Evalora-aa2faf06-b545-447c-a91f-49aa1b341ef1

Evalora LoRA Model

Experiment Information

• Upload ID: aa2faf06-b545-447c-a91f-49aa1b341ef1
• Base Model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
• Dataset: my-run
• Created At: 2026-06-04T03:49:33.368017

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Metrics

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Training Configuration

• Epochs: 3
• Learning Rate: None
• LoRA Rank: 16
• LoRA Alpha: 16

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"
)

model = PeftModel.from_pretrained(
    base_model,
    "abooodjbr/Evalora-aa2faf06-b545-447c-a91f-49aa1b341ef1"
)

tokenizer = AutoTokenizer.from_pretrained("unsloth/mistral-7b-instruct-v0.2-bnb-4bit")

# Replace the placeholders below with your actual input
user_content = (
    "Provide both a score and a rationale by evaluating the student's answer strictly within the mark scheme range, "
    "grading based on how well it meets the question's requirements by comparing the student answer to the reference answer.\n"
    "Question: <YOUR_QUESTION>\n"
    "Reference Answer: <YOUR_REFERENCE_ANSWER>\n"
    "Student Answer: <YOUR_STUDENT_ANSWER>\n"
    "Mark Scheme: <YOUR_MARK_SCHEME>"  # e.g. {'1': 'Mentions X', '2': 'Explains Y'}
)

messages = [
    {
        "role": "system",
        "content": "You are a grading assistant. Evaluate student answers based on the mark scheme. Respond only in JSON format with keys 'score' (int) and 'rationale' (string)."
    },
    {
        "role": "user",
        "content": user_content
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

generated_ids = model.generate(
    **inputs,
    max_new_tokens=128,
    temperature=0.2,
    top_k=5,
    do_sample=False,
)[0]

new_generated_ids = generated_ids[inputs["input_ids"].shape[1]:]
generated_text = tokenizer.decode(new_generated_ids, skip_special_tokens=True)
print(generated_text)
# Output example: {"score": 4, "rationale": "The student correctly identified..."}