Instructions to use kitsunea/modelSmolLM2-improvd-assignment2-exercise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kitsunea/modelSmolLM2-improvd-assignment2-exercise with Transformers:

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

pipe = pipeline("text-generation", model="kitsunea/modelSmolLM2-improvd-assignment2-exercise")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kitsunea/modelSmolLM2-improvd-assignment2-exercise")
model = AutoModelForCausalLM.from_pretrained("kitsunea/modelSmolLM2-improvd-assignment2-exercise", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kitsunea/modelSmolLM2-improvd-assignment2-exercise with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kitsunea/modelSmolLM2-improvd-assignment2-exercise"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kitsunea/modelSmolLM2-improvd-assignment2-exercise",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kitsunea/modelSmolLM2-improvd-assignment2-exercise

SGLang

How to use kitsunea/modelSmolLM2-improvd-assignment2-exercise 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 "kitsunea/modelSmolLM2-improvd-assignment2-exercise" \
    --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": "kitsunea/modelSmolLM2-improvd-assignment2-exercise",
		"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 "kitsunea/modelSmolLM2-improvd-assignment2-exercise" \
        --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": "kitsunea/modelSmolLM2-improvd-assignment2-exercise",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kitsunea/modelSmolLM2-improvd-assignment2-exercise with Docker Model Runner:
```
docker model run hf.co/kitsunea/modelSmolLM2-improvd-assignment2-exercise
```

modelSmolLM2-improvd-assignment2-exercise

File size: 2,358 Bytes

af81b7e

---
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-135M
tags:
- generated_from_trainer
model-index:
- name: modelSmolLM2-improvd-assignment2-exercise
  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. -->

# modelSmolLM2-improvd-assignment2-exercise

This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2899

## 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: 0.0001
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.9359        | 0.1067 | 400  | 2.7948          |
| 2.6792        | 0.2133 | 800  | 2.6778          |
| 2.5989        | 0.32   | 1200 | 2.6008          |
| 2.5019        | 0.4267 | 1600 | 2.5441          |
| 2.4683        | 0.5333 | 2000 | 2.4983          |
| 2.4736        | 0.64   | 2400 | 2.4578          |
| 2.3997        | 0.7467 | 2800 | 2.4135          |
| 2.3478        | 0.8533 | 3200 | 2.3831          |
| 2.3269        | 0.96   | 3600 | 2.3529          |
| 2.0474        | 1.0667 | 4000 | 2.3547          |
| 1.8848        | 1.1733 | 4400 | 2.3444          |
| 1.87          | 1.28   | 4800 | 2.3280          |
| 1.8541        | 1.3867 | 5200 | 2.3183          |
| 1.8413        | 1.4933 | 5600 | 2.3095          |
| 1.8518        | 1.6    | 6000 | 2.2993          |
| 1.8189        | 1.7067 | 6400 | 2.2945          |
| 1.8615        | 1.8133 | 6800 | 2.2911          |
| 1.82          | 1.92   | 7200 | 2.2899          |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1