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Model: afrideva/japanese-mistral-300m-base-GGUF
Source: Original Platform

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+japanese-mistral-300m-base.fp16.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+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

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