Welcome to an exciting journey into the world of advanced AI language models! If you’re looking to harness the capabilities of the Llama3.1-70B-Chinese-Chat, a model fine-tuned for Chinese and English users, you’re in the right place. In this guide, we’ll break down the steps to get you started while ensuring your experience is as smooth as possible.

1. Introduction

The Llama3.1-70B-Chinese-Chat model has been meticulously crafted, borrowing strength from the Meta-Llama-3.1-70B-Instruct. This model employs a cutting-edge approach known as ORPO for optimization. It shines particularly in tasks such as role-playing and function calling.

Before diving into usage, let’s align our expectations regarding its behaviors. As a precaution, the model is designed to maintain objectivity and neutral identity; thus, inquiries such as “Who are you?” may yield random responses.

2. Getting Started with Our Model

Let’s break it down step-by-step.

2.1 Usage of Our BF16 Model

1. First things first: Upgrade your transformers package! Ensure it supports Llama3.1 models with the latest version, which is currently 4.43.0.
2. Next, use this Python script to download our BF16 model:

from huggingface_hub import snapshot_download
snapshot_download(repo_id="shenzhi-wang/Llama3.1-70B-Chinese-Chat", ignore_patterns="*.gguf")

3. Now, we can move to inference with the model using the following script:

import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "YourLocalPath/to/Llama3.1-70B-Chinese-Chat"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda",
    torch_dtype=dtype,
)

chat = {
    "role": "user",
    "content": "写一首关于机器学习的诗。"
}
input_ids = tokenizer.apply_chat_template(chat, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=8192,
    do_sample=True,
    temperature=0.6,
    top_p=0.9
)

response = outputs[0][input_ids.shape[1] - 1 :]
print(tokenizer.decode(response, skip_special_tokens=True))

2.2 Usage of Our GGUF Models

1. First, download our GGUF models from the gguf_models folder.
2. Use the GGUF models with LM Studio.
3. For further details on using GGUF models, check the instructions at this link.

3. Troubleshooting

If you encounter any issues while running the model, here are some troubleshooting ideas:

• Ensure that your transformers package is updated to version 4.43.0 or greater.
• Check for any errors in your file paths while loading the model.
• In case of memory-related errors, you may need to adjust your device settings (like switching between CPU and GPU).

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

4. Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

By following these steps, you should be well on your way to leveraging the powerful capabilities of the Llama3.1-70B-Chinese-Chat model. Happy coding!