---
language:
- ja
library_name: transformers
tags:
- myllm
- causal-lm
- custom-code
- safetensors
pipeline_tag: text-generation
---

# lambda-160m

lambda-160m is an experimental Japanese causal language model created with a custom `myllm` decoder-only Transformer implementation.

All training code is publicly available at [KeisukeMiyamoto1324/myllm](https://github.com/KeisukeMiyamoto1324/myllm).

## Model Details

| Item | Value |
|---|---:|
| Parameters | 164.5M |
| Architecture | Decoder-only Transformer |
| Model type | `myllm` |
| Context length | 1024 tokens |
| Tokenizer | Byte-level BPE |
| Vocabulary size | 65,536 |
| Layers | 16 |
| Hidden size | 768 |
| Attention heads | 12 |
| FFN size | 3,072 |

## Training Data

The model was pretrained on a Japanese text mixture.

| Dataset | Notes |
|---|---|
| `hotchpotch/fineweb-2-edu-japanese` | Japanese web text, Wikipedia domains excluded |
| `MK0727/CleanedWiki-jp` | Japanese Wikipedia-style text, ramped from 50% training progress |

## Training Setup

This model was trained on a single RTX PRO 6000.

| Item | Value |
|---|---:|
| Optimizer | AdamW |
| Learning rate | 2e-4 |
| LR schedule | Warmup cosine |
| Warmup steps | 2,000 |
| Minimum LR ratio | 0.1 |
| Batch size | 96 |
| Max steps | 40,960 |

## Usage

This repository uses custom Transformers code, so `trust_remote_code=True` is required.

```python
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer

repo_id = "MK0727/lambda-160m"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)

inputs = tokenizer("日本の首都は、", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations

This model is not instruction-tuned or safety-aligned. It may generate incorrect, biased, unsafe, or low-quality text.

The model was trained on a limited Japanese corpus mixture and has not been evaluated on standard benchmarks.