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Model: openmoss/SmolLM-1B7-MHA-d_kv_128 Source: Original Platform
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README.md
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---
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license: apache-2.0
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datasets:
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- HuggingFaceTB/smollm-corpus
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base_model:
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- HuggingFaceTB/SmolLM-1.7B
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pipeline_tag: text-generation
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---
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**Research Paper** ["Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs"](https://arxiv.org/abs/2502.14837)
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## Inference
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- Step 1: Download the [**monkey patch file**](https://github.com/JT-Ushio/MHA2MLA/blob/main/src/mha2mla/monkey_patch.py).
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```shell
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wget https://raw.githubusercontent.com/JT-Ushio/MHA2MLA/refs/heads/main/src/mha2mla/monkey_patch.py
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```
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- Step 2(Option): For MHA2MLA models using Partial-RoPE 2-nrom method, Download the [**qk_2-norm file**](https://github.com/JT-Ushio/MHA2MLA/tree/main/utils).
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Take `qk_tensor_1.7B.pth` as an example:
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```shell
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wget https://github.com/JT-Ushio/MHA2MLA/raw/refs/heads/main/utils/qk_tensor_1.7B.pth
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```
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- Step 3: Download the [MHA2MLA models](https://huggingface.co/fnlp/SmolLM-1B7-MHA-d_kv_128) and run inference.
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Take `fnlp/SmolLM-1B7-MHA-d_kv_128` as an example:
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```python
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import torch
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from transformers import AutoConfig, AutoTokenizer, LlamaForCausalLM
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from monkey_patch import infer_monkey_patch
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model_name = "fnlp/SmolLM-1B7-MHA-d_kv_128"
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# Monkey Patch: MHA -> MLA
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config = AutoConfig.from_pretrained(model_name)
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if "RoPE" in config:
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config.RoPE["qk_tensor_path"] = "qk_tensor_1.7B.pth" # Configuration for Specific Models
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infer_monkey_patch(config.RoPE)
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = LlamaForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.bfloat16).cuda()
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# Generate
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text = "Which American-born Sinclair won the Nobel Prize for Literature in 1930?"
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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generation_kwargs = {"do_sample": False, "use_cache": True, "max_new_tokens": 128}
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output = model.generate(**inputs, **generation_kwargs)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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# - Sinclair Lewis
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```
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## Citation
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```
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@misc{ji2025economicalinferenceenablingdeepseeks,
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title={Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs},
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author={Tao Ji and Bin Guo and Yuanbin Wu and Qipeng Guo and Lixing Shen and Zhan Chen and Xipeng Qiu and Qi Zhang and Tao Gui},
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year={2025},
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eprint={2502.14837},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.14837},
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}
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```
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 24,
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"num_key_value_heads": 32,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.3",
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"use_cache": true,
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"vocab_size": 49152
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}
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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merges.txt
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