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lukasdrews/
Gemma-4-E2B-IT-SFT-RLVR-Medical-GGUF

Name: Gemma-4-E2B-IT-SFT-RLVR-Medical-GGUF
Availability: InStock

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Gemma-4-E2B-IT-SFT-RLVR-Medical

Gemma-4-E2B-it fine-tuned on PubMedQA using SFT and RLVR.
Also check out the training code on GitHub.

Setup

python

# !pip install llama-cpp-python
from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="lukasdrews/Gemma-4-E2B-IT-SFT-RLVR-Medical-GGUF",
    filename="gemma-4-E2B-it-sft-rlvr-medical-Q4_K_M.gguf",
    verbose=False,
)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Do GEC produce and bear factor H under complement attack?"}
        ]
    },
]

outputs = llm.create_chat_completion(messages, max_tokens=1024)
print(outputs["choices"][0]["message"]["content"])

Benchmarks

Model	Quantization	PubMedQA (In-Domain)	MedQA-USMLE (Zero-Shot Transfer)
Gemma-4-E2B-it (base model)	-	58.10 %	29.54 %
Gemma-4-E2B-it + SFT + RLVR	-	73.10 %	43.05 %
Gemma-4-E2B-it + SFT + RLVR	Q8_0	72.40 %	43.00 %
Gemma-4-E2B-it + SFT + RLVR	Q6_K	72.10 %	42.18 %
Gemma-4-E2B-it + SFT + RLVR	Q5_K_M	72.00 %	38.88 %
Gemma-4-E2B-it + SFT + RLVR	Q4_K_M	71.80 %	38.88 %