Instructions to use wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt",
	filename="unsloth.BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16
# Run inference directly in the terminal:
llama cli -hf wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16
# Run inference directly in the terminal:
llama cli -hf wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16
# Run inference directly in the terminal:
./llama-cli -hf wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16

Use Docker

docker model run hf.co/wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16

LM Studio
Jan
Ollama
How to use wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt with Ollama:
```
ollama run hf.co/wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16
```

Unsloth Studio

How to use wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt to start chatting

Atomic Chat new
Docker Model Runner
How to use wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt with Docker Model Runner:
```
docker model run hf.co/wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16
```

Lemonade

How to use wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt:BF16

Run and chat with the model

lemonade run user.Llama-3.1-8B-bnb-4bit-Chtagpt-BF16

List all available models

lemonade list

wlhb commited on Aug 11, 2024

Commit

421dfc6

verified ·

1 Parent(s): 377bb1f

Update README.md

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README.md +3 -4

README.md CHANGED Viewed

@@ -1,5 +1,5 @@
 代码：[colab code](https://colab.research.google.com/drive/1SksjvgRbfpxNQUtYdr2mKxn-OXKHuSov?usp=sharing)
-数据集：[datasets](https://huggingface.co/wlhb/GptConversation)
 导出Chatgpt的历史聊天记录后使用origin2trainDatasets.py清洗为符合微调模型的数据集，并通过[unsloth](https://unsloth.ai/)进行微调训练
@@ -8,8 +8,7 @@
 效果评价待确定
-Code: [colab code](https://colab.research.google.com/drive/1SksjvgRbfpxNQUtYdr2mKxn-OXKHuSov?usp=sharing) Datasets: [datasets](https:// huggingface.co/wlhb/GptConversation)
-After exporting Chatgpt's historical chats use origin2trainDatasets.py to clean them into a dataset that fits the fine-tuning model and train them for fine-tuning via [unsloth](https://unsloth.ai/)
 Base model: unsloth/Meta-Llama-3.1-8B-bnb-4bit Training method: lora
 Effectiveness evaluation to be determined

 代码：[colab code](https://colab.research.google.com/drive/1SksjvgRbfpxNQUtYdr2mKxn-OXKHuSov?usp=sharing)
+数据集：导出chatgpt数据并使用 [脚本程序整理出可训练的规范数据](https://huggingface.co/wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt/blob/main/origin2trainDatasets.py)
 导出Chatgpt的历史聊天记录后使用origin2trainDatasets.py清洗为符合微调模型的数据集，并通过[unsloth](https://unsloth.ai/)进行微调训练
 效果评价待确定
+Code: [colab code](https://colab.research.google.com/drive/1SksjvgRbfpxNQUtYdr2mKxn-OXKHuSov?usp=sharing) Dataset: export chatgpt data and use [script program to organize trainable canonical data](https://huggingface.co/wlhb/Llama-3.1-8B-bnb-4bit-Chtagpt/blob/main/origin2trainDatasets.py)
+Export Chatgpt's history chats and use origin2trainDatasets.py to clean them into datasets that match the fine-tuned model and train them with [unsloth](https://unsloth.ai/) for fine-tuning.
 Base model: unsloth/Meta-Llama-3.1-8B-bnb-4bit Training method: lora
 Effectiveness evaluation to be determined