EpistemeAI

Reasoning-Medical0.1-E4B-sft

README

License: apache-2.0

Model Details

Table with columns: Field, Description
Field	Description
Model name	`EpistemeAI/Reasoning-Medical0.1-E4B-sft`
Base model	`unsloth/gemma-4-E4B-it`
Training method	Supervised Fine-Tuning, SFT
Training data size	~100k medical reasoning examples
Primary language	English
Model type	Instruction-tuned causal language model
Domain	Medical reasoning, biomedical QA, clinical education
Intended users	Researchers, students, developers, clinicians using human review

Intended Use

This model is designed for:

Medical education and study support
Biomedical and clinical reasoning practice
Medical multiple-choice question reasoning
Literature review assistance
Research hypothesis exploration
Drafting clinician-reviewed explanations
Medical benchmark experimentation
AI safety and alignment research for healthcare models

This model should be used as a reasoning assistant, not as a final authority.

Out-of-Scope Use

Do not use this model for:

Autonomous medical diagnosis
Treatment decisions without clinician oversight
Emergency medical advice
Prescribing medication
Replacing a licensed clinician
Patient-specific medical decision-making without professional review
High-risk biological protocol design
Harmful biological, chemical, or virological assistance
Any use that violates applicable law, medical regulation, or safety policy

Medical Safety Notice

This model may produce incorrect, incomplete, outdated, or misleading medical information. Medical knowledge changes over time, and language models can hallucinate.

Users should:

Verify outputs against trusted medical references.
Use clinician review for any patient-facing or clinical workflow.
Avoid relying on the model for urgent or high-stakes decisions.
Treat the model as an educational and research-support tool only.

For medical concerns, consult a qualified healthcare professional.

Biological and Chemical Safety Notice

This model is not intended to assist with harmful biological, chemical, or dual-use activities.

The model should not be used to generate, optimize, troubleshoot, or operationalize:

Pathogen engineering
Toxin production
Chemical weapon synthesis
Biological weapon development
Evasion of biosafety controls
Protocols that increase virulence, transmissibility, resistance, or environmental persistence
Any unsafe wet-lab procedure outside appropriate institutional review and biosafety oversight

The intended safety boundary is medical education and biomedical reasoning, not actionable dangerous capability enablement.

Example Usage

Transformers Pipeline

python
from transformers import pipeline
import torch

model_id = "EpistemeAI/Reasoning-Medical0.1-E4B-sft"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": "A 65-year-old man has progressive shortness of breath, bibasilar crackles, and elevated BNP. Explain the likely diagnosis and reasoning."
    }
]

outputs = pipe(
    messages,
    max_new_tokens=1024,
    temperature=0.3,
    top_p=0.9,
)

print(outputs[0]["generated_text"])

Suggested Prompt Format

For best results, ask for structured reasoning and explicitly request uncertainty.

Example:

text
You are a medical reasoning assistant for education and research.

Question:
A patient presents with fever, neck stiffness, photophobia, and altered mental status. What is the most likely diagnosis?

Please provide:
1. Key clinical clues
2. Differential diagnosis
3. Most likely answer
4. Why alternatives are less likely
5. Safety caveats

Recommended Generation Settings

For factual medical reasoning, use lower temperature settings:

python
generation_config = {
    "max_new_tokens": 1024,
    "temperature": 0.2,
    "top_p": 0.9,
    "do_sample": True,
}

For benchmark-style multiple-choice evaluation, consider deterministic decoding:

python
generation_config = {
    "max_new_tokens": 512,
    "temperature": 0.0,
    "do_sample": False,
}

Training Summary

This model was fine-tuned using supervised fine-tuning on medical reasoning data.

The training objective was to improve:

Clinical reasoning structure
Biomedical explanation quality
Medical question-answering performance
Differential diagnosis reasoning
Medical exam-style reasoning
Safer, more cautious healthcare responses

The model was not trained to replace clinicians or provide autonomous medical advice.

