SmolLM2-1.7B-Instruct

Developed by: Daemontatox

Model Type: Fine-tuned Language Model (LLM)

Base Model: HuggingFaceTB/SmolLM2-1.7B-Instruct

Finetuned from model: HuggingFaceTB/SmolLM2-1.7B-Instruct

License: apache-2.0

Languages: en

Tags:

text-generation
instruction-following
transformers
unsloth
llama
trl

Model Description

SmolLM2-1.7B-Instruct is a fine-tuned version of HuggingFaceTB/SmolLM2-1.7B-Instruct, optimized for general-purpose instruction-following tasks. This model combines the efficiency of the LLaMA architecture with fine-tuning techniques to enhance performance in:

Instruction adherence and task-specific prompts.
Creative and coherent text generation.
General-purpose reasoning and conversational AI.

The fine-tuning process utilized Unsloth and the Hugging Face TRL library, achieving a 2x faster training time compared to traditional methods. This efficiency allows for resource-conscious model updates while retaining high-quality performance.

Intended Uses

SmolLM2-1.7B-Instruct is designed for:

Generating high-quality text for a variety of applications, such as content creation and storytelling.
Following complex instructions across different domains.
Supporting research and educational use cases.
Serving as a lightweight option for conversational agents.

Limitations

While the model excels in instruction-following tasks, it has certain limitations:

May exhibit biases inherent in the training data.
Limited robustness for highly technical or specialized domains.
Performance may degrade with overly complex or ambiguous prompts.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "daemontatox/smollm2-1.7b-instruct"  # Replace with the actual model name

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
prompt = "Explain the importance of biodiversity in simple terms: "
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Acknowledgements

Special thanks to the Unsloth team for their tools enabling efficient fine-tuning. The model was developed with the help of open-source libraries and community resources.