Text Generation
Transformers
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MLX
mistral
mistral nemo
creative
creative writing
fiction writing
plot generation
sub-plot generation
story generation
scene continue
storytelling
fiction story
science fiction
romance
all genres
story
writing
vivid prosing
vivid writing
fiction
roleplaying
bfloat16
brainstorm 40x
swearing
mistral nemo instruct
mn
128k context
rp
horror
mergekit
Merge
mlx-my-repo
text-generation-inference
8-bit precision
Instructions to use derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit") model = AutoModelForCausalLM.from_pretrained("derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit") - MLX
How to use derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit
- SGLang
How to use derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit 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 "derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit" \ --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": "derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit", "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 "derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit" \ --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": "derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit" --prompt "Once upon a time"
- Docker Model Runner
How to use derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit with Docker Model Runner:
docker model run hf.co/derek-wh/MN-DARKEST-UNIVERSE-29B-mlx-8Bit
insmek/MN-DARKEST-UNIVERSE-29B-mlx-8Bit
The Model insmek/MN-DARKEST-UNIVERSE-29B-mlx-8Bit was converted to MLX format from DavidAU/MN-DARKEST-UNIVERSE-29B using mlx-lm version 0.22.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("insmek/MN-DARKEST-UNIVERSE-29B-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
8B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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8-bit