DeepSeek R1 - AI Language Models Tool

Overview

DeepSeek‑R1 is an open research reasoning model family (R1 and R1‑Zero) from DeepSeek that emphasizes step‑by‑step chain‑of‑thought reasoning, long context lengths, and production‑grade distilled variants suitable for local and cloud deployment. The project demonstrates an RL‑first training pipeline (RL without an initial supervised fine‑tuning stage) that produced emergent CoT behaviors in the R1‑Zero prototype, and then improved stability by adding a cold‑start SFT stage to produce DeepSeek‑R1. The developers also released distilled dense checkpoints (1.5B–70B) derived from R1 outputs so smaller teams can run reasoning workflows on single GPUs. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-R1)) Adoption and community attention have been rapid: the official Hugging Face model card documents extensive benchmark results showing strong math and code performance for both the MoE R1 models and the distilled variants, and DeepSeek provides open weights and MIT licensing for commercial use and downstream distillation. At the same time, the DeepSeek family and company have been the subject of broader news coverage about rapid growth and regulatory scrutiny in some countries — factors to consider when evaluating hosted usage versus self‑hosting. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-R1))

Model Statistics

• Downloads: 412,165
• Likes: 12939
• Pipeline: text-generation
• Parameters: 684.5B

License: mit

Model Details

Architecture and scale: DeepSeek‑R1 and R1‑Zero are built on a Mixture‑of‑Experts (MoE) design with a very large total parameter count (model card lists ~671B total parameters) while activating a small fraction per token (reported ~37B activated parameters at inference). Context handling is extensive — the model card and docs list up to 128K token context and generation settings tuned for long chain‑of‑thought outputs. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-R1)) Training and pipeline: The project emphasizes a two‑stage approach: (1) an RL‑first exploration that discovered long, self‑verifying chain‑of‑thought behaviors (R1‑Zero), and (2) the addition of a cold‑start / SFT stage to improve readability, reduce repetition, and stabilize outputs (DeepSeek‑R1). The team then used samples from R1 to distill dense, smaller models (1.5B, 7B, 8B, 14B, 32B, 70B) based on public bases like Qwen and Llama to make the reasoning capabilities broadly usable. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-R1)) Distillations and compatibility: Distilled checkpoints are distributed on Hugging Face and are compatible with common runtimes (vLLM, SGLang, and many toolchains used for Qwen/Llama‑based models). Model artifacts are published under an MIT license, enabling commercial use, modification, and further distillation. The official model card includes recommended generation settings (temperature ~0.6, suggested instructions for prompting step‑by‑step reasoning) and practical notes for running and evaluating the models. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-R1)) Community & meta: The DeepSeek R1 family generated strong community interest and derivative work on Hugging Face; the model card and public reporting highlight rapid adoption and debate around performance, cost claims, and governance. Review community threads and the model card's Usage Recommendations before deploying to production. ([huggingface.co](https://huggingface.co/deepseek-ai/DeepSeek-R1))

Key Features

• MoE core model (~671B parameters) that activates ~37B parameters per token for efficient inference.
• 128K token context length for long chain‑of‑thought and multi‑step reasoning tasks.
• RL‑first training pipeline (R1‑Zero) followed by a cold‑start SFT stage to improve readability.
• Open‑source release with MIT license and public weights for commercial use and distillation.
• Distilled dense variants (1.5B–70B) that reproduce R1 reasoning and run on single GPUs.

Example Usage

Example (python):

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

# Example: load a distilled DeepSeek-R1 checkpoint (dense distill) from Hugging Face
model_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"

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

gen = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")

prompt = "Solve: Integrate x^2 sin(x) step by step and show final answer in boxed format."
result = gen(prompt, max_new_tokens=512, do_sample=False)
print(result[0]["generated_text"])

Benchmarks

MATH‑500 (Pass@1) — DeepSeek R1: 97.3% (Source: https://huggingface.co/deepseek-ai/DeepSeek-R1)

AIME 2024 (Pass@1) — DeepSeek R1: 79.8% (Source: https://huggingface.co/deepseek-ai/DeepSeek-R1)

LiveCodeBench (Pass@1‑COT) — DeepSeek R1: 65.9% (Source: https://huggingface.co/deepseek-ai/DeepSeek-R1)

Codeforces (Rating) — DeepSeek R1: 2029 (equivalent rating reported) (Source: https://huggingface.co/deepseek-ai/DeepSeek-R1)

DeepSeek‑R1‑Distill‑Qwen‑32B: MATH‑500 (Pass@1): 94.3% (Source: https://huggingface.co/deepseek-ai/DeepSeek-R1)

Key Information

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