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

Libraries

How to use PleIAs/Monad with Transformers:

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

pipe = pipeline("text-generation", model="PleIAs/Monad")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PleIAs/Monad")
model = AutoModelForCausalLM.from_pretrained("PleIAs/Monad")
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 PleIAs/Monad with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PleIAs/Monad

SGLang

How to use PleIAs/Monad 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 "PleIAs/Monad" \
    --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": "PleIAs/Monad",
		"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 "PleIAs/Monad" \
        --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": "PleIAs/Monad",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Kabil007 commited on Jan 14

Commit

6cfe814

verified ·

1 Parent(s): d8fce6b

Update README.md

Browse files

Adds a working generation example for Monad, demonstrating manual prompting and removing token_type_ids to avoid unused-kwargs errors during inference issues.

Files changed (1) hide show

README.md +22 -1

README.md CHANGED Viewed

@@ -66,4 +66,25 @@ Who are you?<|im_end|>
 Monad has no support yet for multi-turn.
-A major envisioned use case for Monad is explainability, as the model does provide a unique trade-off between observability and actual reasoning performance.

 Monad has no support yet for multi-turn.
+A major envisioned use case for Monad is explainability, as the model does provide a unique trade-off between observability and actual reasoning performance.
+Use with Transformers (Directly)
+```zsh
+from transformers import AutoTokenizer, AutoModelForCausalLM
+prompt = "Hi i am Kabil?\nAssistant:"
+input_token = tokenizer(
+    prompt,
+    return_tensors = "pt"
+  )
+input_token.pop("token_type_ids", None)
+input_token = input_token.to(model.device)
+outputs = model.generate(
+    **input_token,
+    max_new_tokens=40
+  )
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```