Evaluation

Suggested evaluation benchmarks include:

MedQA
MedMCQA
PubMedQA
MMLU Clinical Knowledge
MMLU Medical Genetics
MMLU Anatomy
MMLU Professional Medicine
MMLU-Pro Biology
HealthBench-style rubric evaluation
Human clinician-reviewed evaluation

Evaluation results should be reported with:

Prompt format
Decoding settings
Number of few-shot examples
Exact model revision
Evaluation harness version
Random seed
Sample size

Limitations

This model may:

Hallucinate medical facts
Overstate confidence
Miss rare diagnoses
Fail to ask for missing clinical context
Produce outdated medical guidance
Confuse similar diseases, drugs, or mechanisms
Provide reasoning that sounds plausible but is incorrect
Reflect biases or errors in the fine-tuning dataset
Perform differently across specialties, populations, and languages

Use careful validation before any deployment.

Bias, Fairness, and Risk

Medical AI systems can produce unequal performance across demographic groups, diseases, languages, and healthcare settings.

Users should evaluate the model for:

Demographic bias
Socioeconomic bias
Geographic bias
Rare disease performance
Hallucination rate
Calibration and uncertainty quality
Robustness to ambiguous or adversarial prompts
Safety behavior in high-risk medical scenarios

Deployment Guidance

For any healthcare-adjacent deployment, recommended safeguards include:

Human clinician review
Retrieval from trusted medical sources
Clear uncertainty statements
Logging and audit trails
Refusal behavior for unsafe requests
Post-generation verification
Clinical governance review
Clear user-facing disclaimers
Exclusion from emergency or autonomous diagnosis workflows

Example Medical Disclaimer for Applications

This AI system is for educational and research assistance only. It does not provide medical advice, diagnosis, or treatment. Always consult a qualified healthcare professional for medical concerns. Do not use this system for emergencies or time-sensitive medical decisions.

Relationship to Base Model

This model is a supervised fine-tuned derivative of:

text
unsloth/gemma-4-E4B-it

Users must comply with the license and use restrictions of the base model, this fine-tuned model, and any applicable dataset licenses.

Benchmark

Table with columns: Tasks, Version, Filter, n-shot, Metric, Value
Tasks	Version	Filter	n-shot	Metric		Value
mmlu_pro_biology	3.1	custom-extract	5	exact_match	↑	0.75
mmlu_proX_en_biology	0.0	custom-extract	5	exact_match	↑	0.75

Table with columns: Tasks, Version, Filter, n-shot, Metric, Direction, Model, Score
Tasks	Version	Filter	n-shot	Metric	Direction	Model	Score
medqa_4options	Yaml	none	0	acc	↑	EpistemeAI/Reasoning-Medical0.1-E4B-sft	0.760
medqa_4options	Yaml	none	0

Benchmark Chart

MedQA Benchmark Comparison

No Affiliation Notice

This model is independently fine-tuned and released by EpistemeAI.

It is not affiliated with, endorsed by, sponsored by, or officially associated with Google, DeepMind, Unsloth, or any other base model provider unless explicitly stated.

No claim is made that this model is an official medical device, clinical decision support system, or certified healthcare product.

Citation

If you use this model, please cite:

bibtex
@misc{epistemeai_reasoning_medical_e4b_sft,
  title  = {Reasoning-Medical0.1-E4B-SFT},
  author = {EpistemeAI},
  year   = {2026},
  url    = {https://huggingface.co/EpistemeAI/Reasoning-Medical0.1-E4B-sft}
}

Responsible Use

This model should be used with caution, especially in healthcare contexts.
Medical reasoning models can be useful for education and research, but they must be evaluated carefully before deployment.

For clinical, regulatory, or patient-facing use, additional validation, expert review, and compliance assessment are required.

