|
--- |
|
base_model: ce-lery/japanese-mistral-300m-base |
|
inference: false |
|
model-index: |
|
- name: checkpoints-mistral-300M-FA2 |
|
results: [] |
|
model_creator: ce-lery |
|
model_name: japanese-mistral-300m-base |
|
pipeline_tag: text-generation |
|
quantized_by: afrideva |
|
tags: |
|
- generated_from_trainer |
|
- gguf |
|
- ggml |
|
- quantized |
|
- q2_k |
|
- q3_k_m |
|
- q4_k_m |
|
- q5_k_m |
|
- q6_k |
|
- q8_0 |
|
--- |
|
# ce-lery/japanese-mistral-300m-base-GGUF |
|
|
|
Quantized GGUF model files for [japanese-mistral-300m-base](https://huggingface.co/ce-lery/japanese-mistral-300m-base) from [ce-lery](https://huggingface.co/ce-lery) |
|
|
|
|
|
| Name | Quant method | Size | |
|
| ---- | ---- | ---- | |
|
| [japanese-mistral-300m-base.fp16.gguf](https://huggingface.co/afrideva/japanese-mistral-300m-base-GGUF/resolve/main/japanese-mistral-300m-base.fp16.gguf) | fp16 | 712.33 MB | |
|
| [japanese-mistral-300m-base.q2_k.gguf](https://huggingface.co/afrideva/japanese-mistral-300m-base-GGUF/resolve/main/japanese-mistral-300m-base.q2_k.gguf) | q2_k | 176.84 MB | |
|
| [japanese-mistral-300m-base.q3_k_m.gguf](https://huggingface.co/afrideva/japanese-mistral-300m-base-GGUF/resolve/main/japanese-mistral-300m-base.q3_k_m.gguf) | q3_k_m | 195.04 MB | |
|
| [japanese-mistral-300m-base.q4_k_m.gguf](https://huggingface.co/afrideva/japanese-mistral-300m-base-GGUF/resolve/main/japanese-mistral-300m-base.q4_k_m.gguf) | q4_k_m | 234.80 MB | |
|
| [japanese-mistral-300m-base.q5_k_m.gguf](https://huggingface.co/afrideva/japanese-mistral-300m-base-GGUF/resolve/main/japanese-mistral-300m-base.q5_k_m.gguf) | q5_k_m | 266.47 MB | |
|
| [japanese-mistral-300m-base.q6_k.gguf](https://huggingface.co/afrideva/japanese-mistral-300m-base-GGUF/resolve/main/japanese-mistral-300m-base.q6_k.gguf) | q6_k | 307.38 MB | |
|
| [japanese-mistral-300m-base.q8_0.gguf](https://huggingface.co/afrideva/japanese-mistral-300m-base-GGUF/resolve/main/japanese-mistral-300m-base.q8_0.gguf) | q8_0 | 379.17 MB | |
|
|
|
|
|
|
|
## Original Model Card: |
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# japanese-mistral-300m-base |
|
|
|
## Overview |
|
|
|
Welcome to my model card! |
|
|
|
This Model feature is ... |
|
|
|
- Suppression of unknown word generation by using byte fallback in SentencePiece tokenizer and conversion to huggingface Tokenizers format |
|
- Pretrained by wikipedia dataset and cc100 dataset |
|
- Use of [Mistral 300M](https://huggingface.co/ce-lery/japanese-mistral-300m-base/blob/main/config.json) |
|
|
|
Yukkuri shite ittene! |
|
|
|
## How to use the model |
|
|
|
```python |
|
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
|
import torch |
|
|
|
MODEL_NAME = "ce-lery/japanese-mistral-300m-base" |
|
torch.set_float32_matmul_precision('high') |
|
|
|
DEVICE = "cuda" |
|
if torch.cuda.is_available(): |
|
print("cuda") |
|
DEVICE = "cuda" |
|
else: |
|
print("cpu") |
|
DEVICE = "cpu" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME,use_fast=False) |
|
model = AutoModelForCausalLM.from_pretrained( |
|
MODEL_NAME, |
|
trust_remote_code=True, |
|
).to(DEVICE) |
|
|
|
# streamer = TextStreamer(tokenizer) |
|
|
|
prompt = "大規模言語モデルとは、" |
|
|
|
inputs = tokenizer(prompt, add_special_tokens=False,return_tensors="pt").to(model.device) |
|
with torch.no_grad(): |
|
|
|
outputs = model.generate( |
|
inputs["input_ids"], |
|
max_new_tokens=256, |
|
do_sample=True, |
|
early_stopping=False, |
|
top_p=0.95, |
|
top_k=50, |
|
temperature=0.9, |
|
# streamer=streamer, |
|
no_repeat_ngram_size=2, |
|
num_beams=3 |
|
) |
|
|
|
print(outputs.tolist()[0]) |
|
outputs_txt = tokenizer.decode(outputs[0]) |
|
print(outputs_txt) |
|
|
|
``` |
|
|
|
## Receipe |
|
|
|
If you want to restruct this model, you can refer [this Github repository](https://github.com/ce-lery/japanese-mistral-300m-recipe). |
|
|
|
I wrote the receipe for struction this model. For example, |
|
|
|
- Preprocess with sentencepiece |
|
- Pretraining with flash attention2 and torch.compile and DeepSpeed |
|
- Fine-tuning with databricks-dolly-15k-ja |
|
|
|
If you find my mistake,error,...etc, please create issue. |
|
If you create pulreqest, I'm very happy! |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0006 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 4 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- gradient_accumulation_steps: 64 |
|
- total_train_batch_size: 256 |
|
- optimizer: Adam with betas=(0.9,0.95) and epsilon=0.0001 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 1000 |
|
- num_epochs: 1 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:-----:|:---------------:| |
|
| 4.2911 | 0.12 | 5000 | 4.2914 | |
|
| 3.9709 | 0.24 | 10000 | 3.9900 | |
|
| 3.8229 | 0.36 | 15000 | 3.8388 | |
|
| 3.7197 | 0.47 | 20000 | 3.7454 | |
|
| 3.652 | 0.59 | 25000 | 3.6739 | |
|
| 3.597 | 0.71 | 30000 | 3.6177 | |
|
| 3.5554 | 0.83 | 35000 | 3.5770 | |
|
| 3.536 | 0.95 | 40000 | 3.5582 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.14.1 |