Transformers
Safetensors
llama
speculative-decoding
eagle3
draft-model
kimi-k2.5
fp8
amd-quark
quantized
no-lm-head-quantization
text-generation-inference
quark
Instructions to use amd/Kimi-K2.5-Eagle3-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amd/Kimi-K2.5-Eagle3-FP8 with Transformers:
# Load model directly from transformers import AutoTokenizer, LlamaForCausalLMEagle3 tokenizer = AutoTokenizer.from_pretrained("amd/Kimi-K2.5-Eagle3-FP8") model = LlamaForCausalLMEagle3.from_pretrained("amd/Kimi-K2.5-Eagle3-FP8") - Notebooks
- Google Colab
- Kaggle
Add detailed quantization environment to model card
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by larryli2 - opened
README.md
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No calibration dataset is required for this file-to-file quantization path.
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### vLLM Loading Note
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When using this FP8 Eagle3 checkpoint as a vLLM draft model, make sure the exported `config.json` records the excluded layers as regex patterns. If Quark exports:
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No calibration dataset is required for this file-to-file quantization path.
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### Quantization Environment
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Quantization was run on a single AMD GPU (ROCm) using the following software stack:
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| Component | Version |
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| --- | --- |
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| OS | Linux (x86_64) |
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| Python | 3.11 |
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| PyTorch | 2.9.0 (ROCm 6.4 build) |
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| `pytorch-triton-rocm` | 3.5.0 |
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| Transformers | 4.57.1 |
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| `huggingface_hub` | 0.36.0 |
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| `accelerate` | 1.11.0 |
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| `safetensors` | 0.6.2 |
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| `datasets` | 3.6.0 |
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### vLLM Loading Note
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When using this FP8 Eagle3 checkpoint as a vLLM draft model, make sure the exported `config.json` records the excluded layers as regex patterns. If Quark exports:
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