Write Python Code: Can generate simple functions, loops, and standard library usage.
Chat: Can handle basic conversational queries and explain concepts.
Switch Modes: Trained on a 50/50 mix of instruct-chat and raw code, allowing it to understand both natural language and programming syntax.

📦 How to Use

You can run Monke directly in Python using the transformers library:

python

from transformers import AutoTokenizer, AutoModelForCausalLM

# 1. Load Monke
model_name = "aaravriyer193/monke-0.4b-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# 2. Ask it to code
prompt = "def fibonacci(n):"
inputs = tokenizer(prompt, return_tensors="pt")

# 3. Generate
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚠️ Limitations

Size: At ~400M parameters, this model is significantly smaller than Llama-3 (8B) or GPT-4. It lacks deep reasoning capabilities.
Hallucinations: It may confidently invent Python libraries that do not exist or state incorrect facts.
Logic: While it understands syntax (grammar), it may struggle with complex logic puzzles or multi-step math problems.

📜 Training Data

The model was trained on a custom curated mix of:

50% General Instruct/Chat data (Alpaca style)
50% Python Code snippets (The Stack/CodeSearchNet)

Built with ❤️ and a single GPU.