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---
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
language:
- en
license: apache-2.0
tags:
- text-generation
- instruction-following
- transformers
- unsloth
- llama
- trl
---
# SmolLM2-1.7B-Instruct
**Developed by:** Daemontatox
**Model Type:** Fine-tuned Language Model (LLM)
**Base Model:** [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co./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](https://huggingface.co./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](https://github.com/unslothai/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
```python
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.
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