Uploaded finetuned model

Developed by: EpistemeAI
License: apache-2.0
Finetuned from model : unsloth/gemma-4-E4B-it

This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

EpistemeAI

Model Tree

Base

unsloth/gemma-4-E4B-it

Fine-tuned

this model

Input Modalities

Text

Image

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

Container

Explore FriendliAI today

Get started Talk to an engineer

README

License: apache-2.0

Model Details

Table with columns: Field, Description
Field	Description
Model name	`EpistemeAI/Reasoning-Medical0.1-E4B-sft`
Base model	`unsloth/gemma-4-E4B-it`
Training method	Supervised Fine-Tuning, SFT
Training data size	~100k medical reasoning examples
Primary language	English
Model type	Instruction-tuned causal language model
Domain	Medical reasoning, biomedical QA, clinical education
Intended users	Researchers, students, developers, clinicians using human review

Intended Use

This model is designed for:

Medical education and study support
Biomedical and clinical reasoning practice
Medical multiple-choice question reasoning
Literature review assistance
Research hypothesis exploration
Drafting clinician-reviewed explanations
Medical benchmark experimentation
AI safety and alignment research for healthcare models

This model should be used as a reasoning assistant, not as a final authority.

Out-of-Scope Use

Do not use this model for:

Autonomous medical diagnosis
Treatment decisions without clinician oversight
Emergency medical advice
Prescribing medication
Replacing a licensed clinician
Patient-specific medical decision-making without professional review
High-risk biological protocol design
Harmful biological, chemical, or virological assistance
Any use that violates applicable law, medical regulation, or safety policy

Medical Safety Notice

This model may produce incorrect, incomplete, outdated, or misleading medical information. Medical knowledge changes over time, and language models can hallucinate.

Users should:

Verify outputs against trusted medical references.
Use clinician review for any patient-facing or clinical workflow.
Avoid relying on the model for urgent or high-stakes decisions.
Treat the model as an educational and research-support tool only.

For medical concerns, consult a qualified healthcare professional.

Biological and Chemical Safety Notice

This model is not intended to assist with harmful biological, chemical, or dual-use activities.

The model should not be used to generate, optimize, troubleshoot, or operationalize:

Pathogen engineering
Toxin production
Chemical weapon synthesis
Biological weapon development
Evasion of biosafety controls
Protocols that increase virulence, transmissibility, resistance, or environmental persistence
Any unsafe wet-lab procedure outside appropriate institutional review and biosafety oversight

The intended safety boundary is medical education and biomedical reasoning, not actionable dangerous capability enablement.

Example Usage

Transformers Pipeline

python
from transformers import pipeline
import torch

model_id = "EpistemeAI/Reasoning-Medical0.1-E4B-sft"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": "A 65-year-old man has progressive shortness of breath, bibasilar crackles, and elevated BNP. Explain the likely diagnosis and reasoning."
    }
]

outputs = pipe(
    messages,
    max_new_tokens=1024,
    temperature=0.3,
    top_p=0.9,
)

print(outputs[0]["generated_text"])

Suggested Prompt Format

For best results, ask for structured reasoning and explicitly request uncertainty.

Example:

text
You are a medical reasoning assistant for education and research.

Question:
A patient presents with fever, neck stiffness, photophobia, and altered mental status. What is the most likely diagnosis?

Please provide:
1. Key clinical clues
2. Differential diagnosis
3. Most likely answer
4. Why alternatives are less likely
5. Safety caveats

Recommended Generation Settings

For factual medical reasoning, use lower temperature settings:

python
generation_config = {
    "max_new_tokens": 1024,
    "temperature": 0.2,
    "top_p": 0.9,
    "do_sample": True,
}

For benchmark-style multiple-choice evaluation, consider deterministic decoding:

python
generation_config = {
    "max_new_tokens": 512,
    "temperature": 0.0,
    "do_sample": False,
}

Training Summary

This model was fine-tuned using supervised fine-tuning on medical reasoning data.

