Instructions to use HugoZeballos/nllb-esp-rap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HugoZeballos/nllb-esp-rap with Transformers:

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

pipe = pipeline("text-generation", model="HugoZeballos/nllb-esp-rap")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("HugoZeballos/nllb-esp-rap")
model = AutoModelForMultimodalLM.from_pretrained("HugoZeballos/nllb-esp-rap")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HugoZeballos/nllb-esp-rap with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HugoZeballos/nllb-esp-rap

SGLang

How to use HugoZeballos/nllb-esp-rap 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 "HugoZeballos/nllb-esp-rap" \
    --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": "HugoZeballos/nllb-esp-rap",
		"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 "HugoZeballos/nllb-esp-rap" \
        --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": "HugoZeballos/nllb-esp-rap",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HugoZeballos/nllb-esp-rap with Docker Model Runner:
```
docker model run hf.co/HugoZeballos/nllb-esp-rap
```

nllb-esp-rap / config.json

HugoZeballos

Upload M2M100ForConditionalGeneration

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	{
	"_name_or_path": "/gd/MyDrive/models_new/NLLB-rpa-15000",
	"activation_dropout": 0.0,
	"activation_function": "relu",
	"architectures": [
	"M2M100ForConditionalGeneration"
	],
	"attention_dropout": 0.1,
	"bos_token_id": 0,
	"d_model": 1024,
	"decoder_attention_heads": 16,
	"decoder_ffn_dim": 4096,
	"decoder_layerdrop": 0,
	"decoder_layers": 12,
	"decoder_start_token_id": 2,
	"dropout": 0.1,
	"encoder_attention_heads": 16,
	"encoder_ffn_dim": 4096,
	"encoder_layerdrop": 0,
	"encoder_layers": 12,
	"eos_token_id": 2,
	"init_std": 0.02,
	"is_encoder_decoder": true,
	"max_length": 200,
	"max_position_embeddings": 1024,
	"model_type": "m2m_100",
	"num_hidden_layers": 12,
	"pad_token_id": 1,
	"scale_embedding": true,
	"tokenizer_class": "NllbTokenizer",
	"torch_dtype": "float32",
	"transformers_version": "4.33.0",
	"use_cache": true,
	"vocab_size": 256647
	}