DeepSeek-Coder-V2 - AI Language Models Tool

Overview

DeepSeek-Coder-V2 is an open-source Mixture-of-Experts (MoE) code-specialized language model designed for large-context, high‑fidelity code generation, completion, and reasoning. The project publishes two main scale families (a 16B "Lite" variant and a 236B total-parameter variant with ~21B active parameters under MoE routing) and emphasizes whole-repo reasoning via an extended 128K token context window. According to the model maintainers, the V2 release was further pre-trained from a DeepSeek-V2 checkpoint with roughly an additional 6 trillion tokens to improve mathematical reasoning and coding capabilities. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base)) Practical use cases include chat-based code assistance, in-place code insertion (hole-filling), automated debugging/fixing, and whole-project analysis across hundreds of programming languages. The project publishes evaluation tables showing V2’s strong results on standard code benchmarks (HumanEval, MBPP+, LiveCodeBench) and math benchmarks (GSM8K, MATH), and the team provides both Hugging Face weights and an OpenAI-compatible API on DeepSeek’s platform for pay-as-you-go access. Community feedback since release shows active experimentation (model downloads and community threads), while maintainers document GPU and precision requirements for running larger variants. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base))

Model Statistics

• Downloads: 37,912
• Likes: 82
• Pipeline: text-generation
• Parameters: 235.7B

License: other

Model Details

Architecture and variants: DeepSeek-Coder-V2 is a Mixture-of-Experts (MoE) causal transformer family built on the DeepSeekMoE framework. Public artifacts list two main families: a 16B-parameter "Lite" (active params ~2.4B) and a 236B-parameter model (active params ~21B) using MoE routing so only a subset of experts activate per token. Context & language coverage: all published variants support a 128K token context window and the project reports support for 338 programming languages. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base)) Training & data: the maintainers state V2 was further pre-trained from an intermediate DeepSeek-V2 checkpoint with roughly 6 trillion additional tokens focused on code and math corpora to improve reasoning and code correctness. Inference & hardware: official docs recommend BF16 weights; running the full 236B bf16 model for inference requires multi‑GPU setups (authors note an 80GB*8 configuration for bf16 inference in their how-to). The project supplies a Lite variant and community quantizations for lower-resource setups. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base)) Licensing & distribution: model files (safetensors/bf16) and code are distributed via Hugging Face and GitHub; the project provides both MIT-licensed code and a Model License for the weights, and states commercial use is permitted under their license terms. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base))

Key Features

• 128K token context window for whole‑repo and long-file reasoning.
• Mixture-of-Experts routing: large total params, smaller active parameter footprint.
• Supports 338 programming languages, from mainstream to niche dialects.
• Instruction-tuned and base variants (Lite: 16B total, Full: 236B total).
• Benchmarked: strong HumanEval, MBPP+, GSM8K, and Aider performance.

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Example: small/lite model for local testing (use Lite variant to fit fewer GPUs)
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Base"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16  # recommended for published weights when available
).cuda()

prompt = "# Write a quicksort implementation in Python\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# Notes: authors recommend vLLM or multi-GPU BF16 for the larger 236B variant; see
# the Hugging Face model page and README for vLLM integration, chat templates, and hardware guidance.

Benchmarks

HumanEval (DeepSeek-Coder-V2-Instruct, 236B): 90.2% (Source: https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base)

MBPP+ (DeepSeek-Coder-V2-Instruct, 236B): 76.2% (Source: https://github.com/deepseek-ai/DeepSeek-Coder-V2)

GSM8K (mathematical reasoning, DeepSeek-Coder-V2-Instruct): 94.9% (Source: https://github.com/deepseek-ai/DeepSeek-Coder-V2)

Aider (code-fixing benchmark, DeepSeek-Coder-V2-Instruct): 73.7% (Source: https://github.com/deepseek-ai/DeepSeek-Coder-V2)

LiveCodeBench (code generation, DeepSeek-Coder-V2-Instruct): 43.4% (Source: https://github.com/deepseek-ai/DeepSeek-Coder-V2)

Key Information

• Category: Language Models
• Type: AI Language Models Tool