DeepSeek-V3 - AI Language Models Tool

Overview

DeepSeek-V3 is an open‑weight flagship large language model (base and chat) from DeepSeek-AI that emphasizes high-efficiency training, strong reasoning, and first‑class tool use. The model is a Mixture‑of‑Experts (MoE) design that activates a subset of experts per token to reduce inference cost while retaining large capacity; the project reports a 671B main model (685B including the Multi‑Token Prediction module) and a 128K token context window. DeepSeek‑V3 was pre‑trained on a very large corpus (reported 14.8 trillion tokens) and follows pretraining with supervised fine‑tuning and RLHF stages to improve instruction following and reasoning. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3)) DeepSeek‑V3 targets both research and production use: it ships with FP8‑formatted weights for low‑cost training/inference, runs on community inference stacks such as SGLang, LMDeploy and vLLM, and includes tooling for local deployment on NVIDIA, AMD, and Huawei Ascend hardware. The model card and community releases include conversion/runner recipes and a permissive license enabling commercial use. Note: the project and its hosted services have drawn regulatory attention in some jurisdictions; check recent coverage before production deployment. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3))

Model Statistics

• Downloads: 776,800
• Likes: 4012
• Pipeline: text-generation
• Parameters: 684.5B

Model Details

Architecture and core capabilities - Mixture‑of‑Experts (MoE) backbone with ~671B main‑model parameters and ~37B activated parameters per token (reported); total stored weights reported as ~685B when including the Multi‑Token Prediction (MTP) module. The model uses a Multi‑head Latent Attention (MLA) design and a DeepSeekMoE routing strategy to reduce per‑token compute while preserving model capacity. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3)) Training and precision - Pretrained on ~14.8 trillion tokens with an FP8 training pipeline (UE8M0/UE formats reported) and a multi‑stage training schedule (pretrain → SFT → RL). DeepSeek reports FP8 mixed‑precision training to lower cost and supports FP8/BF16 inference workflows. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3)) Inference, tool‑use and integrations - Official model card and community guides provide recipes to run in SGLang, LMDeploy, vLLM, TensorRT‑LLM, and DeepSeek’s own infer demos; recommended production stack varies by hardware and precision (FP8 for lowest cost, BF16 for broader compatibility). Tool‑use features (tool calls / JSON outputs / structured responses) are supported in chat endpoints and the model offers a “reasoner” / “thinking” mode for visible chain‑of‑thoughts. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3)) Limitations and community notes - The maintainers call out incomplete MLA optimizations in some community implementations, evolving MTP support, and active community work on static cache and packed weights. Users report mixed conversational behavior in some checkpoints; evaluate before deploy. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3))

Key Features

• Mixture‑of‑Experts architecture with 37B activated params per token
• Large 128K token context window for long‑document understanding
• FP8 mixed‑precision training and FP8/BF16 inference workflows
• Multi‑Token Prediction (MTP) module for speculative/multi‑token objectives
• First‑class integration with SGLang, LMDeploy, vLLM, and TensorRT‑LLM
• Permissive model license and commercial use allowed (see model card)
• Tool calls, JSON structured outputs, and a 'reasoner' mode for CoT traces

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# NOTE: running the full DeepSeek-V3 weights requires substantial resources
# and community runtimes (SGLang/LMDeploy/vLLM). This example demonstrates
# how the HF transformer's API is used in examples on the model card.

model_id = "deepseek-ai/DeepSeek-V3"

tokenizer = AutoTokenizer.from_pretrained(model_id)
# choose an appropriate dtype and device mapping in your environment
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)

prompt = "Write a concise, step-by-step plan to implement a REST API in Python."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# generate (adjust max_new_tokens for your use case)
outputs = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# For production or lower-cost inference, follow the model card's recommendations
# and deploy via SGLang, LMDeploy, vLLM, or DeepSeek's inference recipes. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-V3))

Pricing

DeepSeek publishes usage‑based API pricing billed per million tokens. Example snapshot (official docs): input tokens (cache hit) ≈ $0.028 / 1M, input tokens (cache miss) ≈ $0.28 / 1M, output tokens ≈ $0.42 / 1M. DeepSeek also runs off‑peak discounts and tiering; check DeepSeek's official API pricing page for current rates and regional billing. ([api-docs.deepseek.com](https://api-docs.deepseek.com/quick_start/pricing/?utm_source=openai))

Benchmarks

Total parameters (main model): 671B (main) — 685B including MTP module (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Activated parameters per token: 37B (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Pretraining tokens: 14.8 trillion tokens (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Context window (max): 128K tokens (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Hugging Face engagement: Downloads last month: 776,800; Likes: ~4.01k (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Example benchmark highlights: BBH 3‑shot: 87.5; MMLU (5‑shot): 87.1; Math GSM8K (8‑shot): 89.3 (as reported on model card) (Source: https://huggingface.co/deepseek-ai/DeepSeek-V3)

Key Information

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