---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
library_name: peft
tags:
- base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---

# GPT-OSS-20B Empathetic (LoRA Fine-tuned)

This model is a **LoRA fine-tuned adapter** built on top of [unsloth/gpt-oss-20b-unsloth-bnb-4bit](https://huggingface.co/unsloth/gpt-oss-20b-unsloth-bnb-4bit).  
It specializes in generating **empathetic and supportive responses**, making it suitable for conversational AI use cases where emotional awareness is important.

## Model Details

### Model Description

- **Developed by:** Anwesha026 
- **Shared by:** Anwesha026 
- **Model type:** Decoder-only Causal LM (LoRA adapter)
- **Language(s):** English
- **License:** Apache-2.0 
- **Finetuned from model [optional]:** unsloth/gpt-oss-20b-unsloth-bnb-4bit

### Model Sources

- **Repository:** [Anwesha026/fine-tuned-gpt-oss-20b](https://huggingface.co/Anwesha026/fine-tuned-gpt-oss-20b)  
- **Base Model:** [unsloth/gpt-oss-20b-unsloth-bnb-4bit](https://huggingface.co/unsloth gpt-oss-20b-unsloth-bnb-4bit)  

## Uses

### Direct Use
- Empathetic chatbots  
- Companion-like conversational assistants  
- Research in affective computing and emotionally aware dialogue  

### Downstream Use
- Integration into mental health support tools (with **human supervision**)  
- Conversational agents requiring emotionally supportive responses  

### Out-of-Scope Use
- Providing professional medical or psychological advice  
- Factual Q&A where high accuracy is required  
- Malicious or manipulative applications  

---

## Bias, Risks, and Limitations

Like most LLMs, this model may:
- Produce biased, stereotypical, or culturally insensitive outputs  
- Over-generalize empathetic responses  
- Hallucinate factual details  
- Fail in high-stakes or sensitive psychological contexts  

### Recommendations
- Always keep a **human in the loop** when deploying in sensitive domains  
- Do not use as a replacement for professional medical/psychological help  
- Carefully evaluate outputs before real-world use  

---

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
```
```
model_id = "Anwesha026/fine-tuned-gpt-oss-20b"
```
```
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)

inputs = tokenizer("I feel really lonely lately.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

```

## Training Details

### Training Data

**Dataset:** [facebook/empathetic_dialogues](https://huggingface.co/datasets/facebook/empathetic_dialogues)

### Training Procedure

#### Training Hyperparameters

- Batch size (per device): 1

- Gradient accumulation steps: 4 → effective batch size = 1 × 4 = 4

- Learning rate: 1e-4

- Optimizer: AdamW (8-bit)

- Weight decay: 0.01

- Learning rate scheduler: Linear

- Warmup steps: 10

- Max training steps: 300

- Seed: 3407

## Evaluation

### Results

- Improved empathetic alignment compared to the base model

- Some generic/repetitive answers persist

## Technical Specifications 

### Model Architecture and Objective

- Base model: GPT-OSS-20B (decoder-only transformer, 20B parameters)

- Fine-tuning method: LoRA adapters via PEFT

### Compute Infrastructure

#### Hardware

- NVIDIA GPU

#### Software

- Hugging Face Transformers, PEFT, TRL, Unsloth

## Model Card Authors 

- Anwesha026

## Model Card Contact

- Hugging Face:[@Anwesha026](https://huggingface.co/Anwesha026)

### Framework versions

- Transformers: 4.x

- PEFT: 0.17.1

- TRL: latest

- Unsloth: latest