Sliva322

Qwen3-8B-biotech-news-virality-QLoRA

README

License: apache-2.0

Model Details

Base model: Qwen/Qwen3-8B (8.2B parameters), loaded in 4-bit NF4
Adapter: LoRA (r=16, alpha=32, dropout=0.05) on all attention and MLP projections (q/k/v/o_proj, gate/up/down_proj), 43.6M trainable parameters (0.53% of the model)
Task: Headline → predicted engagement percentile, framed as instruction-following text generation
Input: A news headline / title (plain text, no additional context required)
Output: {"engagement_score": <float from 0 to 1>}. Higher values indicate the model expects stronger engagement.
Thinking mode: disabled (enable_thinking=False) during both training and inference

How to Use

This adapter was built to help biotech communicators, PR teams, and researchers gauge in advance how a headline is likely to land with an audience, before publishing.

The prompt must match the training format exactly (same system prompt and user template).

python

import reimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel BASE_MODEL = "Qwen/Qwen3-8B"ADAPTER_ID = "Sliva322/Qwen3-8B-biotech-news-virality-QLoRA" SYSTEM_PROMPT = (    "You predict the engagement percentile of a biotechnology news article. "    "Return ONLY valid JSON with one key named engagement_score. "    "The value must be a decimal number between 0 and 1. "    "Do not add explanations, markdown, or extra keys.") tokenizer = AutoTokenizer.from_pretrained(ADAPTER_ID) base = AutoModelForCausalLM.from_pretrained(    BASE_MODEL,    quantization_config=BitsAndBytesConfig(        load_in_4bit=True,        bnb_4bit_quant_type="nf4",        bnb_4bit_use_double_quant=True,        bnb_4bit_compute_dtype=torch.float16,    ),    device_map="auto",)model = PeftModel.from_pretrained(base, ADAPTER_ID).eval()  def predict_score(title: str) -> float | None:    messages = [        {"role": "system", "content": SYSTEM_PROMPT},        {"role": "user", "content": f"Predict the engagement score for this article title.\n\nTitle: {title}"},    ]    prompt = tokenizer.apply_chat_template(        messages, tokenize=False, add_generation_prompt=True, enable_thinking=False    )    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)    with torch.inference_mode():        out = model.generate(            **inputs, max_new_tokens=24, do_sample=False, pad_token_id=tokenizer.pad_token_id        )    text = tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)    m = re.search(r'"engagement_score"\s*:\s*([0-9]*\.?[0-9]+)', text)    return float(m.group(1)) if m else None  sample_title = "With $263M, LifeMine unearths a new drug for organ transplants"print(f"Title: {sample_title}")print(f"Predicted Engagement Score: {predict_score(sample_title):.4f}")

Requirements: transformers, peft, bitsandbytes>=0.46.1, accelerate, and a CUDA GPU with ~8 GB+ VRAM for 4-bit inference.

Training Details

The training data was built from posts in the r/biotech subreddit, filtered to the "Biotech News 📰" flair. For each post, the title text was paired with a target engagement score derived from the post's upvotes and comment counts.

Source: r/biotech, "Biotech News 📰" flair
Features used: Post title only
Target: Engagement score derived from upvotes and number of comments, rounded to 4 decimals in the JSON answer
Dataset file: Final training dataset
Split: chronological, first 80% for training (2051 titles) and last 20% for validation (513 titles)

Supervised fine-tuning setup:

Table with columns: Parameter, Value
Parameter	Value
Quantization	4-bit NF4, double quantization, fp16 compute
LoRA	r=16, alpha=32, dropout=0.05, all linear projections
Loss	Causal LM loss on the assistant answer only (prompt tokens masked)
Optimizer	paged AdamW 8-bit, lr 2e-4, cosine schedule, 19 warmup steps
Batch	micro-batch 1 × gradient accumulation 16
Epochs	2 (258 steps)
Max sequence length	512
Hardware	1× NVIDIA T4 (15 GB), ~67 min

Evaluation

Validated on 513 recent biotech news headlines (from February 2026 onward, unseen during training). Predictions were produced with greedy decoding; 100% of generations were valid, parseable JSON.

Table with columns: Model, RMSE, MAE, R², Pearson, Spearman, Accuracy @0.05
Model	RMSE	MAE	R²	Pearson	Spearman	Accuracy @0.05
Qwen3-8B + this QLoRA adapter	0.3367	0.2463	-0.2885	0.4409	0.4515	0.2378
Qwen3-8B (zero-shot, same prompt)	0.3900	0.3134	—	0.1207	—

ROC AUC at threshold = 0.8 (classifying a headline as high-virality):

Table with columns: Model, ROC AUC
Model	ROC AUC
Qwen3-8B + this QLoRA adapter	0.766
Qwen3-8B (zero-shot)	0.555
Claude Sonnet 5 (prompt example)	0.697
bartowski/gemma-2-9b-it-GGUF	0.565

At the 0.8 threshold the adapter reaches precision 0.47, recall 0.49 and F1 0.48 on the high-virality class (97 of 513 headlines).

Fine-tuning raised ROC AUC from 0.555 to 0.766 and Pearson correlation from 0.12 to 0.44 over the same base model. It ranks headlines slightly better than the encoder-based BioModernBERT-news-virality and clearly better than zero-shot general-purpose LLMs.

