DeepSeek-R1-Distill-Llama-70B is explicitly documented as a distillation of the DeepSeek-R1 reasoning model into the Llama-3.3-70B-Instruct base architecture. The technical report and GitHub repository detail the multi-stage training pipeline, which includes cold-start supervised fine-tuning (SFT) on curated chain-of-thought (CoT) data followed by reinforcement learning (RL) using Group Relative Policy Optimization (GRPO). While the distillation process is well-described, the specific architectural modifications beyond the base Llama-3.3 framework are only partially detailed in public documentation.

The training data for this specific variant is described as 800,000 samples curated from the larger DeepSeek-R1 model. However, the composition of the original DeepSeek-R1 training data remains largely opaque, with only general mentions of 'diverse internet data' and 'high-quality CoT examples.' There is no public breakdown of data sources (e.g., percentage of web, code, or books) or detailed disclosure of the filtering and cleaning methodologies used for the pre-training corpus of the teacher model.

The model utilizes the Llama-3 tokenizer with a vocabulary size of approximately 128,256 tokens. The tokenizer files are publicly available on Hugging Face, and the configuration is consistent with the claimed Llama-3.3 base. Documentation notes slight modifications to the tokenizer configuration to support specific reasoning tokens (like <think> tags), which are verifiable through the provided model files.

As a dense model, the parameter count is clearly stated as 70.6 billion. Unlike the MoE architecture of the parent DeepSeek-R1 model, all parameters are active during inference. The architectural breakdown is consistent with the Llama-3.3-70B-Instruct foundation, and there is no ambiguity regarding active versus total parameters.

While some compute metrics for the parent DeepSeek-R1 model are mentioned in third-party reports (e.g., 2.8 million GPU hours on H800 clusters), official documentation for this specific 70B distilled variant lacks detailed hardware specifications, total training duration, and carbon footprint calculations. The information available is largely estimated by external analysts rather than officially disclosed in a comprehensive technical model card.

DeepSeek provides benchmark results for AIME 2024, MATH-500, and GPQA, but the evaluation code and exact prompts used for these specific results are not fully public. While the technical report mentions using the 'simple-evals' framework, reproduction instructions for the distilled variants are limited, and there is a lack of third-party verification for the specific 70B distillation performance across all claimed metrics.

The model consistently identifies itself as a DeepSeek-R1 distilled variant and is transparent about its reasoning-focused nature. It utilizes specific output formatting (the <think> block) to signal its internal reasoning process. There are no significant reports of the model claiming to be a competitor's product, although its base identity is tied to the Llama architecture it was distilled into.

The model weights are released under the MIT License, which is highly permissive. However, there is inherent complexity because it is a derivative of Llama-3.3-70B-Instruct, which is governed by the Llama 3.3 Community License. While DeepSeek claims MIT for its weights, users must still navigate the potential overlap with Meta's underlying license terms for the base architecture, leading to moderate ambiguity for commercial users.

Hardware requirements are well-documented by both the provider and the community. VRAM requirements for FP16 (~140GB) and various quantization levels (e.g., 4-bit requiring ~40-48GB) are publicly available. Documentation provides guidance on the trade-offs between quantization and reasoning performance, though official scaling data for context length memory usage is less detailed.

The model lacks a formal semantic versioning system or a detailed public changelog. While updates like the '0528' version have been released, documentation of specific changes between iterations is minimal. There is no clear deprecation path or historical version access provided through official channels, making it difficult for users to track behavioral drift over time.