Instructions to use taide/TAIDE-LX-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use taide/TAIDE-LX-7B-Chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="taide/TAIDE-LX-7B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("taide/TAIDE-LX-7B-Chat")
model = AutoModelForCausalLM.from_pretrained("taide/TAIDE-LX-7B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use taide/TAIDE-LX-7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "taide/TAIDE-LX-7B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taide/TAIDE-LX-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/taide/TAIDE-LX-7B-Chat

SGLang

How to use taide/TAIDE-LX-7B-Chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "taide/TAIDE-LX-7B-Chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taide/TAIDE-LX-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "taide/TAIDE-LX-7B-Chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taide/TAIDE-LX-7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use taide/TAIDE-LX-7B-Chat with Docker Model Runner:
```
docker model run hf.co/taide/TAIDE-LX-7B-Chat
```

簡易練習測試問題?

by NCGWRjason - opened May 9, 2024

Discussion

NCGWRjason

May 9, 2024

您好我有使用您給的簡易練習程式碼

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokens 設定一個new User Access Tokens

my_token = "hf_XXXXXXXXXXXXXXXXXXXXXXXXXXXXX"

load model

model_name = "taide/TAIDE-LX-7B-Chat"

程式碼和taide資料夾是同一個路徑，taide底下的TAIDE-LX-7B-Chat資料夾裡面下載 taide/TAIDE-LX-7B-Chat/tree/main 所有檔案

tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, device_map="auto", token=my_token)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prepare prompt

question = "臺灣最高的建築物是?"
chat = [
{"role": "user", "content": f"{question}"},
]

generate response

x = pipe(chat, max_new_tokens=1024)
print(f"TAIDE: {x}")

------但還是有以下錯誤，好像我還是被檔的樣子，不曉得是什麼原因，我明明有申請token 而且

我都有填寫授權資料上傳了謝謝------------------------------------

Traceback (most recent call last):
File "c:\Users....................\run_t.py", line 8, in
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
File "C:\Users\AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\models\auto\tokenization_auto.py", line 819, in from_pretrained
config = AutoConfig.from_pretrained(
File "C:\Users\AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\models\auto\configuration_auto.py", line 928, in from_pretrained
config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
File "C:\Users\AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\configuration_utils.py", line 631, in get_config_dict
config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
File "C:\Users\AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\configuration_utils.py", line 686, in _get_config_dict
resolved_config_file = cached_file(
File "C:\Users\AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\utils\hub.py", line 416, in cached_file
raise EnvironmentError(
OSError: You are trying to access a gated repo.
Make sure to have access to it at https://huggingface.co/taide/TAIDE-LX-7B-Chat.
401 Client Error. (Request ID: Root=1-663c1de2-0a1477535ad9168138f579be;daa556e3-531f-4d76-8e56-f88f4ea0c19d)

Cannot access gated repo for url https://huggingface.co/taide/TAIDE-LX-7B-Chat/resolve/main/config.json.
Access to model taide/TAIDE-LX-7B-Chat is restricted. You must be authenticated to access it.

nctu6

May 9, 2024

您好，

謝謝您的回饋，範例已更新這個問題，請參考：https://huggingface.co/taide/TAIDE-LX-7B-Chat-4bit/discussions/3

Best regards.

NCGWRjason

May 9, 2024

您好，

謝謝您的回饋，範例已更新這個問題，請參考：https://huggingface.co/taide/TAIDE-LX-7B-Chat-4bit/discussions/3

Best regards.

後來還是不行有這些錯誤

似乎說 load_in_4bit and load_in_8bit 目前都沒有這種參數了

而且還叫我安裝這些套件，但是我明明 pip install之後再次執行還是有這些error

pip install accelerate
pip install -i https://pypi.org/simple/ bitsandbytes

The load_in_4bit and load_in_8bit arguments are deprecated and will be removed in the future versions. Please, pass a BitsAndBytesConfig object in quantization_config argument instead.
Traceback (most recent call last):
File "c:\Users\ \run_t.py", line 32, in
model = AutoModelForCausalLM.from_pretrained(model_name, load_in_4bit=True, device_map="auto", token=my_token)
File "C:\Users\ \AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\models\auto\auto_factory.py", line 563, in from_pretrained
return model_class.from_pretrained(
File "C:\Users\ \AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\modeling_utils.py", line 3165, in from_pretrained
hf_quantizer.validate_environment(
File "C:\Users\ \AppData\Local\Programs\Python\Python310\lib\site-packages\transformers\quantizers\quantizer_bnb_4bit.py", line 62, in validate_environment
raise ImportError(
ImportError: Using bitsandbytes 8-bit quantization requires Accelerate: pip install accelerate and the latest version of bitsandbytes: pip install -i https://pypi.org/simple/ bitsandbytes

nctu6

May 9, 2024

您好，

請參考以下討論，解決環境問題，謝謝您。
https://huggingface.co/taide/Llama3-TAIDE-LX-8B-Chat-Alpha1-4bit/discussions/3

Best regards.

NCGWRjason

May 9, 2024

了解謝謝您是說改成這樣嗎?

不過我目前測試的這台電腦沒讀顯 RAM也只有8G 所以這鐵定無法跑是嘛? 謝謝

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
from torch import bfloat16

https://huggingface.co/docs/hub/security-tokens#user-access-tokens

my_token = "***********************************************************************" # 這行需換成您自己的 access token

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

load model

model_name = "taide/Llama3-TAIDE-LX-8B-Chat-Alpha1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name,
load_in_4bit=True,
device_map="auto",
trust_remote_code=True,
quantization_config=bnb_config,
token=my_token)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prepare prompt

question = "臺灣最高的建築物是?"
chat = [
{"role": "user", "content": f"{question}"},
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False)

generate response

x = pipe(f"{prompt}", max_new_tokens=1024)
print(f"TAIDE: {x}")

nctu6

May 9, 2024

您好，

請參考以下文章，改成使用 GGUF 量化模型：
https://www.reddit.com/r/LocalLLaMA/comments/19f9z64/running_a_local_model_with_8gb_vram_is_it_even/
https://stackoverflow.com/questions/77630013/how-to-run-any-gguf-model-using-transformers-or-any-other-library

GGUF 量化模型，可以自己轉換，或用以下 repo：
https://huggingface.co/ZoneTwelve/TAIDE-LX-7B-Chat-GGUF

Best regards.

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