commit 28dcb5ee113bdedd2695a611fb078d926353748d Author: ModelHub XC Date: Sun Sep 6 09:12:17 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: regnant-io/kw5-lite-base Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..3105900 --- /dev/null +++ b/README.md @@ -0,0 +1,182 @@ +--- +language: +- sw +license: apache-2.0 +library_name: transformers +tags: +- swahili +- kiswahili +- causal-lm +- base-model +- pretrained +- african-languages +- low-resource +datasets: +- HuggingFaceFW/fineweb-2 +pipeline_tag: text-generation +--- + +# KW5-Lite Base + +A 109.5M-parameter Swahili (Kiswahili) language model pretrained from scratch on +1.41B tokens, on a single NVIDIA T4. + +Built by [Regnant](https://www.regnant.io/). + +**This is a base model.** It does next-token prediction only. It does not follow +instructions and has no chat template. For that, use +[regnant-io/kw5-lite-instruct](https://huggingface.co/regnant-io/kw5-lite-instruct). + +## Quick start + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +model = AutoModelForCausalLM.from_pretrained("regnant-io/kw5-lite-base") +tok = AutoTokenizer.from_pretrained("regnant-io/kw5-lite-base") + +# Base model: give it a prefix to continue, not an instruction. +# Write into the text — the tokenizer maps it to id 1. +inputs = tok("Tanzania ni nchi", return_tensors="pt", add_special_tokens=False) + +out = model.generate(**inputs, max_new_tokens=60) +print(tok.decode(out[0], skip_special_tokens=True)) +``` + +``` +Tanzania ni nchi ya amani na utulivu. Ni nchi yenye watu wengi, wenye nguvu +za kiuchumi, wenye uwezo wa kufanya maamuzi magumu kwa wakati mmoja... +``` + +`add_bos_token` is pinned to `false` so that the same tokenizer settings work +for the instruct model, which renders `` as part of its prompt template. +**Put `` at the start of your text yourself** — every training sequence +began with it. + +Write it into the string, as above, rather than concatenating the id onto +`input_ids` afterwards: that leaves `attention_mask` one element shorter than +`input_ids`, and `generate` then fails inside RoPE with +`The size of tensor a (4) must match the size of tensor b (3)`. + +Like the instruct model, this one needs a low temperature and a repetition +penalty to stay coherent; the shipped `generation_config.json` defaults to +temperature 0.2 / `repetition_penalty` 1.3. + +## Model details + +| | | +|---|---| +| Parameters | 109.5M (tied input/output embeddings) | +| Architecture | Llama-compatible decoder-only transformer | +| Layers / hidden / FFN | 12 / 768 / 2048 | +| Attention heads | 12 query, 12 key-value — **standard multi-head attention, not GQA** | +| Normalization | RMSNorm, pre-norm | +| Activation | SwiGLU | +| Position encoding | RoPE, theta 10000 | +| Context length | 2048 | +| Vocabulary | 32,000 SentencePiece BPE | +| Precision | FP32 (trained under FP16 autocast with an FP32 master copy) | +| File size | 438 MB | + +> A previous revision of this card claimed "KV heads: 4 (Grouped Query +> Attention)". That was wrong — `num_key_value_heads` is 12 and always has +> been. This model uses standard MHA. Corrected here. + +### Tokenizer + +32k BPE trained from scratch on the deduplicated Swahili corpus. NFC +normalization applied before training (SentencePiece's own rules are all +NFKC-family, which is lossier), `byte_fallback` so no input can hard-fail, and +digits split. + +Special tokens are fixed at low ids: ``=0, ``=1, ``=2, ``=3. + +Ids 4–6 are `<|system|>`, `<|user|>`, `<|assistant|>`, **reserved but never +trained** — they do not occur in the pretraining corpus, so their embedding +rows are still at initialisation. If you fine-tune and want to use them as +chat-role markers, you must unfreeze `embed_tokens` (or add it to your LoRA +`modules_to_save`), or the model will read them as noise. + +## Training + +| | | +|---|---| +| Data | [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) `swh_Latn`, cleaned and deduplicated (exact + MinHash LSH) | +| Tokens seen | 1.41B — exactly 2 epochs over the corpus | +| Checkpoint | step 6,150 | +| Context length | 1024 during pretraining, extended to 2048 via NTK-aware RoPE rescaling | +| Precision | FP16 mixed precision with GradScaler (T4/Turing has no bf16 tensor cores) | +| Optimizer | 8-bit AdamW (bitsandbytes), lr 3e-4, weight decay 0.1, grad clip 1.0 | +| Schedule | Warmup-Stable-Decay, stopped at the end of the stable phase | +| Hardware | 1× NVIDIA T4 (16 GB), ~60 GPU-hours across resumable 5-hour sessions | + +**On the schedule:** WSD normally ends with an LR decay phase. Training was +stopped at step 6,150 — exactly two epochs — because validation quality began +degrading past that point, so the decay was deliberately not run. Released +checkpoints from later in the run overfit. + +## Evaluation + +| Task | Metric | KW5-Lite Base | Chance | +|---|---|---|---| +| Belebele-sw (4-way) | Accuracy | 32% | 25% | +| AfriXNLI-sw (3-way) | Accuracy | 32% | 33% | + +*n = 50 per task.* + +**Interpret these honestly: this model is at or near chance on both.** With +n=50 the standard error is about ±6.6 points, so 32% vs 25% on Belebele is not +a reliable signal, and AfriXNLI is exactly at chance. A 110M model trained on +1.41B tokens is not expected to do multiple-choice reasoning; these numbers +establish a floor, not a capability. + +What the model is genuinely good at is fluent, well-formed Swahili +continuation. That is what makes it a useful base to fine-tune, and it is what +the [instruct model](https://huggingface.co/regnant-io/kw5-lite-instruct) +builds on. + +## Intended use + +Intended as a **starting point for Swahili fine-tuning** — instruction tuning, +domain adaptation, classification heads — where training from scratch is too +expensive and larger multilingual models are too big to serve. + +Not intended for direct deployment: no instruction following, no safety tuning, +no factual reliability. + +## Limitations + +- **Not an instruction model.** It continues text; it does not answer questions. +- **Factual reliability is poor.** Do not use as a knowledge source. +- **Primarily Tanzanian Swahili**, reflecting the corpus distribution. +- **No safety tuning at all.** It will reproduce harmful, biased or explicit + content present in web text. +- Trained on web-scraped data and carries its biases and quality artifacts. + +## Files + +| File | What it is | +|---|---| +| `model.safetensors` | FP32 weights, tied embeddings, 438 MB | +| `tokenizer.model` | SentencePiece 32k BPE model | +| `tokenizer_config.json` | `add_bos_token=false` — prepend `` yourself | +| `generation_config.json` | Low-temperature defaults that keep this model coherent | +| `training_info.json` | Checkpoint step, tokens seen, benchmark scores | + +A previous revision also shipped `pytorch_model.bin` (a stale duplicate of the +weights), duplicate tokenizer files under two names, and a `config.json` whose +`pad_token_id` was 0 while the tokenizer's `` is 3. All removed or +corrected. + +## Citation + +```bibtex +@misc{kw5lite2026, + title = {KW5-Lite: a 110M-parameter Swahili language model trained on a single T4}, + author = {Regnant}, + year = {2026}, + url = {https://huggingface.co/regnant-io/kw5-lite-base} +} +``` + +Apache 2.0. diff --git a/config.json b/config.json new file mode 100644 index 0000000..099046e --- /dev/null +++ b/config.json @@ -0,0 +1,32 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "float32", + "eos_token_id": 2, + "head_dim": 64, + "hidden_act": "silu", + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 2048, + "max_position_embeddings": 2048, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "num_key_value_heads": 12, + "pad_token_id": 3, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + 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