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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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+---
+license: apache-2.0
+datasets:
+- HuggingFaceTB/smollm-corpus
+base_model:
+- HuggingFaceTB/SmolLM-1.7B
+pipeline_tag: text-generation
+---
+
+**Research Paper** ["Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs"](https://arxiv.org/abs/2502.14837)
+
+## Inference
+
+- Step 1: Download the [**monkey patch file**](https://github.com/JT-Ushio/MHA2MLA/blob/main/src/mha2mla/monkey_patch.py).
+```shell
+wget https://raw.githubusercontent.com/JT-Ushio/MHA2MLA/refs/heads/main/src/mha2mla/monkey_patch.py
+```
+
+- 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). 
+Take `qk_tensor_1.7B.pth` as an example:
+```shell
+wget https://github.com/JT-Ushio/MHA2MLA/raw/refs/heads/main/utils/qk_tensor_1.7B.pth
+```
+
+- Step 3: Download the [MHA2MLA models](https://huggingface.co/fnlp/SmolLM-1B7-MHA-d_kv_128) and run inference. 
+Take `fnlp/SmolLM-1B7-MHA-d_kv_128` as an example:
+
+```python
+import torch
+from transformers import AutoConfig, AutoTokenizer, LlamaForCausalLM
+from monkey_patch import infer_monkey_patch
+
+model_name = "fnlp/SmolLM-1B7-MHA-d_kv_128"
+
+# Monkey Patch: MHA -> MLA
+config = AutoConfig.from_pretrained(model_name)
+if "RoPE" in config:
+    config.RoPE["qk_tensor_path"] = "qk_tensor_1.7B.pth"  # Configuration for Specific Models
+    infer_monkey_patch(config.RoPE)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = LlamaForCausalLM.from_pretrained(model_name, config=config, torch_dtype=torch.bfloat16).cuda()
+
+# Generate
+text = "Which American-born Sinclair won the Nobel Prize for Literature in 1930?"
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+generation_kwargs = {"do_sample": False, "use_cache": True, "max_new_tokens": 128}
+output = model.generate(**inputs, **generation_kwargs)
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+# - Sinclair Lewis
+```
+
+## Citation
+```
+@misc{ji2025economicalinferenceenablingdeepseeks,
+      title={Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs}, 
+      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},
+      year={2025},
+      eprint={2502.14837},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.14837}, 
+}
+```
+

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 2048,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "use_cache": true,
+  "vocab_size": 49152
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.46.3"
+}

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special_tokens_map.json

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tokenizer_config.json

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+}