初始化项目，由ModelHub XC社区提供模型
Model: BhinekaIntiLabs/bhineka-gpt Source: Original Platform
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FROM ./bhineka-gpt.gguf
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TEMPLATE """<|user|> {{ .Prompt }}<|sep|><|assistant|>"""
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PARAMETER temperature 0.8
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PARAMETER top_p 0.9
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PARAMETER num_predict 256
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PARAMETER repeat_penalty 1.0
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PARAMETER stop "<|sep|>"
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PARAMETER stop "<|eos|>"
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PARAMETER stop "<|endoftext|>"
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---
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language:
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- id
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- causal-lm
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- text-generation
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- indonesian
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- english
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- bilingual
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- llama-style
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- bhineka
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datasets:
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- HuggingFaceFW/fineweb-edu
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- HuggingFaceFW/fineweb
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- uonlp/CulturaX
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- allenai/c4
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- wikimedia/wikipedia
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- codeparrot/github-code
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- open-web-math/open-web-math
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---
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# Bhineka-GPT-500M
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Bhineka-GPT-500M is a bilingual Indonesian-English causal language model built with a Llama-style decoder-only architecture. The final pretraining checkpoint contains 556.3M trainable parameters and was validated on 53.7M held-out tokens across English web, Indonesian web, code, and math domains, reaching an overall validation loss of 2.5355 and perplexity of 12.62.
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The project is designed as an end-to-end training pipeline, covering dataset download, text cleaning, language filtering, deduplication, tokenizer training, shard building, curriculum sampling, pretraining, supervised fine-tuning, direct preference optimization, and final Hugging Face export.
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The model is intended for Indonesian and English text generation tasks such as question answering, summarization, rewriting, translation, technical drafting, code assistance, document understanding, and structured Markdown or JSON-style responses.
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## Model Details
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- Model type: decoder-only causal language model
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- Architecture: Llama-style Transformer
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- Languages: Indonesian and English
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- Parameters: 556,269,696 in the validated final pretraining checkpoint
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- Context length: 2048 tokens
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- Tokenizer: BPE tokenizer trained for this project
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- Vocabulary size: 64,000 tokens in the current pipeline config
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- Hidden size: 1152
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- Layers: 28
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- Attention heads: 16
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- Key/value heads: 8, using grouped-query attention
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- Feed-forward size: 3072, using SwiGLU-style activation
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- Positional encoding: RoPE
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- Normalization: RMSNorm
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- Precision target: bfloat16
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- Validation checkpoint: `checkpoints/pretrain/final`
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## Training Pipeline
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Bhineka-GPT-500M is produced through the following stages:
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1. Dataset download from public Hugging Face datasets
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2. Rule-based cleaning and quality filtering
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3. Exact and MinHash deduplication
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4. BPE tokenizer training
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5. Binary shard creation
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6. Domain-weighted curriculum sampling
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7. Causal language model pretraining
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8. Supervised fine-tuning on instruction/chat datasets
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9. Direct Preference Optimization
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10. Export to Hugging Face format with safetensors
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## Training Data
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The current project configuration targets a bilingual and technical mixture with approximately 12.5B total pretraining tokens:
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| Domain | Approximate Target Tokens | Purpose |
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|---|---:|---|
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| English high-quality web | 8.7B | General knowledge, reasoning, writing |
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| Indonesian high-quality web | 2.15B | Indonesian language coverage and local text style |
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| Code | 1.05B | Python, JavaScript, Go, SQL, and technical generation |
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| Math / academic | 600M | Mathematical and academic text exposure |
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Main pretraining sources include FineWeb, FineWeb-Edu, CulturaX Indonesian, mC4 Indonesian, Indonesian Wikipedia, GitHub code subsets, and OpenWebMath.
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Instruction tuning data configured in the project includes Alpaca-style and chat-style datasets such as Alpaca Cleaned, Dolly 15k, Alpaca Indonesian, Alpaca GPT-4 Indonesian, OpenHermes 2.5, and SlimOrca.
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## Intended Uses
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This model is intended for research, experimentation, and application prototyping in Indonesian-English language tasks, including:
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- General chat and instruction following
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- Indonesian and English question answering
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- Indonesian-English translation
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- Summarization and rewriting
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- Technical explanation and drafting
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- Python, JavaScript, Go, and SQL code assistance
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- Markdown and structured response generation
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## Out-of-Scope Uses
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This model should not be used as the sole source of truth for high-stakes decisions, including medical, legal, financial, safety-critical, or emergency contexts. It should also not be used to generate harmful instructions, impersonation, spam, fraud, or privacy-invasive content.
