The NVIDIA Kimi-K2.6 Eagle model is the Eagle head of Moonshot AI's Kimi-K2.6 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Kimi-K2.6 Eagle3 model incorporates Eagle speculative decoding with Model Optimizer.
With draft length 3, this Eagle head achieves an average acceptance length of 2.62 tokens per step on MT-Bench and 2.67 on the SPEED-Bench qualitative subset.
This model is ready for commercial/non-commercial use.
Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks where latency-optimized inference via speculative decoding is desirable.
Architecture Type: Transformers Network Architecture: DeepSeek V3 Number of model parameters 1.8*10^9
Input:
Input Type(s): Text, Image, Video Input Format(s): String, Binary(Base64 encoded), Binary(Base64 encoded) Input Parameters: One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D) Other Properties Related to Input: Context length: 256k
Output:
Output Type(s): Text Output Format: String Output Parameters: One Dimensional(1D): Sequences Other Properties Related to Output: Outputs may include natural-language responses, code, structured JSON, tool-call requests, agent coordination instructions, and generated artifacts depending on serving configuration and application-level tooling.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Model Version(s):
The model is version 1 of Eagle3 and is trained with nvidia-modelopt v0.44.0
Training and Evaluation Datasets:
** Total size (in number of data points) 112K.
** Dataset partition: Training 100%
Training Dataset:
Link:Nemotron-Post-Training-Dataset-v2, only prompts from the datasets were used for data synthesis, (the original responses from GPT were not used), which is then used to train the Eagle modules.
Data Modality: Text, Image, Video Image Training Data Size: None
Text Training Data Size: Less than a Billion Tokens
Video Training Data Size: None
Data Collection Method by dataset: Hybrid: Automated, Synthetic Labeling Method by dataset: Hybrid: Automated, Synthetic Properties: 112K multilingual text samples featuring prompts spanning math, code, STEM, and conversational topics. Each sample includes a synthetic response generated by the target model.
Evaluation Dataset:
Link: MTBench, for more details, see hereData Collection Method by dataset: Hybrid: Human, Synthetic Labeling Method by dataset: Hybrid: Human, Synthetic
Properties: 3,300 multi-turn dialogue sequences, each annotated with expert preference votes.
Link: SPEED-Bench (qualitative subset)
Data Collection Method by dataset: Hybrid: Human, Synthetic Labeling Method by dataset: Hybrid: Human, Synthetic
Properties: 880 single- and multi-turn evaluation prompts spanning 11 categories (coding, humanities, math, multilingual, qa, rag, reasoning, roleplay, stem, summarization, writing).
Inference:
Acceleration Engine: TensorRT-LLM Test Hardware: NVIDIA B200
Eagle Speculative Decoding
Synthesized data was obtained from Moonshot AI's Kimi-K2.6 model, which is then used to finetune the Eagle modules. This model is ready for inference with TensorRT-LLM in Eagle speculative decoding mode. Eagle modules are used to predict candidate tokens beyond the next token. In the generation step, each forward Eagle module generates a distribution of tokens beyond the previous. The longest accepted candidate sequence is selected so that more than 1 token is returned in the generation step. The number of tokens generated in each step is called acceptance rate.
Usage
To serve the checkpoint with TensorRT-LLM, follow the sample commands below with the TensorRT-LLM GitHub repo:
Acceptance rate on SPEED-Bench (qualitative subset) with draft length 3:
Category
SPEED-Bench Acceptance Rate
coding
2.90
humanities
2.42
math
2.86
multilingual
3.01
qa
2.48
rag
3.00
reasoning
2.76
roleplay
2.23
stem
2.57
summarization
2.82
writing
2.33
Overall Average
2.67
Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.
For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards below.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
SUBCARDS:
Explainability
Field:
Response:
Intended Task/Domain:
Text generation, reasoning, summarization, and question answering.
Model Type:
Text and Image-to-text transformer
Intended Users:
This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency.
Output:
Text String(s)
Describe how the model works:
Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:
Not Applicable
Technical Limitations & Mitigation:
The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. Therefore, before deploying any applications of this model, developers should perform safety testing and tuning tailored to their specific applications of the model.
Verified to have met prescribed quality standards?
Yes
Performance Metrics:
Accuracy, Throughput, and user-side throughput
Potential Known Risk
The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
Licensing:
Your usage is governed by the following Governing Terms: Use of this model is governed by the NVIDIA Open Model License.
ADDITIONAL INFORMATION : Modified MIT License. Kimi-K2.6 . |
|Model and Dataset Restrictions:|The Principle of least privilege (PoLP) is applied limiting access for dataset generation. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.|
Privacy
Field:
Response:
Generatable or Reverse engineerable personal data?
No
Personal data used to create this model?
No
Was consent obtained for any personal data used?
Not Applicable
How often is dataset reviewed?
Before Release
Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model?
No
Is there provenance for all datasets used in training?
Yes
Does data labeling (annotation, metadata) comply with privacy laws?
Yes
Is data compliant with data subject requests for data correction or removal, if such a request was made?
