---
license: apache-2.0
base_model: EleutherAI/pythia-410m
tags:
- peft
- lora
- pythia
- fine-tuned
- grey-chain
library_name: peft
---

# Amit-GC Trained Model

This is a LoRA fine-tuned version of EleutherAI/pythia-410m, trained on Grey Chain content about AI and digital transformation.

## Model Details

- **Base Model**: EleutherAI/pythia-410m (410M parameters)
- **Training Method**: LoRA (Low-Rank Adaptation)
- **Training Data**: Grey Chain content about AI & digital transformation
- **Trainable Parameters**: ~0.77% of base model

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load tokenizer and base model
tokenizer = AutoTokenizer.from_pretrained("akp4u/amit-gc-trained-model")
base_model = AutoModelForCausalLM.from_pretrained(
    "EleutherAI/pythia-410m",
    device_map="cpu",
    torch_dtype=torch.float32
)

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "akp4u/amit-gc-trained-model")

# Generate text
prompt = "What is Grey Chain?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Training Details

- Trained for 3 epochs on Grey Chain content
- Uses LoRA with r=8, alpha=16, dropout=0.05
- Optimized for Mac/CPU inference (no quantization required)

## Sample Outputs

The model can answer questions about:
- Grey Chain services and capabilities
- AI and digital transformation
- Machine learning concepts
- Prompt engineering

## Limitations

- Small model (410M parameters) - responses may be limited
- Trained on specific domain content
- Best for Grey Chain and AI-related queries