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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02fde0d6b97f8469ce2ae81b9d498d00b430e0ea852e652a7507e3a497d533ea
- Size of remote file:
- 2.8 MB
- SHA256:
- b6c497a7469b33ced9c38afb1ad6e47f03f5e5dc05f15930799210ec050c5103
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