Instructions to use WilliamHH/Assignment2-modified-V5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WilliamHH/Assignment2-modified-V5 with Transformers:

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

pipe = pipeline("text-generation", model="WilliamHH/Assignment2-modified-V5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WilliamHH/Assignment2-modified-V5")
model = AutoModelForCausalLM.from_pretrained("WilliamHH/Assignment2-modified-V5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use WilliamHH/Assignment2-modified-V5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WilliamHH/Assignment2-modified-V5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WilliamHH/Assignment2-modified-V5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WilliamHH/Assignment2-modified-V5

SGLang

How to use WilliamHH/Assignment2-modified-V5 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 "WilliamHH/Assignment2-modified-V5" \
    --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": "WilliamHH/Assignment2-modified-V5",
		"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 "WilliamHH/Assignment2-modified-V5" \
        --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": "WilliamHH/Assignment2-modified-V5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WilliamHH/Assignment2-modified-V5 with Docker Model Runner:
```
docker model run hf.co/WilliamHH/Assignment2-modified-V5
```

Assignment2-modified-V5

This model is a fine-tuned version of HuggingFaceTB/SmolLM-135M on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.8368

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: 4e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.6905	0.2132	200	3.0405
2.5101	0.4264	400	2.9576
2.4462	0.6397	600	2.8987
2.3591	0.8529	800	2.8647
2.303	1.0661	1000	2.8560
2.1124	1.2793	1200	2.8495
2.1282	1.4925	1400	2.8352
2.0929	1.7058	1600	2.8206
2.0485	1.9190	1800	2.8105
2.0171	2.1322	2000	2.8344
1.9584	2.3454	2200	2.8385
1.9484	2.5586	2400	2.8381
1.9297	2.7719	2600	2.8366
1.9606	2.9851	2800	2.8368

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for WilliamHH/Assignment2-modified-V5

Base model

HuggingFaceTB/SmolLM-135M

Finetuned

(115)

this model