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+architecture.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,48 @@
+ LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
+
+ Llama 3.1 Version Release Date: July 23, 2024
+
+“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
+
+“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.
+
+“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+“Llama 3.1” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.
+
+“Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion thereof) made available under this Agreement.
+
+“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 
+
+By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
+
+1. License Rights and Redistribution.

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+
+    b. Redistribution and Use.  
+
+          i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name.
+
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+
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+
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+  
+2. Additional Commercial Terms. If, on the Llama 3.1 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
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README.md

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----
-license: llama3.1
----
+---
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+datasets:
+- DongkiKim/Mol-LLaMA-Instruct
+language:
+- en
+license: apache-2.0
+tags:
+- biology
+- chemistry
+- medical
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Mol-Llama-3.1-8B-Instruct-Full-Weights
+[[Project Page](https://mol-llama.github.io/)] [[Paper](https://arxiv.org/abs/2502.13449)] [[GitHub](https://github.com/DongkiKim95/Mol-LLaMA)]
+
+This repo contains the weights of Mol-LLaMA including the LoRA weights and projectors, build with Llama: [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
+Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
+
+## Architecture
+![image.png](architecture.png)
+1) Molecular encoders: Pretrained 2D encoder ([MoleculeSTM](https://huggingface.co/chao1224/MoleculeSTM)) and 3D encoder ([Uni-Mol](https://huggingface.co/dptech/Uni-Mol-Models))
+2) Blending Module: Combining complementary information from 2D and 3D encoders via cross-attention
+3) Q-Former: Embed molecular representations into query tokens based on [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased)
+4) LoRA: Adapters for fine-tuning LLMs
+
+
+## Training Dataset
+
+Mol-LLaMA is trained on [Mol-LLaMA-Instruct](https://huggingface.co/datasets/DongkiKim/Mol-LLaMA-Instruct), to learn the fundamental characteristics of molecules with the reasoning ability and explanbility.
+
+## Citation
+
+If you find our model useful, please consider citing our work.
+```
+@misc{kim2025molllama,
+    title={Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model},
+    author={Dongki Kim and Wonbin Lee and Sung Ju Hwang},
+    year={2025},
+    eprint={2502.13449},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+## Acknowledgements
+
+We appreciate [LLaMA](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct), [3D-MoLM](https://huggingface.co/Sihangli/3D-MoLM), [MoleculeSTM](https://huggingface.co/chao1224/MoleculeSTM), [Uni-Mol](https://huggingface.co/dptech/Uni-Mol-Models) and [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased) for their open-source contributions.

config.json

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+{
+  "architectures": [
+    "MolLLaMA"
+  ],
+  "model_type": "mol_llama",
+  "blending_module_config": {
+    "num_heads": 8,
+    "num_layers": 4
+  },
+  "graph_encoder_config": {
+    "encoder_types": [
+      "unimol",
+      "moleculestm"
+    ],
+    "moleculestm_config": {
+      "filename": "demo/demo_checkpoints_Graph/molecule_model.pth",
+      "model_type": "mol_llama_molecule_stm"
+    },
+    "unimol_config": {
+      "dictionary_filename": "mol.dict.txt",
+      "repo_id": "dptech/Uni-Mol-Models",
+      "weights_filename": "mol_pre_no_h_220816.pt"
+    }
+  },
+  "llm_config": {
+    "llm_model": "meta-llama/Llama-3.1-8B-Instruct",
+    "lora_config": {
+      "lora_alpha": 32,
+      "lora_dropout": 0.1,
+      "r": 8
+    }
+  },
+  "qformer_config": {
+    "bert_name": "allenai/scibert_scivocab_uncased",
+    "cross_attention_freq": 2,
+    "embed_dim": 256,
+    "num_query_tokens": 8
+  }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "transformers_version": "4.43.4"
+}

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special_tokens_map.json

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+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128256": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128257": {
+      "content": "<mol>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<mol>"
+  ],
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}