Instructions to use duyntnet/codegeex4-all-9b-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="duyntnet/codegeex4-all-9b-imatrix-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("duyntnet/codegeex4-all-9b-imatrix-GGUF", dtype="auto") - llama-cpp-python
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="duyntnet/codegeex4-all-9b-imatrix-GGUF", filename="codegeex4-all-9b-IQ1_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
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 duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
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 duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
Use Docker
docker model run hf.co/duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "duyntnet/codegeex4-all-9b-imatrix-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "duyntnet/codegeex4-all-9b-imatrix-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
- SGLang
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF 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 "duyntnet/codegeex4-all-9b-imatrix-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "duyntnet/codegeex4-all-9b-imatrix-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "duyntnet/codegeex4-all-9b-imatrix-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "duyntnet/codegeex4-all-9b-imatrix-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF with Ollama:
ollama run hf.co/duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
- Unsloth Studio new
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF 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 duyntnet/codegeex4-all-9b-imatrix-GGUF 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 duyntnet/codegeex4-all-9b-imatrix-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for duyntnet/codegeex4-all-9b-imatrix-GGUF to start chatting
- Docker Model Runner
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF with Docker Model Runner:
docker model run hf.co/duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
- Lemonade
How to use duyntnet/codegeex4-all-9b-imatrix-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull duyntnet/codegeex4-all-9b-imatrix-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.codegeex4-all-9b-imatrix-GGUF-Q4_K_M
List all available models
lemonade list
Quantizations of https://huggingface.co/THUDM/codegeex4-all-9b
Inference Clients/UIs
From original readme
Get Started
Use 4.39.0<=transformers<=4.40.2 to quickly launch codegeex4-all-9b:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained("THUDM/codegeex4-all-9b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
"THUDM/codegeex4-all-9b",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True
).to(device).eval()
inputs = tokenizer.apply_chat_template([{"role": "user", "content": "write a quick sort"}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True ).to(device)
with torch.no_grad():
outputs = model.generate(**inputs, max_length=256)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
If you want to build the chat prompt manually, please make sure it follows the following format:
f"<|system|>\n{system_prompt}\n<|user|>\n{prompt}\n<|assistant|>\n"
Default system_prompt:
你是一位智能编程助手,你叫CodeGeeX。你会为用户回答关于编程、代码、计算机方面的任何问题,并提供格式规范、可以执行、准确安全的代码,并在必要时提供详细的解释。
The English version:
You are an intelligent programming assistant named CodeGeeX. You will answer any questions users have about programming, coding, and computers, and provide code that is formatted correctly.
For infilling ability, please use (without system prompt):
f"<|user|>\n<|code_suffix|>{suffix}<|code_prefix|>{prefix}<|code_middle|><|assistant|>\n"
Additional infos (like file path, programming language, mode) can be added. Example:
<|user|>
###PATH:src/example.py
###LANGUAGE:Python
###MODE:BLOCK
<|code_suffix|>{suffix}<|code_prefix|>{prefix}<|code_middle|><|assistant|>
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