---
license: mit
pretty_name: Yoru
language:
  - en
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
base_model_relation: finetune
library_name: transformers
pipeline_tag: text-generation
tags:
  - transformers
  - llama
  - sft
  - qlora
  - conversational
  - chat
  - slang
  - gen-z
  - smollm2
  - mori
  - yoru
datasets:
  - grenishrai/genz-sft-dataset
---

![Yoru](yoru-1.7b.png)

# Yoru

**Yoru** is a model in the **Mori** family. It is a full fp16 fine-tune of [SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) for casual internet / Gen Z chat.

This repo is the **merged weights**. Load it with Transformers only. No PEFT, no separate base download. It is a style / persona mix, not a facts model.

## Model Details

- **Name:** Yoru
- **Family:** Mori
- **Repo:** [`grenishrai/yoru`](https://huggingface.co/grenishrai/yoru)
- **Base:** [`HuggingFaceTB/SmolLM2-1.7B-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
- **Type:** Causal LM, QLoRA SFT then merged to fp16
- **Weights:** `model.safetensors` (~3.42 GB, fp16)
- **Language:** English (internet slang)
- **License:** MIT (this checkpoint; the base model has its own license)
- **Dataset:** [`grenishrai/genz-sft-dataset`](https://huggingface.co/datasets/grenishrai/genz-sft-dataset) (3,511 train rows)
- **Kept checkpoint:** epoch 2 (`checkpoint-404`), selected by lowest validation loss, then merged

## Uses

### Direct Use

- Chat replies in a short, informal online voice
- Style-transfer / persona experiments
- Side-by-side checks against the base Instruct model

Pass the same system prompt the data used, or the voice will slip.

### Out-of-Scope Use

- Treating replies as real Gen Z speech or sociolinguistic ground truth
- Formal, clinical, legal, medical, or factual QA
- Impersonating a real person
- Safety-critical or production assistant without extra instruction / safety data

## How to Use

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "grenishrai/yoru"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
model.eval()

system = (
    "You are a pure Gen Z speaker. Always reply in natural Gen Z slang and internet speech. "
    "Use words and phrases like: no cap, fr fr, lowkey, highkey, bet, rizz, mid, slay, periodt, "
    "it's giving, sus, oof, vibes, down horrendous, bussin, cooked, goated, main character, etc. "
    "Keep replies casual, short to medium length, and online. Never break character. "
    "Never explain the slang. Never sound formal or like a normal AI."
)

messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": "I barely slept and now I have to be a person today"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    out = model.generate(
        inputs,
        max_new_tokens=80,
        do_sample=True,
        temperature=0.8,
        top_p=0.9,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
```

`eos` and `pad` are both `<|im_end|>`. That is how SmolLM2-Instruct is set up. ChatML template is in this repo.

Optional 4-bit load at inference:

```python
from transformers import BitsAndBytesConfig

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
    "grenishrai/yoru",
    quantization_config=bnb,
    device_map="auto",
)
```

## Training Details

### Training Data

[`grenishrai/genz-sft-dataset`](https://huggingface.co/datasets/grenishrai/genz-sft-dataset): synthetic 3-turn chats (`system`, `user`, `assistant`). One fixed system prompt. Everyday user turns. Informal assistant replies.

An 8% holdout (`seed=42`) was used for validation. There is no public test split.

### Training Procedure

QLoRA SFT with TRL `SFTTrainer` on a Kaggle T4. Completion-only NLL. AMP off (`fp16=False`, `bf16=False`) because T4 + SmolLM2 bf16 LoRA hits a GradScaler crash. After training, the epoch-2 adapter was merged into the base weights and saved as this fp16 checkpoint.

| Setting | Value |
| --- | --- |
| Base | `HuggingFaceTB/SmolLM2-1.7B-Instruct` |
| Train quantization | 4-bit NF4, double quant, fp16 compute |
| Released weights | merged fp16 |
| LoRA rank / alpha / dropout | 32 / 64 / 0.05 |
| LoRA targets | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
| Epochs run | 3 |
| Checkpoint kept | epoch 2 (best `eval_loss`) |
| Effective batch | 16 (4 × 4 grad accum) |
| Learning rate | 2e-4, cosine, ~5% warmup |
| Weight decay / grad clip | 0.01 / 0.3 |
| Max length | 512 |
| Optimizer | `paged_adamw_8bit` |
| Loss | completion-only NLL |
| Seed | 42 |

Earlier Colab runs on the same data (4 and 9 epochs, same LoRA) also peaked at **epoch 2**. Extra epochs dropped train loss and raised val loss. Do not train this mix much past 2–3 epochs.

## Evaluation

Judge this model by **generations**, not token accuracy. Open-ended slang chat will sit near **56% mean token accuracy** even when the voice has moved.

Kaggle 3-epoch run (the checkpoint merged into this repo):

| Epoch | Train loss | Val loss | Mean token acc |
| ---: | ---: | ---: | ---: |
| 1 | 2.30 | 2.21 | 53% |
| **2** | 1.89 | **2.05** | **56%** |
| 3 | 1.41 | 2.12 | 56% |

Best checkpoint: **epoch 2**. Epoch 3 was worse on the holdout.

Informal demo prompts after that run (not a benchmark):

| Prompt | Yoru (approx.) | Note |
| --- | --- | --- |
| I barely slept and now I have to be a person today | short slang, still a pep line | OK |
| Should I text first or wait | said wait | Wrong take vs the data (send one normal text) |
| My boss emailed me after hours | don’t answer, then some garble | Right instinct |
| Rate this meal: leftover rice and hot sauce | `W. staple not a flop` | Best of the four |

Vs the base Instruct model the voice is a clear step: less fake hype and emoji salad. It is **not** a locked persona. The dataset mixes lowercase chat (~65%) and ALL-CAPS mishap captions (~35%), so generations can mix those styles.

## Bias, Risks, and Limitations

- Slang here is a stylized internet register, not a sample of any age group or region.
- Slang dates quickly. Some lines will read as forced.
- Replies can be blunt or dismissive. That is the target style, not a general assistant policy.
- The model can mash slang without a clear take, or miss the intended advice (see “text first”).
- 1.7B + 3.5k synthetic rows will not match a larger chat model on reasoning or facts.
- No safety alignment beyond whatever the base Instruct model already has.

### Recommendations

- Always send the system prompt above.
- Prefer val loss + human reads over token accuracy.
- Mix with general instruction / safety data if you ship this.
- For a single voice, clean the ALL-CAPS caption rows in the dataset and train again. More epochs will not fix that.

## Environmental Impact

- **Hardware:** Kaggle Tesla T4
- **Hours used:** about 45–70 minutes for the 3-epoch run
- **Cloud provider:** Kaggle

## Technical Specs

- **Architecture:** SmolLM2 1.7B Instruct (Llama-style), 24 layers, hidden 2048
- **Objective:** next-token SFT on assistant tokens only
- **Files in this repo:** `model.safetensors`, `config.json`, `generation_config.json`, tokenizer, ChatML template
- **Not in this repo:** LoRA adapter files (removed after merge)

## Citation

```bibtex
@misc{yoru-2026,
  title  = {Yoru (Mori): SmolLM2-1.7B-Instruct fine-tune for casual internet chat},
  author = {grenishrai},
  year   = {2026},
  url    = {https://huggingface.co/grenishrai/yoru}
}
```

## Model Card Contact

Open an issue on the model repository.