Instructions to use Susant-Achary/smolLM2-recipes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Susant-Achary/smolLM2-recipes with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M")
model = PeftModel.from_pretrained(base_model, "Susant-Achary/smolLM2-recipes")

Transformers

How to use Susant-Achary/smolLM2-recipes with Transformers:

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

pipe = pipeline("text-generation", model="Susant-Achary/smolLM2-recipes")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Susant-Achary/smolLM2-recipes", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Susant-Achary/smolLM2-recipes with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Susant-Achary/smolLM2-recipes"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Susant-Achary/smolLM2-recipes",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Susant-Achary/smolLM2-recipes

SGLang

How to use Susant-Achary/smolLM2-recipes 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 "Susant-Achary/smolLM2-recipes" \
    --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": "Susant-Achary/smolLM2-recipes",
		"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 "Susant-Achary/smolLM2-recipes" \
        --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": "Susant-Achary/smolLM2-recipes",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Susant-Achary/smolLM2-recipes with Docker Model Runner:
```
docker model run hf.co/Susant-Achary/smolLM2-recipes
```

Model Card for Susant-Achary/smolLM2-recipes

This is a 360-million-parameter causal language model, fine-tuned with LoRA adapters on a cleaned Indian recipe dataset. It generates step-by-step cooking instructions in English.

Model Details

Model Description

A compact transformer-decoder causal language model (360M parameters) originally from HuggingFaceTB/SmolLM2-360M, enhanced with low-rank adapter finetuning (LoRA) on ~22K Indian recipes (EmTpro01/indian-recipe-cleaned). Ideal for generating detailed, structured recipe instructions.

Developed by: Susant Achary
Model type: Causal Language Model (decoder-only transformer)
Language(s): English
License: Apache-2.0
Finetuned from: HuggingFaceTB/SmolLM2-360M

Model Sources

Hugging Face Hub: https://huggingface.co/Susant-Achary/smolLM2-recipes
Dataset: EmTpro01/indian-recipe-cleaned on Hugging Face Datasets

Uses

Direct Use

Generate detailed, step-by-step Indian recipe instructions given a recipe title and ingredient list.
Integrate into cooking chatbots or recipe suggestion systems.

Downstream Use

Fine-tune further for multilingual or specialized dietary versions.
Plug into kitchen assistant apps or voice-controlled cooking aids.

Out-of-Scope Use

Nutritional analysis or precise caloric computation (model does not predict nutritional values).
Medical or allergy advice—recommend consulting a qualified professional.

Bias, Risks, and Limitations

The model reflects the distribution of the training data (predominantly North and South Indian recipes). It may reproduce cultural biases (e.g., under-representation of certain regional cuisines) and occasional inconsistencies in ingredient quantities or cooking times.

Recommendations

Validate generated recipes before use.
Provide user feedback loops to catch and correct errors.
Augment with nutritional or safety checks from external sources.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("Susant-Achary/smolLM2-recipes")
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained("Susant-Achary/smolLM2-recipes", device_map="auto")

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = (
    "### Recipe: Masala Dosa\n"
    "### Ingredients:\n"
    "rice, urad dal, fenugreek seeds, potato, onion, mustard seeds, curry leaves\n\n"
    "### Instructions:\n"
)
print(generator(prompt, max_length=200, do_sample=True, top_p=0.9))

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Model tree for Susant-Achary/smolLM2-recipes

Base model

HuggingFaceTB/SmolLM2-360M

Adapter

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