from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dogman189/odysseus-2-chat"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "User: What is 2+2?\n\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Chat Format

Base model: <a href="https://huggingface.co/dogman189/odysseus-2" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">dogman189/odysseus-2
Fine-tuning: LoRA (rank 8, alpha 16) on Alpaca-cleaned
Architecture: GPT-2 ( GPT2LMHeadModel )
Parameters: ~320M
Context length: 512 tokens
Vocabulary: 16,000 BPE tokens

The model was fine-tuned on the format:

User: {instruction}

Assistant: {response}

Training Details

LoRA fine-tune of the base model on alpaca-cleaned (51,760 examples)
LoRA rank 8, alpha 16, dropout 0.1, targeting c_proj, c_fc, c_attn
6 epochs (default)

Limitations

Small model fine-tuned on a limited chat dataset. Responses may be inaccurate or nonsensical. Not intended for production use.