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DeepSeek-Coder-V2-Lite-Instruct - AI Language Models Tool

Overview

DeepSeek-Coder-V2-Lite-Instruct is an open-source, instruction-tuned code-specialized language model in the DeepSeek-Coder-V2 family. The Lite-Instruct variant is a Mixture-of-Experts (MoE) design that exposes a 16B-parameter model with a much smaller set of active parameters (reported as ~2.4B active), giving a balance of large-model capability and inference efficiency. It is tuned for code completion, synthesis, and reasoning across a very large set of programming languages and long-context codebases, and is distributed through Hugging Face with weights in BF16 format for local inference. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct?utm_source=openai)) Practically, DeepSeek-Coder-V2-Lite-Instruct targets developers and teams that need high-quality code assistants without relying on closed-source APIs. The model supports extremely long contexts (128k tokens), instruction-style prompting for assistant-like behavior, and is packaged with examples and guidance for running locally or calling DeepSeek’s hosted APIs. Community activity (downloads, quantized builds, and various Hugging Face Spaces) shows active uptake and third-party tooling for quantization/gguf deployment. For licensing and commercial use, the Hugging Face repo notes permissive terms while the authors also provide a paid, OpenAI-compatible API and hosted services. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct?utm_source=openai))

Model Statistics

  • Downloads: 170,280
  • Likes: 523
  • Pipeline: text-generation
  • Parameters: 15.7B

License: other

Model Details

Architecture and sizing: DeepSeek-Coder-V2-Lite-Instruct is built with a Mixture-of-Experts (MoE) approach: the model’s total parameter count is reported at ~16B, while the number of activated experts (active parameters) during inference is substantially lower (reported ~2.4B), which reduces per-inference compute compared with a dense 16B model. Context window: instruction and code runs leverage a 128k-token context length to handle long source files, multi-file projects, and extended prompt + solution chains. Quantization & formats: model artifacts are published in BF16/safetensors and community members provide GGUF/quantized builds for smaller-device inference. ([huggingface.co](https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF?utm_source=openai)) Capabilities and tuning: the Lite-Instruct variant is instruction-tuned for code generation and reasoning (code completion, unit-test generation, docstring creation, bug fixing assistance, and multi-step algorithmic tasks). DeepSeek’s public materials and third-party writeups report strong performance on code benchmarks (HumanEval / MBPP-style suites) and expanded programming language coverage (claims of hundreds of supported languages after V2 pretraining). Deployment notes: the Hugging Face README includes examples for running with transformers (trust_remote_code=True) and warns that high-memory GPUs are required if running full BF16 weights locally (the project provides pointers to quantized community builds to run on more modest setups). ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct?utm_source=openai))

Key Features

  • Mixture-of-Experts design — 16B total, ≈2.4B active parameters during inference.
  • Extremely long context support — 128k-token window for large codebases and multi-file prompts.
  • Instruction-tuned for code tasks: completion, bug fixes, docstrings, and unit-test generation.
  • Published in BF16/safetensors with community GGUF/quantized builds for smaller hardware.
  • Distributed under permissive terms on Hugging Face; authors note commercial use is supported.
  • Open ecosystem: numerous community Spaces, quantization forks, and deployment guides exist.
  • Compatible with DeepSeek hosted API (OpenAI-compatible endpoints) for hosted inference.

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Example: load the Lite-Instruct model for code completion (requires enough GPU memory or use a quantized build)
model_id = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()

prompt = """
# Python: implement a function that merges two sorted lists into one sorted list
def merge_sorted(a, b):
"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# generate continuation (adjust max_new_tokens as needed)
outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Benchmarks

Total parameters (reported): 16B (Source: https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)

Active parameters (reported): ≈2.4B (Mixture-of-Experts active set) (Source: https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF)

Context length: 128k tokens (Source: https://huggingface.co/CISCai/DeepSeek-Coder-V2-Lite-Instruct-SOTA-GGUF)

HumanEval (Lite-Instruct, reported): 81.1% (Source: https://deepwiki.com/deepseek-ai/DeepSeek-Coder-V2/5.1-code-generation-performance)

MBPP+ (Lite-Instruct, reported): 68.8% (Source: https://deepseekdeutsch.io/en/deepseek-coder-v2/)

Downloads (recent / Hugging Face): Downloads last month: 158,732 (as shown on Hugging Face page) (Source: https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)

Last Refreshed: 2026-01-16

Key Information

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