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---
base_model: llm-jp/llm-jp-3-13b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
datasets:
- Aratako/Synthetic-JP-EN-Coding-Dataset-801k
- kanhatakeyama/ramdom-to-fixed-multiturn-Calm3
- kanhatakeyama/AutoMultiTurnByCalm3-22
- ichikara-instruction-003-001-1
- ichikara-instruction-003-003-1
---
# Uploaded model
- **Developed by:** keiju12uh
- **License:** apache-2.0
- **Finetuned from model :** llm-jp/llm-jp-3-13b
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# Used_datasets
- Aratako/Synthetic-JP-EN-Coding-Dataset-801k: 50000 sample
- kanhatakeyama/ramdom-to-fixed-multiturn-Calm3: 10000 sample
- kanhatakeyama/AutoMultiTurnByCalm3-22: 50000 sample
- ichikara-instruction-003-001-1
- ichikara-instruction-003-003-1
# Sample_Use
```
!pip install unsloth
!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install -U torch
!pip install -U peft
!pip install --upgrade torch
!pip install --upgrade xformers
from unsloth import FastLanguageModel
from peft import PeftModel
import torch
import json
from tqdm import tqdm
import re
model_id = "llm-jp/llm-jp-3-13b"
adapter_id = "keiju12uh/llm-jp-3-13b-cp-5-sft-3-lora"
HF_TOKEN = "your-token"
dtype = None
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_id,
dtype=dtype,
load_in_4bit=load_in_4bit,
trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, adapter_id, token = HF_TOKEN)
datasets = []
with open("your-dataset.jsonl", "r") as f:
item = ""
for line in f:
line = line.strip()
item += line
if item.endswith("}"):
datasets.append(json.loads(item))
item = ""
FastLanguageModel.for_inference(model)
results = []
for dt in tqdm(datasets):
input = dt["input"]
prompt = f"""### 指示\n{input}\n### 回答\n"""
inputs = tokenizer([prompt], return_tensors = "pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True, do_sample=False, repetition_penalty=1.2)
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]
results.append({"task_id": dt["task_id"], "input": input, "output": prediction})
json_file_id = re.sub(".*/", "", adapter_id)
with open(f"/your-output-directry/{json_file_id}_output.jsonl", 'w', encoding='utf-8') as f:
for result in results:
json.dump(result, f, ensure_ascii=False)
f.write('\n')
``` |