Description
This LLM model is trained to perform Q&A, Summarization, RAG, and Chat.
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
medical_llm = MedicalLLM.pretrained("jsl_meds_q4_v2", "en", "clinical/models")\
.setInputCols("document")\
.setOutputCol("completions")\
.setBatchSize(1)\
.setNPredict(100)\
.setUseChatTemplate(True)\
.setTemperature(0)
pipeline = Pipeline(
stages = [
document_assembler,
medical_llm
])
prompt = """
### Question:
who you are, describe yourself
"""
data = spark.createDataFrame([[prompt]]).toDF("text")
results = pipeline.fit(data).transform(data)
results.select("completions").show(truncate=False)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val medical_llm = MedicalLLM.pretrained("jsl_meds_q4_v2", "en", "clinical/models")
.setInputCols("document")
.setOutputCol("completions")
.setBatchSize(1)
.setNPredict(100)
.setUseChatTemplate(True)
.setTemperature(0)
val pipeline = new Pipeline().setStages(Array(
document_assembler,
medical_llm
))
val prompt = """
### Question:
who you are, describe yourself
"""
val data = Seq(prompt).toDF("text")
val results = pipeline.fit(data).transform(data)
results.select("completions").show(truncate=False)
Results
Hello! I am JSL Medical LLM, an artificial intelligence language model specialized in medical knowledge. I am here to assist you with any medical inquiries, provide information on health conditions, and help you understand medical terminology. Please feel free to ask me any questions related to health and medicine.
Model Information
| Model Name: | jsl_meds_q4_v2 |
| Compatibility: | Healthcare NLP 5.5.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 2.4 GB |
Benchmarking
We have generated a total of 400 questions, 100 from each category. These questions were labeled and reviewed by 3 physician annotators. % indicates the preference rate.
Please see the more benchmark information here.
## Overall
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.24 | 0.25 | 0.38 |
| GPT4o | 0.19 | 0.26 | 0.27 |
| Neutral | 0.43 | 0.36 | 0.18 |
| None | 0.14 | 0.13 | 0.17 |
| Total | 1.00 | 1.00 | 1.00 |
## Summary
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.47 | 0.48 | 0.42 |
| GPT4o | 0.25 | 0.25 | 0.25 |
| Neutral | 0.22 | 0.22 | 0.25 |
| None | 0.07 | 0.05 | 0.08 |
| Total | 1.00 | 1.00 | 1.00 |
## QA
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.35 | 0.36 | 0.42 |
| GPT4o | 0.24 | 0.24 | 0.29 |
| Neutral | 0.33 | 0.33 | 0.18 |
| None | 0.09 | 0.07 | 0.11 |
| Total | 1.00 | 1.00 | 1.00 |
## BioMedical
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.33 | 0.24 | 0.57 |
| GPT4o | 0.12 | 0.08 | 0.16 |
| Neutral | 0.45 | 0.57 | 0.16 |
| None | 0.10 | 0.10 | 0.10 |
| Total | 1.00 | 1.00 | 1.00 |
## OpenEnded
| Model | Factuality % | Clinical Relevancy % | Conciseness % |
|------------|--------------|----------------------|---------------|
| JSL-MedS | 0.35 | 0.30 | 0.39 |
| GPT4o | 0.30 | 0.33 | 0.41 |
| Neutral | 0.19 | 0.20 | 0.02 |
| None | 0.17 | 0.17 | 0.19 |
| Total | 1.00 | 1.00 | 1.00 |
PREVIOUSJSL_MedS (LLM - q4)