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
File size: 2,597 Bytes
c789784 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | {
"architectures": [
"LlamaForCausalLMEagle3"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 163584,
"draft_vocab_size": 163840,
"dtype": "bfloat16",
"eos_token_id": 163585,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 7168,
"initializer_range": 0.02,
"intermediate_size": 12288,
"max_position_embeddings": 262144,
"max_window_layers": 36,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 64,
"num_hidden_layers": 1,
"num_key_value_heads": 64,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 163840,
"quantization_config": {
"global_quant_config": {
"input_tensors": {
"dtype": "fp8_e4m3",
"is_dynamic": true,
"qscheme": "per_channel",
"ch_axis": 1,
"group_size": null,
"block_size": null,
"symmetric": true,
"round_method": "half_even",
"scale_type": "float",
"scale_format": null,
"scale_calculation_mode": null,
"mx_element_dtype": null,
"observer_cls": "PerChannelMinMaxObserver",
"is_scale_quant": false,
"enable_buffer_reuse": false,
"max_input_numel": 4194304
},
"output_tensors": null,
"weight": {
"dtype": "fp8_e4m3",
"is_dynamic": false,
"qscheme": "per_channel",
"ch_axis": 0,
"group_size": null,
"block_size": null,
"symmetric": true,
"round_method": "half_even",
"scale_type": "float",
"scale_format": null,
"scale_calculation_mode": null,
"mx_element_dtype": null,
"observer_cls": "PerChannelMinMaxObserver",
"is_scale_quant": false,
"enable_buffer_reuse": false,
"max_input_numel": 4194304
},
"bias": null,
"target_device": null
},
"exclude": [
"re:.*fc.*",
"re:.*lm_head.*"
],
"algo_config": null,
"softmax_quant_spec": null,
"quant_method": "quark",
"layer_type_quant_config": {},
"layer_quant_config": {},
"kv_cache_quant_config": {},
"kv_cache_post_rope": false,
"quant_mode": "eager_mode",
"version": "0.12+5bd6865d5ca",
"export": {
"kv_cache_group": [],
"min_kv_scale": 0.0,
"pack_method": "reorder",
"weight_format": "real_quantized",
"weight_merge_groups": null
}
}
} |