Instructions to use llam9/eng1-spa2-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llam9/eng1-spa2-large with Transformers:

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

pipe = pipeline("text-generation", model="llam9/eng1-spa2-large")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llam9/eng1-spa2-large")
model = AutoModelForCausalLM.from_pretrained("llam9/eng1-spa2-large", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llam9/eng1-spa2-large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llam9/eng1-spa2-large"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llam9/eng1-spa2-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llam9/eng1-spa2-large

SGLang

How to use llam9/eng1-spa2-large 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 "llam9/eng1-spa2-large" \
    --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": "llam9/eng1-spa2-large",
		"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 "llam9/eng1-spa2-large" \
        --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": "llam9/eng1-spa2-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llam9/eng1-spa2-large with Docker Model Runner:
```
docker model run hf.co/llam9/eng1-spa2-large
```

eng1-spa2-large

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.8330

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
3.5057	1.0	259	3.4688
3.2487	2.0	518	3.1871
3.0588	3.0	777	3.0542
2.962	4.0	1036	2.9790
2.9041	5.0	1295	2.9288
2.8781	6.0	1554	2.8899
2.8049	7.0	1813	2.8659
2.7617	8.0	2072	2.8467
2.7838	9.0	2331	2.8363
2.7148	10.0	2590	2.8330

Framework versions

Transformers 4.53.0
Pytorch 2.4.1+cu121
Datasets 2.14.7
Tokenizers 0.21.0

Downloads last month: 8

Safetensors

Model size

0.1B params

Tensor type

F32