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## Limitations
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- The model may hallucinate facts, citations, code behavior, or numerical details.
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- Performance may vary across Indonesian dialects, informal registers, and domain-specific terminology.
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- The model can reflect biases and quality issues present in public web, code, math, and instruction datasets.
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- Smaller language models may struggle with long reasoning chains, complex tool use, and strict factuality.
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- The reported validation-loss results cover language-modeling loss only; broader instruction-following, safety, factuality, and downstream task evaluations are still recommended before production use.
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## Evaluation
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Validation loss was measured with `scripts/run_validation_loss.py` on the final pretraining checkpoint:
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- Checkpoint: `checkpoints/pretrain/final`
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- Evaluation date: 2026-05-31
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- Device: CUDA
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- Evaluation dtype: float32
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- Context length: 2048 tokens
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- Batch size: 4
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- Tokens evaluated: 53,678,481
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- Batches evaluated: 6,558
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- Tokenizer vocabulary: 64,000
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- Model vocabulary: 64,000
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- Random-loss baseline: 11.0666
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- Parameter check: 556,269,696 trainable parameters, no non-finite values reported
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| Domain | Loss | Perplexity | Tokens | Batches |
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|---|---:|---:|---:|---:|
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| Overall | 2.5355 | 12.6227 | 53,678,481 | 6,558 |
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| Code | 1.4304 | 4.1804 | 15,121,189 | 1,847 |
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| English high-quality web | 3.1551 | 23.4543 | 20,635,807 | 2,521 |
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| Indonesian high-quality web | 2.7256 | 15.2651 | 11,481,623 | 1,403 |
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| Math | 2.8062 | 16.5462 | 6,439,862 | 787 |
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## Benchmark Comparison
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The following benchmark table compares Bhineka-GPT with several small open-weight language models in the same approximate parameter range. These numbers should be read as an orientation benchmark rather than a perfectly fair leaderboard comparison, because evaluation harness settings, shot count, prompt format, checkpoint type, tokenizer, and instruction tuning status may differ across sources.
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For Bhineka-GPT, ARC, HellaSwag, and WinoGrande were evaluated in 0-shot mode, while GSM8K used 5-shot evaluation.
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| Model | Params | ARC | HellaSwag | WinoGrande | GSM8K | Notes |
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|---|---:|---:|---:|---:|---:|---|
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| **Bhineka-GPT** | 556M | **24.83** | **31.58** | **48.86** | **1.90** | 0-shot except GSM8K 5-shot |
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| Pythia-410M-deduped | ±410M / 0.5B | 27.90 | 40.04 | 52.09 | 0.00 | Open LLM Leaderboard-style evaluation, mostly few-shot [[1]] |
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| Pythia-1B-deduped | 1B | 29.10 | 49.65 | 53.59 | 1.14 | Larger model, trained with substantially more compute and data [[2]] |
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| TinyLlama-1.1B Chat | 1.1B | 36.09 | 61.10 | 61.25 | — | Pretrained on approximately 3T tokens; target training setup reported as 16×A100 for about 90 days [[3]] |
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| TinyLlama 1.1B variant | 1.1B | 30.29 | 55.12 | 55.80 | 0.53 | Fine-tuned variant, Open LLM Leaderboard-style evaluation [[4]] |
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| Qwen2-0.5B | ±0.5B non-embedding | 61.10 | 49.30 | 74.40 | 36.50 | Much more mature model family; not a fair direct comparison against a from-scratch sub-$100 training experiment [[5]] |
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Interpretation:
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- Bhineka-GPT is competitive enough to be a useful research baseline for a from-scratch 556M bilingual Indonesian-English model, especially considering its limited training budget.
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- Larger or more mature models such as TinyLlama, Pythia-1B, and Qwen2-0.5B benefit from more training tokens, more mature infrastructure, and/or larger-scale optimization.
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- The comparison is most useful for positioning Bhineka-GPT as a lightweight experimental bilingual model, not as a claim of state-of-the-art performance.
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These results measure next-token prediction quality on validation data. Recommended additional evaluations before release include Indonesian and English instruction-following benchmarks, translation quality checks, summarization and factuality tests, code generation tests, and safety testing.
