Instructions to use MattDy88/smollm2_desired with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MattDy88/smollm2_desired with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MattDy88/smollm2_desired")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MattDy88/smollm2_desired") model = AutoModelForCausalLM.from_pretrained("MattDy88/smollm2_desired") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MattDy88/smollm2_desired with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MattDy88/smollm2_desired" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MattDy88/smollm2_desired", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MattDy88/smollm2_desired
- SGLang
How to use MattDy88/smollm2_desired 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 "MattDy88/smollm2_desired" \ --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": "MattDy88/smollm2_desired", "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 "MattDy88/smollm2_desired" \ --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": "MattDy88/smollm2_desired", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MattDy88/smollm2_desired with Docker Model Runner:
docker model run hf.co/MattDy88/smollm2_desired
smollm2_desired
This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.8833
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.3819 | 0.32 | 200 | 3.1110 |
| 2.9887 | 0.64 | 400 | 2.9821 |
| 2.8406 | 0.96 | 600 | 2.9237 |
| 2.7739 | 1.28 | 800 | 2.8974 |
| 2.7301 | 1.6 | 1000 | 2.8859 |
| 2.7242 | 1.92 | 1200 | 2.8833 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.2.0
- Tokenizers 0.22.1
- Downloads last month
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Model tree for MattDy88/smollm2_desired
Base model
HuggingFaceTB/SmolLM2-135M