Instructions to use Envoid/MindFlay-22B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Envoid/MindFlay-22B with Transformers:

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

pipe = pipeline("text-generation", model="Envoid/MindFlay-22B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Envoid/MindFlay-22B")
model = AutoModelForCausalLM.from_pretrained("Envoid/MindFlay-22B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Envoid/MindFlay-22B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Envoid/MindFlay-22B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Envoid/MindFlay-22B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Envoid/MindFlay-22B

SGLang

How to use Envoid/MindFlay-22B 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 "Envoid/MindFlay-22B" \
    --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": "Envoid/MindFlay-22B",
		"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 "Envoid/MindFlay-22B" \
        --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": "Envoid/MindFlay-22B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Envoid/MindFlay-22B with Docker Model Runner:
```
docker model run hf.co/Envoid/MindFlay-22B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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Check out the documentation for more information.

This is the original FP16 result of the model created using chargoddard's frankenllama script so that others interested in further experimentation with the results may do so.

WARNING: this model is very unpredictable.

This model is an experiment using the frankenstein script from https://huggingface.co/chargoddard/llama2-22b Except I decided to use it with two models that have already been extensively finetuned. With https://huggingface.co/TheBloke/Llama-2-13B-Chat-fp16 as the base model and https://huggingface.co/Aeala/Enterredaas-33b as the donor model.

The resulting model is surprisingly coherent and still responds well to the llama2chat prompt format [INST]<<SYS>><</SYS>>[/INST] and still has most of llama2chat's bubbly/giddy personality but more gritty and visceral. It makes occasional "typos" along with some other quirks so it was not completely unscathed by the frankensteining process. I plan to massage it over with a LoRA in the near future to bring it into more harmony but in the meantime it is available now for your enjoyment.

Use cases: Chat/RP not much else.

Downloads last month: 10

Safetensors

Model size

22B params

Tensor type

F32

F16

Model tree for Envoid/MindFlay-22B

Quantizations

1 model