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ValiantLabs
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Model Details
Model Provider
ValiantLabs
Model Tree
google/gemma-4-12B-it
Input Modalities
Output Modalities
Supported Functionality
Guardpoint delivers structured medical responses using the gemma-4-12B-it prompt format.
Guardpoint is a reasoning finetune; we recommend enable_thinking=True for all chats.
Example inference script to get started:
python
from transformers import AutoProcessor, AutoModelForCausalLM MODEL_ID = "ValiantLabs/gemma-4-12B-it-Guardpoint" # Load modelprocessor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForCausalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto") # Prepare the model inputprompt = "A 60-year-old undergoes a Total Knee Arthroplasty (TKA). Post-operatively, they complain of a clunking sensation and instability when descending stairs. On exam, they have excessive posterior translation of the tibia at 90 degrees of flexion. The TKA used a Cruciate Retaining (CR) implant. Diagnosis is PCL incompetence or rupture. Explain why a CR implant relies on a functional PCL for femoral rollback and how converting to a Posterior Stabilized (PS) implant resolves this biomechanical failure."#prompt = "I have that tube in my chest for dialysis while my arm heals. The dressing came off in the shower and the tube got tugged a bit. It didn't come out, but now there's this red cuff thing showing that used to be inside the skin. It’s sticking out about an inch. Can I just push it back in and tape it?"#prompt = "In the workup of a tumor of unknown primary, a biopsy shows a poorly differentiated carcinoma. The IHC profile is: CK7+, CK20+, CDX2+, TTF-1 negative, PAX8 negative. Based on this cytokeratin and transcription factor profile, where is the most likely primary site of the malignancy?"#prompt = "I have bad arthritis in my lower back and hips. I saw a chiropractor who said my 'pelvis is twisted' and wants to do high-velocity adjustments. My rheumatologist said absolutely not because of my 'osteophytes'. Who is right? I just want to walk without stiffness." messages = [ {"role": "user", "content": prompt},] # Process inputtext = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)inputs = processor(text=text, return_tensors="pt").to(model.device)input_len = inputs["input_ids"].shape[-1] # Generate outputoutputs = model.generate(**inputs, max_new_tokens=40000)response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) # Parse outputprocessor.parse_response(response)print(response)DISCLAIMER: Guardpoint is a medical reasoning finetune that is subject to the strengths and weaknesses of LLMs. A conversation with an LLM is not a substitute for a professional medical examination. Utilize Guardpoint responsibly.
Guardpoint is created by Valiant Labs.
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from transformers import AutoProcessor, AutoModelForCausalLM MODEL_ID = "ValiantLabs/gemma-4-12B-it-Guardpoint" # Load modelprocessor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForCausalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto") # Prepare the model inputprompt = "A 60-year-old undergoes a Total Knee Arthroplasty (TKA). Post-operatively, they complain of a clunking sensation and instability when descending stairs. On exam, they have excessive posterior translation of the tibia at 90 degrees of flexion. The TKA used a Cruciate Retaining (CR) implant. Diagnosis is PCL incompetence or rupture. Explain why a CR implant relies on a functional PCL for femoral rollback and how converting to a Posterior Stabilized (PS) implant resolves this biomechanical failure."#prompt = "I have that tube in my chest for dialysis while my arm heals. The dressing came off in the shower and the tube got tugged a bit. It didn't come out, but now there's this red cuff thing showing that used to be inside the skin. It’s sticking out about an inch. Can I just push it back in and tape it?"#prompt = "In the workup of a tumor of unknown primary, a biopsy shows a poorly differentiated carcinoma. The IHC profile is: CK7+, CK20+, CDX2+, TTF-1 negative, PAX8 negative. Based on this cytokeratin and transcription factor profile, where is the most likely primary site of the malignancy?"#prompt = "I have bad arthritis in my lower back and hips. I saw a chiropractor who said my 'pelvis is twisted' and wants to do high-velocity adjustments. My rheumatologist said absolutely not because of my 'osteophytes'. Who is right? I just want to walk without stiffness." messages = [ {"role": "user", "content": prompt},] # Process inputtext = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)inputs = processor(text=text, return_tensors="pt").to(model.device)input_len = inputs["input_ids"].shape[-1] # Generate outputoutputs = model.generate(**inputs, max_new_tokens=40000)response = processor.decode(outputs[0][input_len:], skip_special_tokens=False) # Parse outputprocessor.parse_response(response)print(response)