---
library_name: transformers
tags:
- philosophy
- art
datasets:
- ruggsea/stanford-encyclopedia-of-philosophy_chat_multi_turn
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
---
# Llama3-SEP-Chat: Philosophy Expert Assistant

This model is a LoRA-finetuned version of `meta-llama/Meta-Llama-3.1-8B-instruct` on a curated dataset of Stanford Encyclopedia of Philosophy (SEP) conversations. The model is designed to engage in philosophical discussions with a formal yet accessible tone, leveraging the comprehensive knowledge from SEP.

## Model Description

The model was trained using direct finetuning on the instruct variant of Llama 3, preserving its native chat format and instruction-following capabilities while enhancing its philosophical expertise.

### Training Dataset

The training data consists of multi-turn conversations derived from the Stanford Encyclopedia of Philosophy, formatted as chat interactions between a user and an assistant. The conversations maintain academic rigor while ensuring accessibility.

### Chat Format

The model uses Llama 3's native chat format, which is automatically applied by the tokenizer. No additional tokens or formatting were added during finetuning.

## Training Details

### Model Configuration
- Base Model: `meta-llama/Meta-Llama-3.1-8B-instruct`
- Training Type: LoRA (Low-Rank Adaptation)
- Quantization: 4-bit (NF4)
- Compute: Mixed Precision (bfloat16)

### Training Hyperparameters
- Learning Rate: 2e-5
- Train Batch Size: 16
- Gradient Accumulation Steps: 2
- Effective Batch Size: 32
- Optimizer: paged_adamw_8bit
- Training Epochs: 5
- Warmup Ratio: 0.03
- LoRA Configuration:
  - Rank: 256
  - Alpha: 128
  - Dropout: 0.05
  - Target: all-linear layers

### Framework Versions
- Transformers: latest
- PEFT: latest
- PyTorch: 2.1.0+cu121
- TRL: latest
- Accelerate: latest

## Usage

The model can be used with the standard Hugging Face transformers library:

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

model_id = "ruggsea/Llama3.1-Instruct-SEP-Chat"

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Format your input using the chat template
messages = [
    {"role": "user", "content": "What is the categorical imperative?"}
]

# Apply the chat template
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False
)

# Generate response
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.2,
    no_repeat_ngram_size=3,
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)
```

## Limitations

- The model's knowledge is primarily focused on philosophical concepts and may not perform as well on general topics
- As with all language models, it may occasionally generate incorrect or inconsistent information
- The model inherits any limitations and biases present in the base Llama 3 model and the SEP dataset

## License

This model is subject to the Meta Llama 3 license terms. Please refer to Meta's licensing for usage requirements and restrictions.