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--- |
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license: mit |
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datasets: |
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- microsoft/rStar-Coder |
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- deepseek-ai/DeepSeek-ProverBench |
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language: |
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- en |
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metrics: |
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- accuracy |
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- bertscore |
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- character |
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- code_eval |
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base_model: |
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- deepseek-ai/deepseek-coder-6.7b-instruct |
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- stabilityai/stablecode-completion-alpha-3b-4k |
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tags: |
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- code |
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--- |
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# Model Card for Lara — Hybrid Code Model (DeepSeek + StableCode) |
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Lara is a hybrid fine‑tuned **code generation & completion model** built from |
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**DeepSeek‑Coder 6.7B** and **StableCode Alpha 3B‑4K**. |
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Designed for **general‑purpose programming** — from quick completions to multi‑file scaffolding — |
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and optionally capable of **Chandler Bing‑style sarcastic commentary** for developer amusement. |
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MIT licensed — free to use, modify, and redistribute. |
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--- |
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## Model Details |
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- **Developed by:** [@dgtalbug](https://huggingface.co/dgtalbug) |
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- **Funded by:** Self‑funded |
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- **Shared by:** [@dgtalbug](https://huggingface.co/dgtalbug) |
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- **Model type:** Causal Language Model for code generation & completion |
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- **Language(s):** English (primary), multilingual code comments possible |
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- **License:** MIT |
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- **Finetuned from:** |
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- [`deepseek-ai/deepseek-coder-6.7b-instruct`](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) |
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- [`stabilityai/stablecode-completion-alpha-3b-4k`](https://huggingface.co/stabilityai/stablecode-completion-alpha-3b-4k) |
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--- |
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## Model Sources |
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- **Repository:** [https://huggingface.co/dgtalbug/lara](https://huggingface.co/dgtalbug/lara) |
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- **Paper:** N/A (based on open‑source models) |
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- **Demo:** Coming soon |
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--- |
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## Uses |
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### Direct Use |
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- Code completion in IDEs |
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- Script & function generation |
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- Annotated code examples for learning |
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- Humorous coding commentary (optional, via prompt) |
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### Downstream Use |
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- Fine‑tune for a single language (e.g., Java‑only bot) |
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- Integrate into AI coding assistants |
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- Educational & training platforms |
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### Out‑of‑Scope Use |
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- Malicious code generation |
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- Non‑code general chat |
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- Security‑critical code without review |
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--- |
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## Bias, Risks, and Limitations |
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- May hallucinate APIs or syntax |
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- Humor mode may inject irrelevant lines |
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- Biases from public code sources may appear in output |
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### Recommendations |
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- Always review generated code before deployment |
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- Use sarcasm mode in casual or learning contexts, not production |
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- Test code in sandbox environments |
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--- |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_id = "dgtalbug/lara" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto") |
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prompt = "Write a Python function to reverse a string" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=150) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
