Instructions to use Mathildeholst/Warning-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mathildeholst/Warning-generator with Transformers:

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

pipe = pipeline("text-generation", model="Mathildeholst/Warning-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mathildeholst/Warning-generator")
model = AutoModelForCausalLM.from_pretrained("Mathildeholst/Warning-generator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mathildeholst/Warning-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Mathildeholst/Warning-generator

SGLang

How to use Mathildeholst/Warning-generator 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 "Mathildeholst/Warning-generator" \
    --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": "Mathildeholst/Warning-generator",
		"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 "Mathildeholst/Warning-generator" \
        --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": "Mathildeholst/Warning-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mathildeholst/Warning-generator with Docker Model Runner:
```
docker model run hf.co/Mathildeholst/Warning-generator
```

Mathildeholst commited on Oct 21, 2025

Commit

4ea4a18

verified ·

1 Parent(s): 39051d5

End of training

Browse files

Files changed (1) hide show

README.md +9 -9

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/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 6.6796
 ## Model description
@@ -47,17 +47,17 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 7.629         | 0.32  | 200  | 6.6328          |
-| 6.3124        | 0.64  | 400  | 6.6325          |
-| 6.3561        | 0.96  | 600  | 6.5846          |
-| 6.338         | 1.28  | 800  | 6.7885          |
-| 6.3065        | 1.6   | 1000 | 6.7319          |
-| 6.3446        | 1.92  | 1200 | 6.6796          |
 ### Framework versions
-- Transformers 4.56.1
 - Pytorch 2.8.0+cu126
 - Datasets 4.0.0
-- Tokenizers 0.22.0

 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: 6.8104
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| 7.6463        | 0.32  | 200  | 6.4905          |
+| 6.2428        | 0.64  | 400  | 6.3232          |
+| 6.3949        | 0.96  | 600  | 6.5444          |
+| 6.3416        | 1.28  | 800  | 6.8307          |
+| 6.5742        | 1.6   | 1000 | 6.9138          |
+| 6.4862        | 1.92  | 1200 | 6.8104          |
 ### Framework versions
+- Transformers 4.57.1
 - Pytorch 2.8.0+cu126
 - Datasets 4.0.0
+- Tokenizers 0.22.1