Instructions to use WilliamHH/Assignment2-modified-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WilliamHH/Assignment2-modified-V2 with Transformers:

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

pipe = pipeline("text-generation", model="WilliamHH/Assignment2-modified-V2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WilliamHH/Assignment2-modified-V2")
model = AutoModelForCausalLM.from_pretrained("WilliamHH/Assignment2-modified-V2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use WilliamHH/Assignment2-modified-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WilliamHH/Assignment2-modified-V2"
# 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-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WilliamHH/Assignment2-modified-V2

SGLang

How to use WilliamHH/Assignment2-modified-V2 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-V2" \
    --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-V2",
		"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-V2" \
        --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-V2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WilliamHH/Assignment2-modified-V2 with Docker Model Runner:
```
docker model run hf.co/WilliamHH/Assignment2-modified-V2
```

Assignment2-modified-V2 / README.md

WilliamHH

End of training

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	---
	library_name: transformers
	license: apache-2.0
	base_model: HuggingFaceTB/SmolLM-135M
	tags:
	- generated_from_trainer
	model-index:
	- name: Assignment2-modified-V2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# Assignment2-modified-V2

	This model is a fine-tuned version of [HuggingFaceTB/SmolLM-135M](https://huggingface.co/HuggingFaceTB/SmolLM-135M) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.9966

	## 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 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: 2

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 3.1852 \| 0.32 \| 200 \| 3.1458 \|
	\| 2.8767 \| 0.64 \| 400 \| 3.0504 \|
	\| 2.7815 \| 0.96 \| 600 \| 3.0009 \|
	\| 2.4577 \| 1.28 \| 800 \| 3.0048 \|
	\| 2.3987 \| 1.6 \| 1000 \| 2.9972 \|
	\| 2.3939 \| 1.92 \| 1200 \| 2.9966 \|


	### Framework versions

	- Transformers 4.56.1
	- Pytorch 2.8.0+cu126
	- Datasets 4.0.0
	- Tokenizers 0.22.0