The training objective was to improve:

Clinical reasoning structure
Biomedical explanation quality
Medical question-answering performance
Differential diagnosis reasoning
Medical exam-style reasoning
Safer, more cautious healthcare responses

The model was not trained to replace clinicians or provide autonomous medical advice.

Evaluation

Suggested evaluation benchmarks include:

MedQA
MedMCQA
PubMedQA
MMLU Clinical Knowledge
MMLU Medical Genetics
MMLU Anatomy
MMLU Professional Medicine
MMLU-Pro Biology
HealthBench-style rubric evaluation
Human clinician-reviewed evaluation

Evaluation results should be reported with:

Prompt format
Decoding settings
Number of few-shot examples
Exact model revision
Evaluation harness version
Random seed
Sample size

Limitations

This model may:

Hallucinate medical facts
Overstate confidence
Miss rare diagnoses
Fail to ask for missing clinical context
Produce outdated medical guidance
Confuse similar diseases, drugs, or mechanisms
Provide reasoning that sounds plausible but is incorrect
Reflect biases or errors in the fine-tuning dataset
Perform differently across specialties, populations, and languages

Use careful validation before any deployment.

Bias, Fairness, and Risk

Medical AI systems can produce unequal performance across demographic groups, diseases, languages, and healthcare settings.

Users should evaluate the model for:

Demographic bias
Socioeconomic bias
Geographic bias
Rare disease performance
Hallucination rate
Calibration and uncertainty quality
Robustness to ambiguous or adversarial prompts
Safety behavior in high-risk medical scenarios

Deployment Guidance

For any healthcare-adjacent deployment, recommended safeguards include:

Human clinician review
Retrieval from trusted medical sources
Clear uncertainty statements
Logging and audit trails
Refusal behavior for unsafe requests
Post-generation verification
Clinical governance review
Clear user-facing disclaimers
Exclusion from emergency or autonomous diagnosis workflows

Example Medical Disclaimer for Applications

Relationship to Base Model

This model is a supervised fine-tuned derivative of:

text
unsloth/gemma-4-E4B-it

Users must comply with the license and use restrictions of the base model, this fine-tuned model, and any applicable dataset licenses.

Benchmark

Table with columns: Tasks, Version, Filter, n-shot, Metric, Value
Tasks	Version	Filter	n-shot	Metric		Value
mmlu_pro_biology	3.1	custom-extract	5	exact_match	↑	0.75
mmlu_proX_en_biology	0.0	custom-extract	5	exact_match	↑	0.75

Table with columns: Tasks, Version, Filter, n-shot, Metric, Direction, Model, Score
Tasks	Version	Filter	n-shot	Metric	Direction	Model	Score
medqa_4options	Yaml	none	0	acc	↑	EpistemeAI/Reasoning-Medical0.1-E4B-sft	0.760
medqa_4options	Yaml	none	0

Benchmark Chart

MedQA Benchmark Comparison

No Affiliation Notice

This model is independently fine-tuned and released by EpistemeAI.

It is not affiliated with, endorsed by, sponsored by, or officially associated with Google, DeepMind, Unsloth, or any other base model provider unless explicitly stated.

No claim is made that this model is an official medical device, clinical decision support system, or certified healthcare product.

Citation

If you use this model, please cite:

bibtex
@misc{epistemeai_reasoning_medical_e4b_sft,
  title  = {Reasoning-Medical0.1-E4B-SFT},
  author = {EpistemeAI},
  year   = {2026},
  url    = {https://huggingface.co/EpistemeAI/Reasoning-Medical0.1-E4B-sft}
}

Responsible Use

This model should be used with caution, especially in healthcare contexts.
Medical reasoning models can be useful for education and research, but they must be evaluated carefully before deployment.

For clinical, regulatory, or patient-facing use, additional validation, expert review, and compliance assessment are required.

Uploaded finetuned model

Developed by: EpistemeAI
License: apache-2.0
Finetuned from model : unsloth/gemma-4-E4B-it

This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library.