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## Usage
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After export and upload, the model can be loaded with Transformers. Because this project defines a custom `bhineka` model architecture, the model repository may need to include the custom modeling files and be loaded with `trust_remote_code=True`.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo_id = "BhinekaIntiLabs/bhineka-gpt"
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tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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torch_dtype="auto",
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device_map="auto",
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trust_remote_code=True,
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)
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prompt = "<|user|> Jelaskan apa itu deduplikasi data dalam pelatihan model bahasa.<|sep|><|assistant|>"
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inputs = tokenizer(
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prompt,
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return_tensors="pt",
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add_special_tokens=False,
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).to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.8,
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top_p=0.9,
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repetition_penalty=1.1,
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use_cache=False,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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)
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completion = outputs[0, inputs["input_ids"].shape[1]:]
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print(tokenizer.decode(completion, skip_special_tokens=True))
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```
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The exporter saves the model in Hugging Face format with safetensors, tokenizer files, config files, and generation config.
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## License
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This model card declares the `apache-2.0` license in the Hugging Face metadata. Please ensure that all training data usage, code dependencies, and released model artifacts are compatible with this license before publishing.
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[1]: https://huggingface.co/postbot/emailgen-pythia-410m-deduped "postbot/emailgen-pythia-410m-deduped"
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[2]: https://huggingface.co/EleutherAI/pythia-1b-deduped/discussions/3/files "EleutherAI/pythia-1b-deduped · Adding Evaluation Results"
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[3]: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/blob/refs%2Fpr%2F32/README.md "README.md · TinyLlama/TinyLlama-1.1B-Chat-v1.0 at refs/pr/32"
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[4]: https://huggingface.co/lgaalves/tinyllama-1.1b-chat-v0.3_platypus "lgaalves/tinyllama-1.1b-chat-v0.3_platypus"
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[5]: https://huggingface.co/Qwen/Qwen2-0.5B "Qwen/Qwen2-0.5B"
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## Citation
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If you use this model or pipeline, cite the project repository:
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```bibtex
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@software{bhineka_llm_500m,
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title = {Bhineka-GPT-500M},
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author = {Bhineka-GPT contributors},
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year = {2026},
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note = {Bilingual Indonesian-English language model training pipeline}
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}
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```
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bhineka-gpt.gguf
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bhineka-gpt.gguf
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size 1114931904
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"model_type": "llama",
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"hidden_act": "silu",
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"hidden_size": 1152,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 2048,
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"rope_scaling": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.0",
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"use_cache": true,
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"vocab_size": 64000
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}
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{
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"do_sample": true,
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"temperature": 0.7,
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"top_p": 0.9,
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"repetition_penalty": 1.1,
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"transformers_version": "4.40.0"
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}
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model.safetensors
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|
||||
13
special_tokens_map.json
Normal file
13
special_tokens_map.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|system|>",
|
||||
"<|user|>",
|
||||
"<|assistant|>",
|
||||
"<|sep|>",
|
||||
"<|endoftext|>"
|
||||
],
|
||||
"bos_token": "<|bos|>",
|
||||
"eos_token": "<|eos|>",
|
||||
"pad_token": "<|pad|>",
|
||||
"unk_token": "<|unk|>"
|
||||
}
|
||||
319132
tokenizer.json
Normal file
319132
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
94
tokenizer_config.json
Normal file
94
tokenizer_config.json
Normal file
@@ -0,0 +1,94 @@
|
||||
{
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<|pad|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<|bos|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "<|eos|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"3": {
|
||||
"content": "<|unk|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"4": {
|
||||
"content": "<|system|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"5": {
|
||||
"content": "<|user|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"6": {
|
||||
"content": "<|assistant|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"7": {
|
||||
"content": "<|sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"8": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|system|>",
|
||||
"<|user|>",
|
||||
"<|assistant|>",
|
||||
"<|sep|>",
|
||||
"<|endoftext|>"
|
||||
],
|
||||
"bos_token": "<|bos|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|eos|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<|pad|>",
|
||||
"tokenizer_class": "PreTrainedTokenizerFast",
|
||||
"unk_token": "<|unk|>",
|
||||
"add_bos_token": false,
|
||||
"add_eos_token": false,
|
||||
"chat_template": "{%- for message in messages -%}{%- if message['role'] == 'system' -%}<|system|> {{ message['content'] }}<|sep|>{%- elif message['role'] == 'user' -%}<|user|> {{ message['content'] }}<|sep|>{%- elif message['role'] == 'assistant' -%}<|assistant|> {{ message['content'] }}<|eos|>{%- endif -%}{%- endfor -%}{% if add_generation_prompt %}<|assistant|> {% endif %}"
|
||||
}
|
||||
Reference in New Issue
Block a user