Instructions to use Heejindo/rationale_model_e10_save5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Heejindo/rationale_model_e10_save5000 with Transformers:

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

pipe = pipeline("text-generation", model="Heejindo/rationale_model_e10_save5000")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Heejindo/rationale_model_e10_save5000")
model = AutoModelForCausalLM.from_pretrained("Heejindo/rationale_model_e10_save5000")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Heejindo/rationale_model_e10_save5000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Heejindo/rationale_model_e10_save5000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heejindo/rationale_model_e10_save5000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Heejindo/rationale_model_e10_save5000

SGLang

How to use Heejindo/rationale_model_e10_save5000 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/rationale_model_e10_save5000" \
    --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/rationale_model_e10_save5000",
		"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/rationale_model_e10_save5000" \
        --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/rationale_model_e10_save5000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Heejindo/rationale_model_e10_save5000 with Docker Model Runner:
```
docker model run hf.co/Heejindo/rationale_model_e10_save5000
```

rationale_model_e10_save5000

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: 2.6975

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: 1e-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: 10

Training results

Training Loss	Epoch	Step	Validation Loss
0.3986	0.9538	5000	2.6975
0.1468	1.9077	10000	3.2221
0.1156	2.8615	15000	3.4922
0.0981	3.8153	20000	3.6490
0.0847	4.7692	25000	3.8345
0.0704	5.7230	30000	3.9968
0.0551	6.6768	35000	4.2504
0.0433	7.6307	40000	4.5271
0.0354	8.5845	45000	4.7534
0.0317	9.5383	50000	4.9696

Framework versions

Transformers 4.46.3
Pytorch 2.3.0
Datasets 2.14.4
Tokenizers 0.20.3

Downloads last month: 1

Safetensors

Model size

1B params

Tensor type

F32

Model tree for Heejindo/rationale_model_e10_save5000

Base model

meta-llama/Llama-3.2-1B

Finetuned

(908)

this model