You can deploy this model and start earning money today!

htigenai/
reflect_one_4bit

Name: reflect_one_4bit
Rating: 2.5 (6 reviews)

bigscience-bloom-rail-1.0

4-bit

bitsandbytes

Views

No views yet

Reflect-One: Fine-tuned Llama 3.1 for Educational Reflection Feedback

This model is a fine-tuned version of Meta's Llama 3.1 8B, specifically optimized for providing thoughtful, constructive feedback on student reflections in interdisciplinary project-based learning environments.

Model Details

Base Model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
Training Type: Supervised Fine-Tuning (SFT) using QLoRA
Context Length: 4096 tokens
Training Date: March 2025
License: Llama 3.1 Community License

Intended Use

Primary Use: Providing constructive feedback on student reflections in interdisciplinary projects
Intended Users: Educational institutions, instructors, and learning platforms
Out-of-Scope Use: Any use outside of educational feedback contexts

Training Details

Fine-tuning Approach

QLoRA fine-tuning with 4-bit quantization
LoRA rank: 16
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training batch size: 8
Gradient accumulation steps: 4
Learning rate: 3e-4
Training epochs: 3

Training Data

The model was fine-tuned on a curated dataset of student reflections and expert feedback, focusing on:

Interdisciplinary project work
Professional identity development
Stakeholder collaboration
Problem-solving in ambiguous situations

Performance and Limitations

Capabilities

Provides structured, constructive feedback aligned with learning outcomes
Maintains consistent narrative style
Identifies key learning moments in student reflections
Suggests areas for deeper reflection

Limitations

Limited to English language feedback
Should not be used as the sole source of feedback
May not capture course-specific technical requirements
Limited by the context window of 4096 tokens

Ethical Considerations

This model inherits the ethical considerations of the base Llama 3.1 model and adds specific considerations for educational use:

Should be used as a supplementary tool, not a replacement for human instructors
Feedback should be reviewed for appropriateness and accuracy
Model outputs should be transparent to students
Privacy considerations for student data must be maintained

Versions Available

Two versions of the model are available:

htigenai/reflect_one: 16-bit version for higher precision
htigenai/reflect_one_4bit: 4-bit quantized version for efficient deployment

Example Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "htigenai/reflect_one"  # or "htigenai/reflect_one_4bit"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16
)

# Format input following Llama 3.1 template
input_text = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a reflective instructor for ISBEP.<|eot_id|><|start_header_id|>user<|end_header_id|>
[Student reflection here]<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""

# Generate feedback
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Citation

If you use this model in your research, please cite:

bibtex

@misc{reflect_one_2025,
  title={ReflectOne: Fine-tuned Llama 3.1 for Educational Reflection Feedback},
  author={Oliveira, M.J.B., Ruijten - Dodoiu, P.},
  year={2025},
  publisher={unpublished}
}