Model reference · open weights

KwaiCoder

Available as managed deployment LLMs Kwaipilot Text gen · MoE 1 variants 57 dl/mo

KwaiCoder is an open-weight language model from Kwaipilot. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

MakerKwaipilot
TypeLanguage models
TaskText gen · MoE
Parameters (lead)23.3B
Context4k tokens
Runs withtransformers
Released2025-01-22
Popularity57 downloads / month
LicenceOpen weights

About

What KwaiCoder is

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 = """def find_longest_substring(s):
    seen = {}
    max_length = 0
    start = 0
        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"""
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},
}

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys kwaicoder for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (kwaicoder below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"kwaicoder","messages":[{"role":"user","content":"Hello"}]}'

Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms