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	Opus 5	GPT 5.6	Gemini 3.8 Flash	GLM 5.3	Qwen3.8 Flash Next (1M)	DeepSeek V4.1 Flash	DeepSeek V4 Flash	Fx-Bio 0913
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))

Requires a transformers version that supports the DeepseekV4ForCausalLM architecture. 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.

License

Apache-2.0