Model Card Contact

For questions or issues, please open a discussion on the model's Hugging Face repository page.

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Model Details

Model Provider

Sliva322

Model Tree

Base

Qwen/Qwen3-8B

Adapter

this model

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README

License: apache-2.0

Model Details

Base model: Qwen/Qwen3-8B (8.2B parameters), loaded in 4-bit NF4
Adapter: LoRA (r=16, alpha=32, dropout=0.05) on all attention and MLP projections (q/k/v/o_proj, gate/up/down_proj), 43.6M trainable parameters (0.53% of the model)
Task: Headline → predicted engagement percentile, framed as instruction-following text generation
Input: A news headline / title (plain text, no additional context required)
Output: {"engagement_score": <float from 0 to 1>}. Higher values indicate the model expects stronger engagement.
Thinking mode: disabled (enable_thinking=False) during both training and inference

How to Use

This adapter was built to help biotech communicators, PR teams, and researchers gauge in advance how a headline is likely to land with an audience, before publishing.

The prompt must match the training format exactly (same system prompt and user template).

python

import reimport torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel BASE_MODEL = "Qwen/Qwen3-8B"ADAPTER_ID = "Sliva322/Qwen3-8B-biotech-news-virality-QLoRA" SYSTEM_PROMPT = (    "You predict the engagement percentile of a biotechnology news article. "    "Return ONLY valid JSON with one key named engagement_score. "    "The value must be a decimal number between 0 and 1. "    "Do not add explanations, markdown, or extra keys.") tokenizer = AutoTokenizer.from_pretrained(ADAPTER_ID) base = AutoModelForCausalLM.from_pretrained(    BASE_MODEL,    quantization_config=BitsAndBytesConfig(        load_in_4bit=True,        bnb_4bit_quant_type="nf4",        bnb_4bit_use_double_quant=True,        bnb_4bit_compute_dtype=torch.float16,    ),    device_map="auto",)model = PeftModel.from_pretrained(base, ADAPTER_ID).eval()  def predict_score(title: str) -> float | None:    messages = [        {"role": "system", "content": SYSTEM_PROMPT},        {"role": "user", "content": f"Predict the engagement score for this article title.\n\nTitle: {title}"},    ]    prompt = tokenizer.apply_chat_template(        messages, tokenize=False, add_generation_prompt=True, enable_thinking=False    )    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)    with torch.inference_mode():        out = model.generate(            **inputs, max_new_tokens=24, do_sample=False, pad_token_id=tokenizer.pad_token_id        )    text = tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)    m = re.search(r'"engagement_score"\s*:\s*([0-9]*\.?[0-9]+)', text)    return float(m.group(1)) if m else None  sample_title = "With $263M, LifeMine unearths a new drug for organ transplants"print(f"Title: {sample_title}")print(f"Predicted Engagement Score: {predict_score(sample_title):.4f}")

Requirements: transformers, peft, bitsandbytes>=0.46.1, accelerate, and a CUDA GPU with ~8 GB+ VRAM for 4-bit inference.

Training Details

Source: r/biotech, "Biotech News 📰" flair
Features used: Post title only
Target: Engagement score derived from upvotes and number of comments, rounded to 4 decimals in the JSON answer
Dataset file: Final training dataset
Split: chronological, first 80% for training (2051 titles) and last 20% for validation (513 titles)

Supervised fine-tuning setup:

Table with columns: Parameter, Value
Parameter	Value
Quantization	4-bit NF4, double quantization, fp16 compute
LoRA	r=16, alpha=32, dropout=0.05, all linear projections
Loss	Causal LM loss on the assistant answer only (prompt tokens masked)
Optimizer	paged AdamW 8-bit, lr 2e-4, cosine schedule, 19 warmup steps
Batch	micro-batch 1 × gradient accumulation 16
Epochs	2 (258 steps)
Max sequence length	512
Hardware	1× NVIDIA T4 (15 GB), ~67 min

Evaluation

Validated on 513 recent biotech news headlines (from February 2026 onward, unseen during training). Predictions were produced with greedy decoding; 100% of generations were valid, parseable JSON.

Table with columns: Model, RMSE, MAE, R², Pearson, Spearman, Accuracy @0.05
Model	RMSE	MAE	R²	Pearson	Spearman	Accuracy @0.05
Qwen3-8B + this QLoRA adapter	0.3367	0.2463	-0.2885	0.4409	0.4515	0.2378
Qwen3-8B (zero-shot, same prompt)	0.3900	0.3134	—	0.1207	—

ROC AUC at threshold = 0.8 (classifying a headline as high-virality):

Table with columns: Model, ROC AUC
Model	ROC AUC
Qwen3-8B + this QLoRA adapter	0.766
Qwen3-8B (zero-shot)	0.555
Claude Sonnet 5 (prompt example)	0.697
bartowski/gemma-2-9b-it-GGUF	0.565

At the 0.8 threshold the adapter reaches precision 0.47, recall 0.49 and F1 0.48 on the high-virality class (97 of 513 headlines).

Model Card Contact

For questions or issues, please open a discussion on the model's Hugging Face repository page.