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
```

WilliamHH commited on Oct 3, 2025

Commit

45dc681

verified ·

1 Parent(s): aa40605

End of training

Browse files

Files changed (1) hide show

README.md +8 -14

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 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: 2.8719
 ## Model description
@@ -35,7 +35,7 @@ More information needed
 ### Training hyperparameters
 The following hyperparameters were used during training:
-- learning_rate: 3e-05
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
@@ -47,18 +47,12 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 2.594         | 0.16  | 200  | 2.9858          |
-| 2.5675        | 0.32  | 400  | 2.9604          |
-| 2.5578        | 0.48  | 600  | 2.9417          |
-| 2.6641        | 0.64  | 800  | 2.9061          |
-| 2.6633        | 0.8   | 1000 | 2.8829          |
-| 2.6115        | 0.96  | 1200 | 2.8656          |
-| 2.4333        | 1.12  | 1400 | 2.8809          |
-| 2.3539        | 1.28  | 1600 | 2.8797          |
-| 2.3336        | 1.44  | 1800 | 2.8743          |
-| 2.334         | 1.6   | 2000 | 2.8731          |
-| 2.3502        | 1.76  | 2200 | 2.8723          |
-| 2.3205        | 1.92  | 2400 | 2.8719          |
 ### Framework versions

 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
 ### Training hyperparameters
 The following hyperparameters were used during training:
+- learning_rate: 5e-05
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
 | 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