Instructions to use FrancisYang77/squad_results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrancisYang77/squad_results with Transformers:

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

pipe = pipeline("text-generation", model="FrancisYang77/squad_results")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FrancisYang77/squad_results")
model = AutoModelForCausalLM.from_pretrained("FrancisYang77/squad_results")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FrancisYang77/squad_results with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrancisYang77/squad_results"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrancisYang77/squad_results",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FrancisYang77/squad_results

SGLang

How to use FrancisYang77/squad_results 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 "FrancisYang77/squad_results" \
    --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": "FrancisYang77/squad_results",
		"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 "FrancisYang77/squad_results" \
        --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": "FrancisYang77/squad_results",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FrancisYang77/squad_results with Docker Model Runner:
```
docker model run hf.co/FrancisYang77/squad_results
```

squad_results

This model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 6.2957

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	15	5.5286
No log	2.0	30	5.6499
No log	3.0	45	5.7365
No log	4.0	60	5.8599
No log	5.0	75	5.9417
No log	6.0	90	6.0606
No log	7.0	105	6.1215
No log	8.0	120	6.1976
No log	9.0	135	6.2711
No log	10.0	150	6.2957

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: -

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for FrancisYang77/squad_results

Base model

EleutherAI/gpt-neo-125m

Finetuned

(183)

this model