Instructions to use osei1819/fine_tuned_phi1_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osei1819/fine_tuned_phi1_5 with Transformers:

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

pipe = pipeline("text-generation", model="osei1819/fine_tuned_phi1_5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("osei1819/fine_tuned_phi1_5")
model = AutoModelForCausalLM.from_pretrained("osei1819/fine_tuned_phi1_5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use osei1819/fine_tuned_phi1_5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osei1819/fine_tuned_phi1_5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osei1819/fine_tuned_phi1_5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/osei1819/fine_tuned_phi1_5

SGLang

How to use osei1819/fine_tuned_phi1_5 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 "osei1819/fine_tuned_phi1_5" \
    --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": "osei1819/fine_tuned_phi1_5",
		"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 "osei1819/fine_tuned_phi1_5" \
        --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": "osei1819/fine_tuned_phi1_5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use osei1819/fine_tuned_phi1_5 with Docker Model Runner:
```
docker model run hf.co/osei1819/fine_tuned_phi1_5
```

fine_tuned_phi1_5

This model is a fine-tuned version of microsoft/phi-1_5 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3498

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 16
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
lr_scheduler_warmup_steps: 500
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.9275	4	5.3873
No log	1.9275	8	5.1770
6.4196	2.9275	12	4.5702
6.4196	3.9275	16	3.1211
4.6448	4.9275	20	2.0151
4.6448	5.9275	24	0.5937
4.6448	6.9275	28	0.4527
0.9737	7.9275	32	0.4155
0.9737	8.9275	36	0.3759
0.4308	9.9275	40	0.3498

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: -

Safetensors

Model size

1B params

Tensor type

F32

Model tree for osei1819/fine_tuned_phi1_5

Base model

microsoft/phi-1_5

Finetuned

(239)

this model