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  4. BioMedLM
open sourcellm

BioMedLM

Apache 2.0 biomedical LLM trained on 50B PubMed tokens — perfect medical AI base

Developed by Stanford CRFM & MosaicML

Try Model
2.7BParams
YesAPI
stableStability
BioMedLM 2.7BVersion
Apache 2.0License
PyTorchFramework
YesRuns Local

Playground

Implementation Example

Example Prompt

user input
Summarize the typical mechanism of action for SGLT2 inhibitors in treating type 2 diabetes.

Model Output

model response
SGLT2 (sodium-glucose cotransporter 2) inhibitors act on the proximal tubule of the kidney, blocking SGLT2-mediated glucose reabsorption from the glomerular filtrate. This causes excess glucose to be excreted in urine (glucosuria), lowering blood glucose levels independently of insulin. Additional benefits include modest weight loss, blood pressure reduction, and proven cardiovascular and renal protective effects.

Examples

Real-World Applications

  • PubMed literature assistants
  • medical Q&A
  • clinical research summarization
  • drug discovery NLP
  • biomedical entity linking
  • medical education tools.

Docs

Model Intelligence & Architecture

What is BioMedLM?

BioMedLM (formerly PubMedGPT) is a 2.7-billion-parameter language model developed by Stanford CRFM (Center for Research on Foundation Models) and MosaicML, released in December 2022. It is trained exclusively on biomedical literature from PubMed — over 50 billion tokens of medical research papers, clinical notes, and biomedical texts.

It's released under the Apache 2.0 license, free for any commercial use including healthcare research and pharmaceutical applications.

Why BioMedLM Is Still Relevant in 2026

While newer medical LLMs like OpenBioLLM and Meditron have surpassed BioMedLM on benchmarks, it remains highly valued for its small size, full Apache 2.0 license, and pure biomedical training — making it ideal as a fine-tuning base for specialized medical AI tools.

Key Features and Capabilities

BioMedLM supports medical Q&A, biomedical literature summarization, clinical note understanding, drug interaction analysis, medical entity recognition, and PubMed-based reasoning.

Who Should Use BioMedLM?

BioMedLM is built for medical researchers, biomedical NLP engineers, healthcare startups, pharmaceutical companies, and academic medical schools.

Top Use Cases

Real-world applications include PubMed literature search assistants, medical question answering, clinical research summarization, drug discovery NLP, biomedical entity linking, and medical education tools.

Where Can You Run It?

BioMedLM runs on Hugging Face Transformers, MosaicML's inference toolkit, and vLLM. The 2.7B model fits in 6 GB VRAM at full precision.

How to Use BioMedLM (Quick Start)

Load via Hugging Face: AutoModelForCausalLM.from_pretrained('stanford-crfm/BioMedLM'). For best results, fine-tune on your specific medical task with a few thousand examples.

When Should You Choose BioMedLM?

Choose BioMedLM when you need a small, Apache 2.0 medical LLM as a fine-tuning base. For ready-to-use clinical Q&A, OpenBioLLM-7B or Meditron 70B perform better.

Pricing

BioMedLM is completely free under Apache 2.0.

Pros and Cons

Pros: ✔ Apache 2.0 license ✔ Pure biomedical training ✔ Small 2.7B size ✔ Stanford CRFM backing ✔ Easy to fine-tune ✔ Fast inference

Cons: ✘ Surpassed by OpenBioLLM and Meditron ✘ Limited general knowledge ✘ 2K context window ✘ Not for clinical decisions without supervision

Final Verdict

BioMedLM remains a foundational Apache 2.0 biomedical LLM in 2026 — perfect as a fine-tuning starting point. Discover more medical AI at FreeAPIHub.com.

Evaluation

Advantages & Limitations

Advantages
  • ✓ Apache 2.0 license
  • ✓ Pure biomedical training
  • ✓ Small 2.7B size
  • ✓ Stanford CRFM backing
  • ✓ Easy to fine-tune
  • ✓ Fast inference
Limitations
  • ✗ Surpassed by OpenBioLLM and Meditron
  • ✗ Limited general knowledge
  • ✗ 2K context window
  • ✗ Not for clinical decisions without supervision

Important Notice

Verify Before You Decide

Last verified · Apr 29, 2026

The details on this page — including pricing, features, and availability — are based on our last review and may not reflect the provider's current offering. Providers update their products frequently, sometimes without prior notice.

What may have changed

Pricing Plans
Features & Limits
Availability
Terms & Policies

Always visit the official provider website to confirm the latest pricing, terms, and feature availability before subscribing or integrating.

Check official site

External Resources

Try the Model Official Website Source Code

Technical Details

Architecture
GPT-style decoder transformer
Stability
stable
Framework
PyTorch
License
Apache 2.0
Release Date
2022-12-15
Signup Required
No
API Available
Yes
Runs Locally
Yes

Rate Limits

No limits self-hosted

Pricing

Completely free under Apache 2.0

Best For

Medical researchers needing an Apache 2.0 fine-tuning base for biomedical NLP

Alternative To

BioBERT, BioMegatron, ClinicalBERT

Compare With

biomedlm vs openbiollmbiomedlm vs meditronbiomedlm vs biobertfree medical llmpubmedgpt

Tags

#Biomedical NLP#Stanford Crfm#Biomedlm#Healthcare LLM#Medical AI#Open Source AI

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