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

Overview

DeepSeek-Coder-V2-Lite-Instruct is an open-source, instruction-tuned Mixture-of-Experts (MoE) code language model from DeepSeek AI designed for code generation, infilling, and reasoning across a very large context window. The Lite-Instruct variant is a 16B-total-parameter model that activates ~2.4B parameters per request (sparse/MoE execution), and supports up to 128k tokens of context — enabling tasks such as multi-file completions, large-repo editing, and long-horizon debugging. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)) The model family was further pre-trained from an intermediate DeepSeek-V2 checkpoint with additional large-scale token exposure to boost coding and mathematical reasoning; DeepSeek reports benchmark-level performance that is competitive with leading closed-source systems on code and math tasks. The project provides ready-to-run artifacts on Hugging Face (safetensors/BF16), an OpenAI-compatible API on DeepSeek’s platform, vLLM-ready instructions, and community-maintained quantizations (GGUF/llama.cpp-compatible) for local/edge use. For licensing, DeepSeek-Coder-V2 models are released under a Model License with commercial use permitted; the code repository is MIT-licensed. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct))

Model Statistics

• Downloads: 216,018
• Likes: 548
• Pipeline: text-generation

License: other

Model Details

Architecture and variants: DeepSeek-Coder-V2 is built on the DeepSeekMoE framework (DeepSeekMoE paper) and is released in Lite (16B total / ~2.4B active) and Full (236B total / ~21B active) variants, each with Base and Instruct checkpoints. The MoE design selectively routes token computation to a subset of experts per token, lowering runtime memory/compute while retaining a large parameter capacity. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)) Technical capabilities and training: The model is a decoder-only transformer trained with a mix of next-token prediction and Fill‑In‑The‑Middle (FIM) objectives to support infilling and in-place edits. DeepSeek reports extended training from DeepSeek‑V2 with billions of additional tokens to improve code and math reasoning. The model supports a 128k token context window and claims support for hundreds of programming languages (public documentation lists 338 languages). Recommended inference paths include Hugging Face Transformers for standard usage and vLLM for high-throughput/long-context scenarios; community quantized builds in GGUF and other formats are available for local deployment. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)) Implementation details: released artifacts on Hugging Face use safetensors and BF16 formats; the model card includes example usage for code completion, FIM insertion tokens, and chat-style instruction prompts. The underlying research paper describes DeepSeekMoE strategies to improve expert specialization, including expert segmentation and a shared-expert strategy to reduce redundancy. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct))

Key Features

• Mixture-of-Experts sparsity: large total params, small active parameter set per request (resource-efficient).
• 128k token context: handle multi-file codebases and long-revision histories.
• Fill‑In‑The‑Middle (FIM): trained for middle-of-file infilling and code edits.
• Instruction‑tuned Instruct checkpoint for chat and task-focused prompts.
• Community quantizations (GGUF / llama.cpp) and vLLM examples for local deployment.

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Example: Hugging Face Transformers (BF16) — adapted from the model card
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"

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

# For chat-style instruction prompts, the model card provides an apply_chat_template utility
# and vLLM examples for long-context / high-throughput usage. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct))

Benchmarks

HumanEval / Pass@1 (reported): ≈90.2% (reported) (Source: https://marktechpost.com/2024/06/18/meet-deepseek-coder-v2-by-deepseek-ai-the-first-open-source-ai-model-to-surpass-gpt4-turbo-in-coding-and-math-supporting-338-languages-and-128k-context-length/)

MATH (reported): ≈75.7% (reported) (Source: https://mathsolver.top/post/deepseek-coder-v2-the-first-open-source-model-to-beat-gpt-4-turbo-in-math-and-coding)

GSM8K (reported): ≈94.9% (reported) (Source: https://mathsolver.top/post/deepseek-coder-v2-the-first-open-source-model-to-beat-gpt-4-turbo-in-math-and-coding)

Model downloads (Hugging Face — month): 216,018 downloads (last month, as listed on Hugging Face) (Source: https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)

Context window: 128,000 tokens (documented) (Source: https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct)

Key Information

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