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Model: RehanaHasin/qwen2.5-7b-instruct-adjuvant-extractor
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- biomedical
+- infectious-disease
+- vaccine-adjuvant
+- information-extraction
+- evidence-grounded
+base_model: Qwen/Qwen2.5-7B-Instruct
+---
+
+# qwen2.5-7b-instruct-adjuvant-extractor
+
+This model is a task-specific fine-tuned version of **Qwen/Qwen2.5-7B-Instruct** for extracting **vaccine adjuvant concepts** and associated **evidence snippets** from infectious disease biomedical abstracts.
+
+## Model Summary
+
+- **Model name:** `qwen2.5-7b-instruct-adjuvant-extractor`
+- **Base model:** `Qwen/Qwen2.5-7B-Instruct`
+- **Fine-tuning method:** LoRA adapter training, merged into full model weights
+- **Primary task:** Evidence-linked adjuvant extraction from title+abstract text
+
+## Prompt Used for Inference
+
+### System prompt
+
+```text
+You are a biomedical information extraction assistant.
+```
+
+### User instruction template
+
+```text
+Extract infectious-disease adjuvants from the text and provide evidence snippets.
+Return ONLY valid JSON in this format:
+[{"adjuvant": "<string>", "evidence": "<string>"}, ...]
+Do not include any extra keys or explanation.
+```
+
+### Input format
+
+```text
+Title: <paper title>
+Abstract: <paper abstract>
+```
+
+The model receives the user instruction template followed by the title/abstract text.
+
+## Actual Output Format Target
+
+The model is prompted to return a JSON array of objects with exactly two keys:
+
+```json
+[
+  {
+    "adjuvant": "<string>",
+    "evidence": "<string>"
+  }
+]
+```
+
+Expected behavior:
+- Return a JSON array (can be empty: `[]`).
+- Each object must contain:
+  - `adjuvant`: normalized or near-normalized adjuvant name
+  - `evidence`: supporting text snippet from the same input abstract
+- No extra keys and no explanatory text outside JSON.
+
+## Input/Output Example
+
+### Example Input
+
+```text
+Title: Intranasal vaccination study using alum and MPLA adjuvants in a murine influenza model.
+Abstract: Mice immunized with antigen formulated with alum showed increased IgG titers. A separate group receiving MPLA-adjuvanted vaccine demonstrated stronger IFN-gamma responses and reduced viral load after challenge.
+```
+
+### Expected Output
+
+```json
+[
+  {
+    "adjuvant": "alum",
+    "evidence": "Mice immunized with antigen formulated with alum showed increased IgG titers."
+  },
+  {
+    "adjuvant": "MPLA",
+    "evidence": "A separate group receiving MPLA-adjuvanted vaccine demonstrated stronger IFN-gamma responses and reduced viral load after challenge."
+  }
+]
+```
+
+### Notes on Output Validity
+
+- Output must be valid JSON.
+- Output must be a JSON array (use `[]` if no supported adjuvant is found).
+- Each item should include only `adjuvant` and `evidence`.
+- Evidence text should come from the provided input abstract.
+
+## Working Inference Code (Validated)
+
+```python
+import torch
+import json
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+repo_id = "RehanaHasin/Qwen2.5-7B-Instruct-adjuvant-extractor"
+SYS_PROMPT = "You are a biomedical information extraction assistant."
+PROMPT_INSTRUCTION = (
+    "Extract infectious-disease adjuvants from the text and provide evidence snippets.\n"
+    "Return ONLY valid JSON in this format:\n"
+    "[{\"adjuvant\": \"<string>\", \"evidence\": \"<string>\"}, ...]\n"
+    "Do not include any extra keys or explanation."
+)
+title = "Protective immune response against Streptococcus pyogenes in mice after intranasal vaccination with the fibronectin-binding protein SfbI."
+abstract = (
+    "Despite the significant impact on human health caused by Streptococcus pyogenes, "
+    "there is currently no vaccine available. Intranasal immunization of mice with either "
+    "SfbI alone or coupled to cholera toxin B subunit (CTB) triggered efficient SfbI-specific responses."
+)
+user_input = f"{PROMPT_INSTRUCTION}\n\nTitle: {title}\nAbstract: {abstract}"
+tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    dtype=torch.float16,
+    device_map="auto",
+)
+model.eval()
+chat = tokenizer.apply_chat_template(
+    [
+        {"role": "system", "content": SYS_PROMPT},
+        {"role": "user", "content": user_input},
+    ],
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer(chat, return_tensors="pt", truncation=True, max_length=1024)
+inputs = {k: v.to(model.get_input_embeddings().weight.device) for k, v in inputs.items()}
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+prediction = tokenizer.decode(
+    outputs[0][inputs["input_ids"].shape[1]:],
+    skip_special_tokens=True
+).strip()
+print(json.dumps(json.loads(prediction), indent=2, ensure_ascii=False))
+```
+
+## Intended Use
+
+This model is intended for research workflows in biomedical literature mining, especially:
+
+- infectious disease vaccine literature curation
+- vaccine adjuvant concept extraction
+- evidence-linked information extraction for downstream manual review
+
+This model is not intended for clinical decision-making.
+
+## Training Data and Split Context
+
+The model was trained on a curated infectious disease adjuvant corpus derived from VIOLIN ecosystem resources.
+
+- Corpus size used in workflow: 298 abstracts
+- Fixed split framework used across models:
+  - 256 train
+  - 13 validation
+  - 29 test
+
+## Training Configuration (Fixed Manuscript Setting)
+
+- LoRA rank (`r`): 8
+- Learning rate: `2e-4`
+- Epochs: `5`
+- Quantization during fine-tuning: 4-bit NF4 with double quantization
+- Compute dtype: float16
+- Per-device batch size and gradient accumulation were configured for stable updates across model families.
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+repo_id = "RehanaHasin/Qwen2.5-7B-Instruct-adjuvant-extractor"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+```
+
+## Prompting Recommendation
+
+Use prompts that explicitly request structured JSON output containing only:
+
+- `adjuvant`
+- `evidence`
+
+and restrict extra commentary to reduce parsing errors.
+
+## Limitations
+
+- Evaluated on a focused infectious-disease adjuvant corpus; broader-domain generalization is not guaranteed.
+- Performance depends on abstract quality and terminology variation.
+- Structured output may still require post-processing and manual validation.
+
+## Ethical and Safety Notes
+
+- Outputs can contain extraction errors or unsupported predictions.
+- Human review is required before downstream knowledge integration.
+- Not for diagnosis, treatment, or direct patient-care decisions.
+
+## Reproducibility Resources
+
+Code, notebooks, and workflow details are available at:  
+https://github.com/hurlab/Infectious-Disease-Adjuvant-LLM-Fine-tuning
+
+
+## Citation
+
+If you use this model, please cite the associated manuscript and project repository.
+
+## Contact
+
+For questions, please contact hasin.rehana@und.edu.

added_tokens.json

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+  "<tool_call>": 151657,
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+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
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+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.5",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.5"
+}

merge_metadata.json

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+{
+  "base_model": "/home/hasin.rehana/models/Qwen2.5-7B-Instruct",
+  "adapter_dir": "results/hparam_runs_qwen2.5_7b_GRID1_V2/run_8",
+  "output_dir": "finetuned_models/qwen2.5-7b-instruct-adjuvant-extractor",
+  "dtype": "float16",
+  "device_map": "cpu",
+  "tokenizer_source": "base_model"
+}

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