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)
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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
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