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Model: RehanaHasin/qwen2.5-7b-instruct-adjuvant-extractor Source: Original Platform
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233
README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- biomedical
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- infectious-disease
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- vaccine-adjuvant
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- information-extraction
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- evidence-grounded
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base_model: Qwen/Qwen2.5-7B-Instruct
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---
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# qwen2.5-7b-instruct-adjuvant-extractor
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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.
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## Model Summary
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- **Model name:** `qwen2.5-7b-instruct-adjuvant-extractor`
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- **Base model:** `Qwen/Qwen2.5-7B-Instruct`
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- **Fine-tuning method:** LoRA adapter training, merged into full model weights
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- **Primary task:** Evidence-linked adjuvant extraction from title+abstract text
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## Prompt Used for Inference
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### System prompt
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```text
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You are a biomedical information extraction assistant.
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```
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### User instruction template
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```text
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Extract infectious-disease adjuvants from the text and provide evidence snippets.
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Return ONLY valid JSON in this format:
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[{"adjuvant": "<string>", "evidence": "<string>"}, ...]
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Do not include any extra keys or explanation.
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```
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### Input format
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```text
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Title: <paper title>
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Abstract: <paper abstract>
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```
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The model receives the user instruction template followed by the title/abstract text.
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## Actual Output Format Target
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The model is prompted to return a JSON array of objects with exactly two keys:
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```json
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[
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{
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"adjuvant": "<string>",
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"evidence": "<string>"
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}
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]
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```
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Expected behavior:
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- Return a JSON array (can be empty: `[]`).
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- Each object must contain:
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- `adjuvant`: normalized or near-normalized adjuvant name
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- `evidence`: supporting text snippet from the same input abstract
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- No extra keys and no explanatory text outside JSON.
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## Input/Output Example
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### Example Input
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```text
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Title: Intranasal vaccination study using alum and MPLA adjuvants in a murine influenza model.
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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.
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```
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### Expected Output
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```json
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[
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{
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"adjuvant": "alum",
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"evidence": "Mice immunized with antigen formulated with alum showed increased IgG titers."
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},
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{
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"adjuvant": "MPLA",
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"evidence": "A separate group receiving MPLA-adjuvanted vaccine demonstrated stronger IFN-gamma responses and reduced viral load after challenge."
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}
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]
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```
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### Notes on Output Validity
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- Output must be valid JSON.
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- Output must be a JSON array (use `[]` if no supported adjuvant is found).
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- Each item should include only `adjuvant` and `evidence`.
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- Evidence text should come from the provided input abstract.
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## Working Inference Code (Validated)
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```python
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import torch
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import json
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from transformers import AutoTokenizer, AutoModelForCausalLM
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repo_id = "RehanaHasin/Qwen2.5-7B-Instruct-adjuvant-extractor"
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SYS_PROMPT = "You are a biomedical information extraction assistant."
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PROMPT_INSTRUCTION = (
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"Extract infectious-disease adjuvants from the text and provide evidence snippets.\n"
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"Return ONLY valid JSON in this format:\n"
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"[{\"adjuvant\": \"<string>\", \"evidence\": \"<string>\"}, ...]\n"
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"Do not include any extra keys or explanation."
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)
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title = "Protective immune response against Streptococcus pyogenes in mice after intranasal vaccination with the fibronectin-binding protein SfbI."
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abstract = (
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"Despite the significant impact on human health caused by Streptococcus pyogenes, "
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"there is currently no vaccine available. Intranasal immunization of mice with either "
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"SfbI alone or coupled to cholera toxin B subunit (CTB) triggered efficient SfbI-specific responses."
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)
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user_input = f"{PROMPT_INSTRUCTION}\n\nTitle: {title}\nAbstract: {abstract}"
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tokenizer = AutoTokenizer.from_pretrained(repo_id, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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dtype=torch.float16,
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device_map="auto",
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)
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model.eval()
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chat = tokenizer.apply_chat_template(
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[
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{"role": "system", "content": SYS_PROMPT},
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{"role": "user", "content": user_input},
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],
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(chat, return_tensors="pt", truncation=True, max_length=1024)
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inputs = {k: v.to(model.get_input_embeddings().weight.device) for k, v in inputs.items()}
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=200,
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do_sample=False,
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pad_token_id=tokenizer.eos_token_id,
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)
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prediction = tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True
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).strip()
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print(json.dumps(json.loads(prediction), indent=2, ensure_ascii=False))
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```
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## Intended Use
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This model is intended for research workflows in biomedical literature mining, especially:
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- infectious disease vaccine literature curation
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- vaccine adjuvant concept extraction
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- evidence-linked information extraction for downstream manual review
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This model is not intended for clinical decision-making.
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## Training Data and Split Context
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The model was trained on a curated infectious disease adjuvant corpus derived from VIOLIN ecosystem resources.
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- Corpus size used in workflow: 298 abstracts
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- Fixed split framework used across models:
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- 256 train
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- 13 validation
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- 29 test
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## Training Configuration (Fixed Manuscript Setting)
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- LoRA rank (`r`): 8
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- Learning rate: `2e-4`
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- Epochs: `5`
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- Quantization during fine-tuning: 4-bit NF4 with double quantization
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- Compute dtype: float16
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- Per-device batch size and gradient accumulation were configured for stable updates across model families.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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repo_id = "RehanaHasin/Qwen2.5-7B-Instruct-adjuvant-extractor"
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tokenizer = AutoTokenizer.from_pretrained(repo_id)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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```
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## Prompting Recommendation
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Use prompts that explicitly request structured JSON output containing only:
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- `adjuvant`
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- `evidence`
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and restrict extra commentary to reduce parsing errors.
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## Limitations
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- Evaluated on a focused infectious-disease adjuvant corpus; broader-domain generalization is not guaranteed.
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- Performance depends on abstract quality and terminology variation.
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- Structured output may still require post-processing and manual validation.
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## Ethical and Safety Notes
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- Outputs can contain extraction errors or unsupported predictions.
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- Human review is required before downstream knowledge integration.
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- Not for diagnosis, treatment, or direct patient-care decisions.
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## Reproducibility Resources
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||||
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Code, notebooks, and workflow details are available at:
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https://github.com/hurlab/Infectious-Disease-Adjuvant-LLM-Fine-tuning
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## Citation
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||||
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If you use this model, please cite the associated manuscript and project repository.
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## Contact
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For questions, please contact hasin.rehana@und.edu.
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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||||
{%- 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" }}
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||||
{{- tool | tojson }}
|
||||
{%- endfor %}
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||||
{{- "\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' }}
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||||
{%- 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 %}
|
||||
58
config.json
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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
|
||||
}
|
||||
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generation_config.json
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generation_config.json
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{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"repetition_penalty": 1.05,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.57.5"
|
||||
}
|
||||
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merge_metadata.json
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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"
|
||||
}
|
||||
151388
merges.txt
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merges.txt
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Load Diff
3
model-00001-of-00004.safetensors
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3
model-00001-of-00004.safetensors
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||||
version https://git-lfs.github.com/spec/v1
|
||||
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|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user