---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
- grpo
- lora
- transformers
- trl
- unsloth
- fitness
- nutrition
- reasoning
- rl
license: apache-2.0
---

# Model Card for Fitness Agent (14B-Qwen2.5)

<!-- Provide a quick summary of what the model is/does. -->
This is a fine-tuned LoRA adapter for `unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit`, trained to act as a specialized **Fitness & Nutrition Agent**. The model was trained using **Group Relative Policy Optimization (GRPO)** to improve its reasoning capabilities in creating personalized workout plans, analyzing nutrition logs, and providing evidence-based health advice.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
This model is an RL-finetuned version of Qwen 2.5 14B designed to solve complex fitness and nutrition queries. Unlike standard LLMs, this agent was trained with specific rewards for:
1.  **Reasoning Quality:** Producing logical, step-by-step explanations for its recommendations.
2.  **Safety & Constraints:** Strictly adhering to dietary restrictions (allergies, preferences) and physical limitations.
3.  **Format Compliance:** Generating structured JSON outputs for workout plans and diet logs when required.

It uses the LangGraph framework to manage agent state and tool invocation during training.

- **Developed by:** socaitcy
- **Funded by [optional]:** Self-funded
- **Model type:** LoRA Adapter (Fine-tuned Causal LM)
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit

### Model Sources [optional]

- **Repository:** https://huggingface.co/socaitcy/fitness-agent-14B-qwen2.5-adapter

## Uses

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is intended to be used as a conversational assistant or API backend for:
- Generating personalized weekly workout routines.
- Calculating macronutrient needs based on user stats.
- Answering questions about exercise form and dietary science.

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
Integrated into the `fitness-reasoning-rl-agent` system, where it can call external tools (search, database lookups) to augment its answers with real-time data.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
- **Medical Advice:** This model is for fitness and wellness coaching only. It is **not** a substitute for professional medical advice, diagnosis, or treatment.
- **Extreme Diets:** The model should not be used to generate dangerous or extreme weight loss protocols.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- **Hallucination:** Like all LLMs, it can occasionally invent facts or exercises that do not exist.
- **Knowledge Cutoff:** Its knowledge is limited to the base model's training data plus the fine-tuning dataset; it may not know the very latest fitness trends unless provided via context.
- **User Physiology:** It relies on user-provided data (weight, age, etc.) and cannot verify physical health status.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should always consult with a physician before starting any new exercise or nutrition program generated by this model.

## How to Get Started with the Model

Use the code below to get started with the model.

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

config = PeftConfig.from_pretrained("socaitcy/fitness-agent-14B-qwen2.5-adapter")
base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit", device_map="auto", load_in_4bit=True)
model = PeftModel.from_pretrained(base_model, "socaitcy/fitness-agent-14B-qwen2.5-adapter")
tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit")

prompt = "Create a 3-day workout plan for a beginner with no equipment."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The model was trained on a custom dataset of fitness scenarios (`data/fitness_scenarios.jsonl`), including:
- Synthetic user profiles with specific goals (e.g., "Lose 5kg", "Marathon prep").
- Validated nutritional constraints (e.g., "Vegan", "Gluten-free").
- Correct vs. incorrect workout split logic.

### Training Procedure

#### Preprocessing [optional]

Data was formatted into specific prompt templates used by the agent system to simulate user interactions.

#### Training Hyperparameters

- **Training regime:** Mixed precision (bf16) with LoRA (Rank=8, Alpha=16).
- **Optimizer:** AdamW 8-bit
- **Method:** GRPO (Group Relative Policy Optimization)
- **Quantization:** 4-bit (BitsAndBytes)

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

- **Hardware Type:** NVIDIA GPU (e.g., H100/A100/4090)
- **Hours used:** ~2-10 hours (Estimated)
- **Cloud Provider:** Private / Local
- **Compute Region:** Local

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

@misc{fitness-agent-2025,
  author = {socaitcy},
  title = {Fitness Agent 14B (Qwen2.5 LoRA)},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Repository},
  howpublished = {\url{https://huggingface.co/socaitcy/fitness-agent-14B-qwen2.5-adapter}}
}### Framework versions

- PEFT 0.18.0
- Transformers
- Unsloth
- TRL