Instructions to use kitsunea/modelSmolLM2-improvd-assignment2-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kitsunea/modelSmolLM2-improvd-assignment2-2 with Transformers:

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

pipe = pipeline("text-generation", model="kitsunea/modelSmolLM2-improvd-assignment2-2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kitsunea/modelSmolLM2-improvd-assignment2-2")
model = AutoModelForCausalLM.from_pretrained("kitsunea/modelSmolLM2-improvd-assignment2-2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kitsunea/modelSmolLM2-improvd-assignment2-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kitsunea/modelSmolLM2-improvd-assignment2-2"
# 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-assignment2-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kitsunea/modelSmolLM2-improvd-assignment2-2

SGLang

How to use kitsunea/modelSmolLM2-improvd-assignment2-2 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-assignment2-2" \
    --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-assignment2-2",
		"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-assignment2-2" \
        --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-assignment2-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kitsunea/modelSmolLM2-improvd-assignment2-2 with Docker Model Runner:
```
docker model run hf.co/kitsunea/modelSmolLM2-improvd-assignment2-2
```

modelSmolLM2-improvd-assignment2-2

File size: 2,345 Bytes

ba7366d

---
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-135M
tags:
- generated_from_trainer
model-index:
- name: modelSmolLM2-improvd-assignment2-2
  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-assignment2-2

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: 2.2993

## 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: 0.0003
- 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: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.9887        | 0.1067 | 200  | 2.9221          |
| 2.7337        | 0.2133 | 400  | 2.7976          |
| 2.6423        | 0.32   | 600  | 2.7118          |
| 2.534         | 0.4267 | 800  | 2.6432          |
| 2.4911        | 0.5333 | 1000 | 2.5883          |
| 2.4906        | 0.64   | 1200 | 2.5351          |
| 2.4029        | 0.7467 | 1400 | 2.4751          |
| 2.3421        | 0.8533 | 1600 | 2.4331          |
| 2.3091        | 0.96   | 1800 | 2.3934          |
| 1.9326        | 1.0667 | 2000 | 2.4085          |
| 1.7083        | 1.1733 | 2200 | 2.3911          |
| 1.6905        | 1.28   | 2400 | 2.3594          |
| 1.6651        | 1.3867 | 2600 | 2.3497          |
| 1.647         | 1.4933 | 2800 | 2.3315          |
| 1.6486        | 1.6    | 3000 | 2.3155          |
| 1.6093        | 1.7067 | 3200 | 2.3068          |
| 1.6538        | 1.8133 | 3400 | 2.3016          |
| 1.6138        | 1.92   | 3600 | 2.2993          |


### Framework versions

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