---
license: apache-2.0
language:
- zh
- en
- fr
---

### Direct Use

Run the model

import torch

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TextStreamer

model_id = "xinping/Mixtral-instruction-zh_V0.1-nf4"

model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto')

tokenizer = AutoTokenizer.from_pretrained(model_id)

streamer = TextStreamer(tokenizer,skip_prompt=True, skip_special_tokens=True)

text = "今天是星期五，后天是星期几？"

print(text)

model_input = tokenizer(text, return_tensors="pt").to("cuda")

result = model.generate(**model_input,streamer=streamer, max_new_tokens=2048, repetition_penalty=1.15)


[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

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### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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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 -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary