Model card lengkap (angka dari manifest_qwen38.json)

README.md

@@ -1,22 +1,121 @@
----
-base_model: unsloth/Qwen3.8-27B-unsloth-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen3_5
-- trl
-license: apache-2.0
-language:
-- en
----
-
-# Uploaded  model
-
-- **Developed by:** TheCoderScientist
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen3.8-27B-unsloth-bnb-4bit
-
-This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+---
+license: apache-2.0
+base_model: unsloth/Qwen3.8-27B-unsloth-bnb-4bit
+language:
+- id
+library_name: peft
+tags:
+- lora
+- qlora
+- unsloth
+- code
+- indonesian
+- qwen3
+pipeline_tag: text-generation
+---
+
+# GarudaCoder-27B 🦅 — Indonesian Coding Assistant
+
+QLoRA adapter (r=8, alpha=8) distilled onto **Qwen3.8-27B** (unsloth 4-bit build) — successor of [GarudaCoder-7B-Coder-ID-lora](https://huggingface.co/TheCoderScientist/GarudaCoder-7B-Coder-ID-lora). Fine-tuned on curated high-quality reasoning traces from two teachers (Fable 5 + Hermes gpt-5.6-sol). Focus: explaining the reasoning **before** writing code, honesty about uncertainty (anti-hallucination), and Indonesian developer context. All model output is in Indonesian.
+
+## Trained capabilities
+- Chain-of-thought: root cause → alternatives + trade-offs → solution
+- Self-correction: draft → mental test → find its own bug → fix
+- Multi-turn debugging, asking clarification on ambiguous requests, refusing misguided requests
+- Grounded answers: architecture, debugging workflows, and Indonesian edge cases
+
+## Usage
+
+> **IMPORTANT — avoid stuck downloads:** the HF Xet backend is known to stall at the `Reconstructing (incomplete total...)` stage ([xet-core#850](https://github.com/huggingface/xet-core/issues/850)). Disable Xet **before any imports**:
+
+```python
+import os
+os.environ["HF_HUB_DISABLE_XET"] = "1"  # MUST be the very first cell/line
+```
+
+### Via Unsloth (recommended)
+
+```python
+from unsloth import FastModel
+
+model, tokenizer = FastModel.from_pretrained(
+    "TheCoderScientist/GarudaCoder-27B-ID-lora",
+    max_seq_length=1024, load_in_4bit=True,
+)
+FastModel.for_inference(model)
+
+pesan = [
+    {"role": "system", "content": "<copy the system prompt from the section below — use verbatim>"},
+    {"role": "user", "content": "Kenapa [[0]*3]*3 di Python bikin semua baris ikut berubah?"},
+]
+inputs = tokenizer.apply_chat_template(pesan, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
+out = model.generate(input_ids=inputs, max_new_tokens=1024, temperature=0.3, do_sample=True)
+print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+### Via transformers + PEFT (without Unsloth)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+
+bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
+                         bnb_4bit_compute_dtype=torch.float16)
+base = AutoModelForCausalLM.from_pretrained(
+    "unsloth/Qwen3.8-27B-unsloth-bnb-4bit", quantization_config=bnb, device_map="auto")
+model = PeftModel.from_pretrained(base, "TheCoderScientist/GarudaCoder-27B-ID-lora")
+tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3.8-27B-unsloth-bnb-4bit")
+```
+
+## System prompt
+
+Use verbatim — the model was trained with it; without it, reasoning quality degrades.
+
+```
+Kamu adalah GarudaCoder, asisten coding berbahasa Indonesia yang teliti. Utamakan kebenaran teknis dan kode yang dapat diuji. Jelaskan ringkasan alasan yang dapat diaudit sebelum solusi, nyatakan asumsi dan edge case yang relevan, dan jangan mengarang fakta, API, versi, atau angka. Jika informasi tidak cukup, ajukan pertanyaan klarifikasi. Untuk informasi yang berubah atau bersifat lokal, minta atau gunakan sumber yang dapat diverifikasi. Tolak praktik berbahaya dan tawarkan alternatif yang aman. Jangan tampilkan chain-of-thought privat.
+```
+
+## Important notes
+
+- Sampling config used during eval:
+
+```json
+{
+  "max_new_tokens": 1024,
+  "do_sample": true,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20,
+  "repetition_penalty": 1.0
+}
+```
+
+- VRAM: 4-bit weights ≈ 20.5 GiB total → needs **2× T4 (16 GB)** or a single 24 GB+ GPU. Does not fit on one T4.
+- This is a **LoRA adapter (PEFT)**, not a full model — the Qwen3.8-27B 4-bit base is downloaded automatically on load.
+- On T4 (no bfloat16) this architecture trains in float32 via Unsloth; 4-bit inference works fine.
+- Full provenance (per-file dataset SHA256, seeds, revisions) is committed in this repo at `training/manifest_qwen38.json`.
+
+## Troubleshooting
+
+| Symptom | Cause | Fix |
+|---|---|---|
+| Download stuck at `Reconstructing (incomplete total...)` | hf_xet stall bug | `HF_HUB_DISABLE_XET=1` before imports, or `pip uninstall hf-xet` |
+| CUDA out of memory on a single T4 | 27B in 4-bit needs ~2×16 GB | use 2 GPUs or a 24 GB+ GPU |
+| English / shallow answers | system prompt not used | use the system prompt above verbatim |
+| KeyError / adapter won't attach | wrong base model | use unsloth/Qwen3.8-27B-unsloth-bnb-4bit (pinned revision in manifest) |
+
+## Training
+
+- QLoRA 4-bit, r=8, alpha=8, max_seq 1024, 1 epoch, 229 examples, Kaggle T4×2
+- SFT with train_on_responses_only (instruction/response masking)
+- Base pinned: unsloth/Qwen3.8-27B-unsloth-bnb-4bit @ 8aa5f05d26b7205477066e1449e0af13f762a299 (upstream Qwen/Qwen3.8-27B @ 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0)
+- Seed 42 · environment: kaggle-t4x2 · Python 3.12.13 · torch 2.10.0+cu128 · CUDA 12.8 · VRAM 29.12 GiB
+- Holdout: 80 examples kept out of training entirely
+- Dataset provenance (SHA256 in manifest): distilasi_fable, penguatan_batch8, reasoning_batch9, hermes_debugging, hermes_grounding, hermes_architecture, hermes_edge_indonesia
+- Anti-leakage: holdout/benchmark files gated (FILE_DILARANG_UNTUK_TRAINING)
+
+## Lineage
+- Predecessor: [GarudaCoder-7B-Coder-ID-lora](https://huggingface.co/TheCoderScientist/GarudaCoder-7B-Coder-ID-lora) (Qwen2.5-Coder-7B)
+- Teacher traces: Fable 5 + Hermes gpt-5.6-sol
+- This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)