Instructions to use Rohitkhatri75436/smollm_flutter_packages_model_structured_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rohitkhatri75436/smollm_flutter_packages_model_structured_text with Transformers:

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

pipe = pipeline("text-generation", model="Rohitkhatri75436/smollm_flutter_packages_model_structured_text")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rohitkhatri75436/smollm_flutter_packages_model_structured_text")
model = AutoModelForCausalLM.from_pretrained("Rohitkhatri75436/smollm_flutter_packages_model_structured_text", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rohitkhatri75436/smollm_flutter_packages_model_structured_text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rohitkhatri75436/smollm_flutter_packages_model_structured_text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rohitkhatri75436/smollm_flutter_packages_model_structured_text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rohitkhatri75436/smollm_flutter_packages_model_structured_text

SGLang

How to use Rohitkhatri75436/smollm_flutter_packages_model_structured_text 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 "Rohitkhatri75436/smollm_flutter_packages_model_structured_text" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rohitkhatri75436/smollm_flutter_packages_model_structured_text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Rohitkhatri75436/smollm_flutter_packages_model_structured_text" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rohitkhatri75436/smollm_flutter_packages_model_structured_text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rohitkhatri75436/smollm_flutter_packages_model_structured_text with Docker Model Runner:
```
docker model run hf.co/Rohitkhatri75436/smollm_flutter_packages_model_structured_text
```

Rohitkhatri75436 commited on May 18, 2025

Commit

2e098d8

verified ·

1 Parent(s): 76f9970

rohitkhatri75436/fine-tuned-360m-smollm-flutter-structured

Browse files

Files changed (3) hide show

README.md +4 -5
model.safetensors +1 -1
training_args.bin +1 -1

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/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8510
 ## Model description
@@ -42,15 +42,14 @@ The following hyperparameters were used during training:
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - num_epochs: 3
-- mixed_precision_training: Native AMP
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 0.5574        | 1.0   | 197  | 0.8908          |
-| 0.4378        | 2.0   | 394  | 0.8597          |
-| 0.4158        | 3.0   | 591  | 0.8510          |
 ### Framework versions

 This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.8575
 ## Model description
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - num_epochs: 3
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| 0.5609        | 1.0   | 197  | 0.8989          |
+| 0.4427        | 2.0   | 394  | 0.8667          |
+| 0.4213        | 3.0   | 591  | 0.8575          |
 ### Framework versions

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