Fx-Bio
Introduction
Fx-Bio-0913 is a biomedical reasoning large language model post-trained on DeepSeek-V4-Flash, developed by The Endless Frontier lab. It is specialized for biological and biomedical research tasks — including gene-function puzzles, experimental reasoning, and multi-step evidence integration — through post-training on curated biomedical reasoning data (Biomni / BioMystery-related tasks). The model retains the 1M-token context length of its base model, and its weights are openly released under Apache-2.0.
Main Results
Fx-Bio-0913 on two biomedical reasoning benchmarks, BioMysteryBench and BiominiBench, compared with the DeepSeek-V4-Flash base model and Opus-5. Post-training yields consistent gains over the base model across all metrics, with the largest improvements on harder problems (BioMysteryBench Human-Difficult, +5.8) and on pass@3 of BiominiBench (+6.6).
Benchmark Results
Comparison of Fx-Bio-0913 with representative frontier models on BioMysteryBench (avg@5 and pass@5) and BiominiBench (avg@3 and pass@3). All scores are Accuracy (%) from our internal evaluation pipeline; higher is better.
| Benchmark | Opus5 | GPT5.6 | Gemini 3.8Flash | GLM5.3 | Qwen3.8 FlashNext (1M) | DeepSeek V4.1Flash | DeepSeek V4Flash | Fx-Bio0913 |
|---|---|---|---|---|---|---|---|---|
| BioMysteryBench | ||||||||
| Human-Solvable (avg@5) | 89.3 | 85.5 | 88.8 | 84.7 | 86.9 | 89.0 | 85.2 | 85.5 |
| Human-Difficult (avg@5) | 45.9 | 34.1 | 42.4 | 47.1 | 49.4 | 36.5 | 31.8 | 37.6 |
| pass@5 | 86.7 | 85.6 | 84.4 | 88.9 | 86.7 | 85.6 | 82.2 | 86.7 |
| BiominiBench | ||||||||
| avg@3 | 79.1 | 68.2 | 69.3 | 81.1 | 80.6 | 80.9 | 71.0 | 77.9 |
| pass@3 | 86.7 | 78.6 | 78.5 | 87.2 | 88.9 | 85.9 | 78.7 | 85.3 |
Model Overview
| Item | Details |
|---|---|
| Base model | DeepSeek-V4-Flash |
| Architecture | MoE causal LM (DeepseekV4ForCausalLM) |
| Weights | bf16 safetensors (114 shards) |
| Context length | 1M tokens |
| Checkpoint | checkpoint-265 |
| License | Apache-2.0 |
Quickstart
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "endless-frontier/Fx-Bio"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="bfloat16",
device_map="auto",
)
inputs = tokenizer("<your prompt>", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
[!Important] Requires a
transformersversion that supports theDeepseekV4ForCausalLMarchitecture. For production workloads or high-throughput scenarios, a dedicated serving framework (e.g. SGLang or vLLM) with DeepSeek-V4 support is recommended.
Training
Fx-Bio is post-trained on biomedical reasoning data (Biomni / BioMystery-related tasks) with Megatron-LM. The detailed training recipe and data composition will be described in an upcoming technical report.
args.json contains the original Megatron-LM launch arguments for reproducibility.
Disclaimer
For research use only. Model outputs may contain errors or inaccuracies and must not be used directly for clinical diagnosis or medical decision-making.