Instructions to use Heejindo/model_output_e10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Heejindo/model_output_e10 with Transformers:

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

pipe = pipeline("text-generation", model="Heejindo/model_output_e10")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Heejindo/model_output_e10")
model = AutoModelForCausalLM.from_pretrained("Heejindo/model_output_e10")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Heejindo/model_output_e10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Heejindo/model_output_e10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heejindo/model_output_e10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Heejindo/model_output_e10

SGLang

How to use Heejindo/model_output_e10 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 "Heejindo/model_output_e10" \
    --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": "Heejindo/model_output_e10",
		"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 "Heejindo/model_output_e10" \
        --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": "Heejindo/model_output_e10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Heejindo/model_output_e10 with Docker Model Runner:
```
docker model run hf.co/Heejindo/model_output_e10
```

model_output_e10

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.1035

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: 8
eval_batch_size: 8
seed: 42
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: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.1363	0.0954	500	2.1193
1.7864	0.1908	1000	2.1035
1.5126	0.2862	1500	2.1698
1.2379	0.3815	2000	2.2744
0.9729	0.4769	2500	2.3314

Framework versions

Transformers 4.46.3
Pytorch 2.3.0
Datasets 2.14.4
Tokenizers 0.20.3

Downloads last month: -

Safetensors

Model size

1B params

Tensor type

F32

Model tree for Heejindo/model_output_e10

Base model

meta-llama/Llama-3.2-1B

Finetuned

(908)

this model