Instructions to use ellen625/opt_125_wiki_k10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ellen625/opt_125_wiki_k10 with Transformers:

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

pipe = pipeline("text-generation", model="ellen625/opt_125_wiki_k10")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ellen625/opt_125_wiki_k10")
model = AutoModelForMultimodalLM.from_pretrained("ellen625/opt_125_wiki_k10")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ellen625/opt_125_wiki_k10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ellen625/opt_125_wiki_k10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ellen625/opt_125_wiki_k10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ellen625/opt_125_wiki_k10

SGLang

How to use ellen625/opt_125_wiki_k10 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 "ellen625/opt_125_wiki_k10" \
    --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": "ellen625/opt_125_wiki_k10",
		"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 "ellen625/opt_125_wiki_k10" \
        --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": "ellen625/opt_125_wiki_k10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ellen625/opt_125_wiki_k10 with Docker Model Runner:
```
docker model run hf.co/ellen625/opt_125_wiki_k10
```

opt_125_wiki_k10

This model is a fine-tuned version of facebook/opt-125m on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.0748

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.1921	0.4172	500	1.1153
1.1596	0.8344	1000	1.0920
1.1265	1.2516	1500	1.0861
1.113	1.6688	2000	1.0788
1.0929	2.0859	2500	1.0763
1.0872	2.5031	3000	1.0760
1.0991	2.9203	3500	1.0750

Framework versions

Transformers 4.40.2
Pytorch 2.2.2
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for ellen625/opt_125_wiki_k10

Base model

facebook/opt-125m

Finetuned

(117)

this model