Instructions to use mstyslavity/monad_random_experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mstyslavity/monad_random_experimental with Transformers:

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

pipe = pipeline("text-generation", model="mstyslavity/monad_random_experimental")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mstyslavity/monad_random_experimental")
model = AutoModelForCausalLM.from_pretrained("mstyslavity/monad_random_experimental")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mstyslavity/monad_random_experimental with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mstyslavity/monad_random_experimental

SGLang

How to use mstyslavity/monad_random_experimental 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 "mstyslavity/monad_random_experimental" \
    --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": "mstyslavity/monad_random_experimental",
		"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 "mstyslavity/monad_random_experimental" \
        --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": "mstyslavity/monad_random_experimental",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mstyslavity/monad_random_experimental with Docker Model Runner:
```
docker model run hf.co/mstyslavity/monad_random_experimental
```

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Passthrough merge method using PleIAs/Monad as a base.

Models Merged

The following models were included in the merge:

PleIAs/Baguettotron

Configuration

The following YAML configuration was used to produce this model:

merge_method: passthrough
dtype: bfloat16
out_dtype: float32
base_model: PleIAs/Monad
slices:
  - sources:
    - model: PleIAs/Monad
      layer_range: [0,64]
  - sources:
    - model: PleIAs/Monad
      layer_range: [10,36]
  - sources:
    - model: PleIAs/Monad
      layer_range: [12,30]
  - sources:
    - model: PleIAs/Monad
      layer_range: [0,24]
  - sources:
    - model: PleIAs/Monad
      layer_range: [16,32]
  - sources:
    - model: PleIAs/Monad
      layer_range: [48,64]
  - sources:
    - model: PleIAs/Baguettotron
      layer_range: [0,40]
  - sources:
    - model: PleIAs/Baguettotron
      layer_range: [40,80]
tokenizer:
  source: base
parameters:
  normalize: true

Downloads last month: 3

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for mstyslavity/monad_random_experimental

PleIAs/Baguettotron

PleIAs/Monad

Merge model

this model