist-7b with Transformers:

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

pipe = pipeline("text-generation", model="maldv/electric-mist-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maldv/electric-mist-7b")
model = AutoModelForCausalLM.from_pretrained("maldv/electric-mist-7b")
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 maldv/electric-mist-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maldv/electric-mist-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maldv/electric-mist-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maldv/electric-mist-7b

SGLang

How to use maldv/electric-mist-7b 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 "maldv/electric-mist-7b" \
    --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": "maldv/electric-mist-7b",
		"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 "maldv/electric-mist-7b" \
        --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": "maldv/electric-mist-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use maldv/electric-mist-7b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for maldv/electric-mist-7b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for maldv/electric-mist-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for maldv/electric-mist-7b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="maldv/electric-mist-7b",
    max_seq_length=2048,
)

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

Electric Mist 7B

Developed by: maldv
License: cc-by-nc-4.0
Finetuned from model: alpindale/Mistral-7B-v0.2-hf
Methodology: Simple newline delimited, rolling window book and stripped conversation data.

Have you learned anything?

Yes, I learned that if you try to train models that aren't the base model, that the results are trash. I have heard rumors that merging the LoRA's works, which is why the companion LoRA for this is published as well.

Will It Write

It's good. It goes page after page. It needs an authors note to stay on track though.

Data

90% sci-fi fiction text data (with a lot of the pulpiest removed), then 10% of the other datasets mixed together; around 6000 8192 context samples, lora r 64, lr .00005, 2 epochs.

Chat Template

It was trained to follow no prompt at all, just to start going; which means the best results are from when you start the story. There is explicitly no chat in the training data. Simply double newline delimited (even with the orca, math, etc).

Issues

Punctuation isn't perfect, and has spacing issues, but I have yet to see it collapse even after dumping 40000 tokens in a rolling 8192 context.

Downloads last month: 10

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for maldv/electric-mist-7b

Base model

mistral-community/Mistral-7B-v0.2

Finetuned

(37)

this model

Quantizations

1 model

maldv
/

electric-mist-7b