File size: 2,071 Bytes
60fa1f2 1682f29 60fa1f2 76ce9e3 b6e6fc6 76ce9e3 1682f29 76ce9e3 1682f29 60fa1f2 1682f29 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
base_model: llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- ja
datasets:
- kinokokoro/ichikara-instruction-003
---
# Uploaded model
- **Developed by:** trikudayodayodayo
- **License:** apache-2.0
- **Finetuned from model :** llm-jp/llm-jp-3-13b
# Overview
This repository provides a Japanese Large Language Model finetuned on ichikara datasets
# supervised-fintuning
Thme model was finetuned on a subset from mxture of the following dataset.
Training epoch:1
- ichikara-instruction-003-001-1
- ichikara-instruction-003-001-2
- ichikara-instruction-003-001-2.2
- ichikara-instruction-003-003-5.1
- ichikara-instruction-003-003-5.2
- ichikara-instruction-003-002-1
- ichikara-instruction-003-003-1
Authors
tsuchida rikuto
How to Use
To use this model, run the code below
```python
!pip install -U bitsandbytes
!pip install -U transformers
!pip install -U accelerate
!pip install -U datasets
!pip install ipywidgets --upgrade
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
import torch
from tqdm import tqdm
import json
model_name = "trikudayodayodayo/llm-jp-3-13b-it-1209_lora"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=False,
)
HF_TOKEN="Type your HF_TOKEN"
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
device_map="auto",
token = HF_TOKEN
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token = HF_TOKEN)
input = "Type text here"
tokenized_input = tokenizer.encode(input, add_special_tokens=False, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
tokenized_input,
max_new_tokens=100,
do_sample=False,
repetition_penalty=1.2
)[0]
output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
print(output)
``` |