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celebibasar
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Model Details
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celebibasar
Model Tree
Qwen/Qwen2.5-Coder-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The model was trained with this system prompt on its highest-quality data. Use it for best results:
text
You are DroidSenior, a staff-level Android engineer. Write idiomatic, modern Kotlin (Jetpack Compose, Coroutines/Flow, Hilt, Room) following the official Android architecture guidance. Explain key design decisions and trade-offs briefly, never use deprecated APIs, avoid over-engineering, and include tests when they add value. Answer in the user's language.python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "celebibasar/DroidSenior-7B"SYSTEM_PROMPT = """You are DroidSenior, a staff-level Android engineer. Write idiomatic, modern Kotlin (Jetpack Compose, Coroutines/Flow, Hilt, Room) following the official Android architecture guidance. Explain key design decisions and trade-offs briefly, never use deprecated APIs, avoid over-engineering, and include tests when they add value. Answer in the user's language.""" tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": "Implement a paginated list screen with Compose, Room and Paging 3."},]text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1500, do_sample=True, temperature=0.3, top_p=0.9)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))bash
vllm serve celebibasar/DroidSenior-7B --max-model-len 16384bash
ollama run hf.co/celebibasar/DroidSenior-7B-GGUF:Q4_K_MFor the recommended system prompt, download Modelfile and the GGUF file from the GGUF repo and run
ollama create droid-senior -f Modelfile.
Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8) generated realistic questions and answered them with a detailed staff-engineer prompt.| source | examples | notes |
|---|---|---|
Synthetic senior Q&A distilled from Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 | 4143 | judge-filtered (kept 4143 of 8788) |
| JetBrains/KExercises | 4000 | filtered: Android/Kotlin, legacy APIs, dedup; license: CC-BY-NC-SA-4.0 (non-commercial) |
| giggiovpg/android-kotlin-compose-compiler-verified | 5767 | filtered: Android/Kotlin, legacy APIs, dedup |
| giggiovpg/ornith-android-instruct | 5176 | filtered: Android/Kotlin, legacy APIs, dedup |
| glaiveai/glaive-code-assistant-v3 | 586 | filtered: Android/Kotlin, legacy APIs, dedup |
| ise-uiuc/Magicoder-OSS-Instruct-75K | 33 | filtered: Android/Kotlin, legacy APIs, dedup |
| m-a-p/CodeFeedback-Filtered-Instruction | 49 | filtered: Android/Kotlin, legacy APIs, dedup |
| microsoft/NextCoderDataset | 5000 | filtered: Android/Kotlin, legacy APIs, dedup |
| theblackcat102/evol-codealpaca-v1 | 1566 | filtered: Android/Kotlin, legacy APIs, dedup |
| stage | method | lora_r | lora_alpha | epochs | learning_rate | effective_batch | max_seq_len | train_examples |
|---|---|---|---|---|---|---|---|---|
| SFT | LoRA (bf16) | 64 | 64 | 2 | 0.0001 | 32 | 8192 | 7793 |
| DPO | LoRA | 32 | 32 | 1 | 5e-06 | 16 | 8192 | 654 |
Evaluated on 150 held-out questions (never used in training), scored 1-10 by Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 with the same rubric for every model.
| model | correctness | modernity | architecture | code_quality | completeness | clarity | overall | legacy_api_rate |
|---|---|---|---|---|---|---|---|---|
| base | 6.91 | 6.13 | 5.92 | 6.09 | 5.56 | 6.6 | 5.84 | 0.09 |
| sft | 8.08 | 7.47 | 7.66 | 7.46 | 7.05 | 7.87 | 7.23 | 0.0 |
| dpo | 8.13 | 7.54 | 7.73 | 7.53 | 7.08 | 8.02 | 7.29 | 0.01 |
Head-to-head vs. the base model (overall score per question):
| model | win | tie | loss |
|---|---|---|---|
| sft | 76% | 17% | 7% |
| dpo | 73% | 23% | 5% |
Caveats: LLM-as-judge scores, judged by the same model family that generated the training data, on questions drawn from the same generator distribution. Treat them as a relative signal, not an absolute benchmark.
Released under apache-2.0. Base model: Qwen2.5-Coder-7B-Instruct (Apache-2.0). Training data includes non-commercially licensed sets (JetBrains/KExercises); check their terms before commercial use.
You are DroidSenior, a staff-level Android engineer. Write idiomatic, modern Kotlin (Jetpack Compose, Coroutines/Flow, Hilt, Room) following the official Android architecture guidance. Explain key design decisions and trade-offs briefly, never use deprecated APIs, avoid over-engineering, and include tests when they add value. Answer in the user's language.from transformers import AutoModelForCausalLM, AutoTokenizer repo = "celebibasar/DroidSenior-7B"SYSTEM_PROMPT = """You are DroidSenior, a staff-level Android engineer. Write idiomatic, modern Kotlin (Jetpack Compose, Coroutines/Flow, Hilt, Room) following the official Android architecture guidance. Explain key design decisions and trade-offs briefly, never use deprecated APIs, avoid over-engineering, and include tests when they add value. Answer in the user's language.""" tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": "Implement a paginated list screen with Compose, Room and Paging 3."},]text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1500, do_sample=True, temperature=0.3, top_p=0.9)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))