Instructions to use Kwaipilot/KwaiCoder-23B-A4B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kwaipilot/KwaiCoder-23B-A4B-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kwaipilot/KwaiCoder-23B-A4B-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Kwaipilot/KwaiCoder-23B-A4B-v1") model = AutoModelForCausalLM.from_pretrained("Kwaipilot/KwaiCoder-23B-A4B-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Kwaipilot/KwaiCoder-23B-A4B-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kwaipilot/KwaiCoder-23B-A4B-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kwaipilot/KwaiCoder-23B-A4B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kwaipilot/KwaiCoder-23B-A4B-v1
- SGLang
How to use Kwaipilot/KwaiCoder-23B-A4B-v1 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 "Kwaipilot/KwaiCoder-23B-A4B-v1" \ --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": "Kwaipilot/KwaiCoder-23B-A4B-v1", "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 "Kwaipilot/KwaiCoder-23B-A4B-v1" \ --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": "Kwaipilot/KwaiCoder-23B-A4B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kwaipilot/KwaiCoder-23B-A4B-v1 with Docker Model Runner:
docker model run hf.co/Kwaipilot/KwaiCoder-23B-A4B-v1
Kwaipilot KwaiCoder-23B-A4B-v1
1.Model Details
Introduction
KwaiCoder-23BA4-v1 is the latest open-source self-developed code completion model from the Kwaipilot team at Kuaishou. The training of the model relies on an efficient training approach proposed by the Kwaipilot team. By incorporating techniques such as model pruning, knowledge distillation, and fine-grained merging, the training of the 23B-wide MoE architecture code completion model was achieved at 1/30 of the cost compared to traditional methods. It has also set new SOTA benchmarks across multiple code-related evaluation datasets.
Performance
2.Usage
Code Completion
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Kwaipilot/KwaiCoder-23B-A4B-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id,trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16,trust_remote_code=True)
text = "#write a quick sort algorithm"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=80)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(text):])
Code Insertion
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Kwaipilot/KwaiCoder-23B-A4B-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id,trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16,trust_remote_code=True)
text = """<|fim▁begin|>def find_longest_substring(s):
seen = {}
max_length = 0
start = 0
<|fim▁hole|>
if char in seen and seen[char] >= start:
start = seen[char] + 1
seen[char] = end
max_length = max(max_length, end - start + 1)
return max_length<|fim▁end|>"""
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=80)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(text):])
3.License
This code repository is licensed under the MIT License.
4.BibTex
@misc{kwaicoder,
title = {KwaiCoder: Code mathematical abilities comprehensive improvement.},
author = {Kwaipilot team},
year = {2024},
}
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