
This is a Chinese instruction-tuning lora checkpoint based on llama-7B from [this repo's work](https://github.com/Facico/Chinese-Vicuna)

We use the 50k Chinese data, which is the combination of [alpaca_chinese_instruction_dataset](https://github.com/hikariming/alpaca_chinese_dataset.git) and the Chinese conversation data from sharegpt-90k data.
We finetune the model for 3 epochs use a single 4090 with ctxlen=2048.

You can use it like this:
```python
from transformers import LlamaForCausalLM
from peft import PeftModel

model = LlamaForCausalLM.from_pretrained(
    "decapoda-research/llama-7b-hf",
    load_in_8bit=True,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(
    model,
    "Chinese-Vicuna/Chinese-Vicuna-lora-7b-chatv1"
    torch_dtype=torch.float16,
    device_map={'': 0}
)
```

We offer train-args and train-log in [here](https://huggingface.co/Chinese-Vicuna/Chinese-Vicuna-lora-7b-chatv1/tree/main/train_4800_args)
{"language": ["zh", "en"], "license": "apache-2.0", "datasets": ["philschmid/sharegpt-raw"]}
zh en dataset:philschmid/sharegpt-raw license:apache-2.0 region:us
TAGS
#zh #en #dataset-philschmid/sharegpt-raw #license-apache-2.0 #region-us 


This is a Chinese instruction-tuning lora checkpoint based on llama-7B from this repo's work

We use the 50k Chinese data, which is the combination of alpaca_chinese_instruction_dataset and the Chinese conversation data from sharegpt-90k data.
We finetune the model for 3 epochs use a single 4090 with ctxlen=2048.

You can use it like this:


We offer train-args and train-log in here
TAGS
#zh #en #dataset-philschmid/sharegpt-raw #license-apache-2.0 #region-us 
