AnveshAI/AnveshAI-Ritchie-0.1B

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Ritchie is a compact 100.7M-parameter language model purpose-built for C programming — named after Dennis Ritchie, the creator of the C language. Built on the LLaMA architecture, Ritchie is designed to generate C code from natural language task descriptions.

Small enough to run on any device — including edge devices like smartphones, tablets, Raspberry Pi, and other embedded hardware. At just ~200MB (FP16), Ritchie brings C code generation to devices where larger models simply can't run. No GPU required — it runs comfortably on CPU.

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Highlights

• 100.7M parameters — one of the smallest dedicated C code generation models available
• ~200MB model size (FP16) — fits in memory on virtually any device
• Runs on CPU — no GPU required, works on laptops, phones, edge devices, and embedded systems
• LLaMA architecture — well-supported by HuggingFace Transformers, llama.cpp, ONNX, and other runtimes
• Task-oriented special tokens — structured prompting with <|task|>, <|code|>, <|plan|>, <|bug|>, <|fix|> tokens
• 2048 token context window

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Model Details

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Quick Start

Installation

pip install transformers torch

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "AnveshAI/AnveshAI-Ritchie-0.1B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
)
model.eval()

prompt = "<|task|>Write a C function to compute the nth Fibonacci number<|code|>"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.4,
        top_p=0.95,
        repetition_penalty=1.15,
        pad_token_id=tokenizer.pad_token_id,
    )

generated = output[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

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Special Tokens

Ritchie uses structured special tokens for different stages of the code generation pipeline:

Prompt Format

<|task|>Your natural language description here<|code|>

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Tips for Best Results

1. Use repetition_penalty=1.15 — helps prevent repetitive output patterns
2. Keep max_new_tokens at 256 — the model works best with shorter outputs
3. Use temperature=0.4 — lower temperature produces more focused code
4. Use the <|task|>...<|code|> format — this is the prompt format the model was trained on
5. Simple, direct prompts work best — e.g., "Write a C function to swap two integers using pointers"

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Limitations

• 100M scale — this is a small model; expect imperfect outputs on complex tasks
• C language only — not trained for other programming languages
• May produce repetitive output — use repetition_penalty to mitigate
• Not instruction-tuned — this is a base code generation model, not a chatbot
• May not always follow the task — task adherence is limited at this model scale

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Training Details

• Base Architecture: LLaMA (custom configuration)
• Training Data: C language source code corpus
• Optimizer: AdamW
• Precision: FP16 mixed precision
• Best Checkpoint: Step 3,200 (selected by lowest eval loss)
• Hardware: Kaggle GPU environment

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Citation

@misc{anveshai-ritchie-2026,
  title={AnveshAI Ritchie 0.1B},
  author={AnveshAI},
  year={2026},
  url={https://huggingface.co/AnveshAI/Ritchie-0.1B}
}

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License

This model is released under the Apache 2.0 License.

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Built by AnveshAI
Named after Dennis Ritchie — the father of C