metadata

license: mit
datasets:
  - microsoft/rStar-Coder
  - deepseek-ai/DeepSeek-ProverBench
language:
  - en
metrics:
  - accuracy
  - bertscore
  - character
  - code_eval
base_model:
  - deepseek-ai/deepseek-coder-6.7b-instruct
  - stabilityai/stablecode-completion-alpha-3b-4k
tags:
  - code

Model Card for Lara — Hybrid Code Model (DeepSeek + StableCode)

Lara is a hybrid fine‑tuned code generation & completion model built from
DeepSeek‑Coder 6.7B and StableCode Alpha 3B‑4K.
Designed for general‑purpose programming — from quick completions to multi‑file scaffolding —
and optionally capable of Chandler Bing‑style sarcastic commentary for developer amusement.

MIT licensed — free to use, modify, and redistribute.

Model Details

Developed by: @dgtalbug
Funded by: Self‑funded
Shared by: @dgtalbug
Model type: Causal Language Model for code generation & completion
Language(s): English (primary), multilingual code comments possible
License: MIT
Finetuned from:
- deepseek-ai/deepseek-coder-6.7b-instruct
- stabilityai/stablecode-completion-alpha-3b-4k

Model Sources

Repository: https://huggingface.co/dgtalbug/lara
Paper: N/A (based on open‑source models)
Demo: Coming soon

Uses

Direct Use

Code completion in IDEs
Script & function generation
Annotated code examples for learning
Humorous coding commentary (optional, via prompt)

Downstream Use

Fine‑tune for a single language (e.g., Java‑only bot)
Integrate into AI coding assistants
Educational & training platforms

Out‑of‑Scope Use

Malicious code generation
Non‑code general chat
Security‑critical code without review

Bias, Risks, and Limitations

May hallucinate APIs or syntax
Humor mode may inject irrelevant lines
Biases from public code sources may appear in output

Recommendations

Always review generated code before deployment
Use sarcasm mode in casual or learning contexts, not production
Test code in sandbox environments

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dgtalbug/lara"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")

prompt = "Write a Python function to reverse a string"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))