DeepSeek-V2 - AI Language Models Tool

Overview

DeepSeek-V2 is a Mixture-of-Experts (MoE) language model released by DeepSeek-AI that emphasizes economical training and efficient inference for large-context generation and chat. The model family totals ~236 billion parameters while activating about 21 billion parameters per token, letting it match or exceed many dense models' performance with far lower compute and memory costs. DeepSeek-V2 supports very long contexts (128k tokens) and was pretrained and then instruction-tuned with supervised fine-tuning (SFT) and reinforcement learning (RL) to produce both a base and chat-capable variant. (See the model card and paper for details.) https://huggingface.co/deepseek-ai/DeepSeek-V2, https://huggingface.co/papers/2405.04434 DeepSeek-V2 introduces two architectural innovations—Multi-head Latent Attention (MLA) to compress the key-value (KV) cache for efficient long-context inference, and DeepSeekMoE, a sparse MoE feed-forward design for economical training. According to the authors, these changes reduce KV-cache storage by over 90%, improve maximum generation throughput by multiple factors, and cut training cost versus their prior 67B dense design. The model is published on Hugging Face with code and weights for local use (with heavier hardware needs) and also accessible through DeepSeek’s hosted chat/API offerings. https://github.com/deepseek-ai/DeepSeek-V2, https://huggingface.co/deepseek-ai/DeepSeek-V2

Model Statistics

• Downloads: 3,661
• Likes: 330
• Pipeline: text-generation
• Parameters: 235.7B

License: other

Model Details

Architecture and sparsity: DeepSeek-V2 is a Mixture-of-Experts transformer-style language model that totals approximately 236B parameters while activating ~21B parameters per token via MoE routing. The MoE (DeepSeekMoE) layer design is intended to give dense-like quality while keeping compute and memory use lower during training and inference. Source: model paper and model card. https://huggingface.co/papers/2405.04434, https://huggingface.co/deepseek-ai/DeepSeek-V2 Attention & long context: DeepSeek-V2 uses Multi-head Latent Attention (MLA). MLA compresses key/value information into a latent representation so that the KV cache footprint during generation is dramatically reduced, enabling a 128k-token context window for tasks that require very long documents. This approach trades off a compact latent KV representation for faster, less memory-intensive decoding. https://huggingface.co/papers/2405.04434 Training & fine-tuning: The team reports large-scale pretraining on a multi-source corpus (paper: 8.1 trillion tokens; some docs list a larger aggregate figure—see notes below) followed by supervised fine-tuning and RLHF/SFT stages to produce chat-capable checkpoints. The model authors claim training-cost reductions (≈42.5%) compared with their earlier dense 67B model. https://huggingface.co/papers/2405.04434, https://github.com/deepseek-ai/DeepSeek-V2 Tokenizer & formats: Third-party documentation and DeepSeek’s pages report a byte-level BPE tokenizer with a very large vocabulary (reported ~100k) to support multilingual and code-heavy inputs. Running at full performance on public tooling may require vLLM or vendor-optimized runtimes. https://chat-deep.ai/models/deepseek-v2/, https://huggingface.co/deepseek-ai/DeepSeek-V2 Hardware & inference notes: The model repo notes that efficient local BF16 inference typically requires many high-memory GPUs (the repo references 80GB×8-class setups for BF16 runs) and that Hugging Face’s generic runtime will be slower than DeepSeek’s internal/vLLM-optimized inference. https://huggingface.co/deepseek-ai/DeepSeek-V2, https://github.com/deepseek-ai/DeepSeek-V2 Notes on reported numbers: the paper and official model card/published docs are the authoritative sources, but there are minor inconsistencies between different docs (for example pretraining corpus size reported as 8.1T tokens in the paper and as higher totals in some transformed docs). See the paper and model card for the canonical figures. https://huggingface.co/papers/2405.04434, https://huggingface.co/deepseek-ai/DeepSeek-V2

Key Features

• Mixture-of-Experts (MoE) routing: ~236B total with ≈21B activated per token.
• Multi-head Latent Attention (MLA): compresses KV cache to support 128k context lengths.
• High efficiency: reported ≈42.5% lower training cost vs DeepSeek 67B.
• Large-context generation: practical 128k-token window for long documents and code bases.
• Chat-ready variants: SFT and RL-finetuned chat checkpoints with competitive HumanEval scores.

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch

# Note: DeepSeek-V2 uses custom layers/routing. When using the HF checkpoint, set trust_remote_code=True.
# For large context or best performance, prefer vendor runtimes (vLLM) or multi-GPU BF16 setups.

model_id = "deepseek-ai/DeepSeek-V2"

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

gen = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)

prompt = "Write a short summary describing the benefits of Mixture-of-Experts models in two paragraphs."
outputs = gen(prompt, max_new_tokens=200, do_sample=False)
print(outputs[0]["generated_text"])

Benchmarks

Total parameters: ≈236B (Source: https://huggingface.co/deepseek-ai/DeepSeek-V2)

Activated parameters per token (sparse activation): ≈21B (Source: https://huggingface.co/papers/2405.04434)

Context window (reported): 128k tokens (Source: https://huggingface.co/papers/2405.04434)

Pretraining corpus (reported): 8.1 trillion tokens (paper); some docs list higher aggregate totals (Source: https://huggingface.co/papers/2405.04434)

Training cost reduction vs DeepSeek-67B: ≈42.5% lower training cost (reported) (Source: https://huggingface.co/papers/2405.04434)

KV-cache reduction (MLA): ≈93.3% reduction in KV cache size (reported) (Source: https://huggingface.co/papers/2405.04434)

Throughput improvement (max generation): ≈5.76× improvement (reported) (Source: https://huggingface.co/papers/2405.04434)

MMLU (base) — selected benchmark: 78.5 (reported, base model) (Source: https://huggingface.co/deepseek-ai/DeepSeek-V2)

C-Eval (Chinese) — selected benchmark: 81.7 (reported, base model) (Source: https://huggingface.co/deepseek-ai/DeepSeek-V2)

HumanEval (chat RL variant) — selected benchmark: 81.1 (reported, chat RL variant) (Source: https://github.com/deepseek-ai/DeepSeek-V2)

Key Information

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