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Model: refarde/Mistral-7B-Instruct-v0.2-Ko-S-Core
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---
base_model: mistralai/Mistral-7B-Instruct-v0.2
license: apache-2.0
pipeline_tag: text-generation
language:
- en
- ko
tags:
- finetuned
- text-generation
datasets:
- royboy0416/ko-alpaca
inference: false
model_type: mixtral
---
# Model Card for Mistral-7B-Instruct-v0.2-Ko-S-Core
## Model Details
* **Base Model**: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
## Dataset Details
### Used Datasets
- royboy0416/ko-alpaca

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---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct 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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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### Framework versions
- PEFT 0.7.1

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from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
from peft import PeftModel
import json
import os
class EndpointHandler():
def __init__(self, path=""):
base_model_path = json.load(open(os.path.join(path, "training_params.json")))["model"]
model = AutoModelForCausalLM.from_pretrained(
base_model_path,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
trust_remote_code=True,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)
model = PeftModel.from_pretrained(model, path)
model = model.merge_and_unload()
self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
if parameters is not None:
prediction = self.pipeline(inputs, **parameters)
else:
prediction = self.pipeline(inputs)
return prediction

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