Instructions to use Mattimax/DAC60M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mattimax/DAC60M with Transformers:

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

pipe = pipeline("text-generation", model="Mattimax/DAC60M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mattimax/DAC60M")
model = AutoModelForCausalLM.from_pretrained("Mattimax/DAC60M")
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 Mattimax/DAC60M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mattimax/DAC60M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mattimax/DAC60M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mattimax/DAC60M

SGLang

How to use Mattimax/DAC60M 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 "Mattimax/DAC60M" \
    --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": "Mattimax/DAC60M",
		"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 "Mattimax/DAC60M" \
        --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": "Mattimax/DAC60M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mattimax/DAC60M with Docker Model Runner:
```
docker model run hf.co/Mattimax/DAC60M
```

Mattimax commited on Jan 30

Commit

f96f448

verified ·

1 Parent(s): 1d5fcab

Update README.md

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README.md +3 -3

README.md CHANGED Viewed

@@ -45,7 +45,7 @@ L’obiettivo non è competere con modelli di scala superiore, ma offrire **un b
 ## 🧠 Architecture
-DAC67M utilizza una variante personalizzata di **LlamaForCausalLM**.
 ### Core Configuration
@@ -82,7 +82,7 @@ DAC67M utilizza una variante personalizzata di **LlamaForCausalLM**.
 ## 🔤 Tokenizer
-DAC67M utilizza il tokenizer del modello:
 **sapienzanlp/Minerva-350M-base-v1.0**
 [https://huggingface.co/sapienzanlp/Minerva-350M-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-350M-base-v1.0)
@@ -109,7 +109,7 @@ Dettagli sul training:
 ## 🎯 Intended Use
-DAC67M è adatto per:
 * Generazione di testo
 * Autocompletamento

 ## 🧠 Architecture
+DAC60M utilizza una variante personalizzata di **LlamaForCausalLM**.
 ### Core Configuration
 ## 🔤 Tokenizer
+DAC60M utilizza il tokenizer del modello:
 **sapienzanlp/Minerva-350M-base-v1.0**
 [https://huggingface.co/sapienzanlp/Minerva-350M-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-350M-base-v1.0)
 ## 🎯 Intended Use
+DAC60M è adatto per:
 * Generazione di testo
 * Autocompletamento