llama-2-7b-chat-ko

⛱This repo is under construction

llama-2-7b-chat-ko🇰🇷

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Korean Pretraning Model. Need to Instruction Tuning

Model Details

Model Description

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• Language(s) (NLP): [More Information Needed]
• License: [cc-by-nc-sa-4.0]
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Uses

You need to install
$ pip install protobuf

This model was trained with Qlora

# Case: Load model directly
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, BitsAndBytesConfig, AutoConfig
from peft import  PeftModel

generation_config = dict(
    temperature=0.3,
    top_k=40,
    top_p=0.9,
    do_sample=True,
    num_beams=1,
    repetition_penalty=1.1,
    max_new_tokens=400
    )

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

config = AutoConfig.from_pretrained('meta-llama/Llama-2-7b-chat-hf')


model = LlamaForCausalLM.from_pretrained(
    'meta-llama/Llama-2-7b-chat-hf',
    low_cpu_mem_usage=True,
    quantization_config=bnb_config,
)
tokenizer = LlamaTokenizer.from_pretrained('Chang-Su/llama-2-7b-chat-ko')
model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(model, 'Chang-Su/llama-2-7b-chat-ko')


model.eval()
input_text = '안녕 네 이름은'
with torch.no_grad():
    print("Start inference.")
    results = []
    inputs = tokenizer(input_text,return_tensors="pt")  #add_special_tokens=False ?
    generation_output = model.generate(
        input_ids = inputs["input_ids"].to('cuda:2'),
        attention_mask = inputs['attention_mask'].to('cuda:2'),
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
        **generation_config
    )
    s = generation_output[0]
    output = tokenizer.decode(s,skip_special_tokens=True)

    response = output.split("### Response:")[0].strip()
    print(f"====================")
    print(f"Input: '{input_text}'\n")
    print(f"Output: {response}\n")

    results.append({"Input":input_text,"Output":response})

# Case: Load model directly
not published yet

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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