| --- |
| base_model: meta-llama/Llama-3.2-1B-Instruct |
| license: llama3.2 |
| tags: |
| - knowledge-graph-completion |
| - link-prediction |
| - biomedical |
| - cold-start |
| - llm-as-scorer |
| pipeline_tag: text-generation |
| --- |
| |
| # Cold-Start Chemical–Gene Ranking Readout (1B) |
|
|
| **Built with Llama.** Full fine-tune of **`meta-llama/Llama-3.2-1B-Instruct`** from the paper |
| *"Cold-Start Link Prediction Needs a Ranking Readout"* (GaLM 2026 @ CIKM). The model is read |
| out as a **length-normalized sequence log-probability scorer** to rank candidate genes for a |
| chemical–gene interaction query, under a cold-start (unseen-chemical) protocol. |
|
|
| This is the **reproducible headline model**: a *generic* (non-biomedical) Llama-3.2-1B, |
| fine-tuned with a retained recipe, that reaches the same numbers as the biomedical 1B used in |
| the paper — showing the result rides **general pretraining**, not domain-specific leakage. |
|
|
| ## Headline results (cold-start, full candidate set of 13,972 genes) |
| | Metric | Value | |
| |---|---| |
| | Full-candidate MRR | **0.536** | |
| | Sampled MRR (random neg, K=99) | 0.837 | |
| | Sampled MRR (hard neg, K=99) | 0.798 | |
| | Popularity floor (full-candidate) | 0.106 | |
|
|
| The score is `s(h,r,g) = (1/|g|) · log P_θ(g | π(h,r))`; genes are ranked by `s`. |
| Candidate-constrained decoding is equivalent to this ranking readout; **unconstrained |
| generation fails** at cold-start, which is the paper's central point. |
|
|
| ## Usage (sketch) |
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| repo = "BioRel/coldstart-ranking-readout-1b" |
| tok = AutoTokenizer.from_pretrained(repo) |
| lm = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map={"": 0}).eval() |
| |
| # rank genes by length-normalized log-prob of each candidate given the (chemical, relation) question: |
| # s(q, g) = (1/|g|) * sum_t log P(g_t | q, g_<t) |
| # Full scorer: https://github.com/<repo> (src/06_baselines/path_b_llm_ranking.py, kg_llm_exactrank.py) |
| ``` |
|
|
| ## Intended use & limitations |
| - **Research use only.** Cold-start chemical–gene *ranking* (scoring), not free generation and |
| not clinical decision-making. |
| - The companion LoRA adapters`BioRel/coldstart-lora-{1b,3b,8b}` is also provided. |
|
|
| ## Training data & license |
| - **Data:** derived from the **Comparative Toxicogenomics Database (CTD)**, https://ctdbase.org. |
| **Subject to CTD terms**; users must download CTD data themselves |
| (terms: https://ctdbase.org/about/legal.jsp). **CTD may access this dataset for quality control |
| purposes.** Non-commercial / research use. |
| - **Base model:** `meta-llama/Llama-3.2-1B-Instruct` — governed by the **Llama 3.2 Community |
| License**. You must accept Meta's license. This fine-tune is distributed under the same terms. |
|
|
| Please cite CTD: A. P. Davis et al., *Comparative Toxicogenomics Database (CTD): update 2021*, |
| Nucleic Acids Research, 2021. https://ctdbase.org |
|
|
