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[!TIP] This is an experimental model, so it might not perform well for some prompts and may be sensitive to hyper parameters. It is mainly trained to enhance reasoning capabilities.
| Metric | Value |
|---|---|
| Avg. | |
| IFEval (0-Shot) | |
| BBH (3-Shot) | |
| MATH Lvl 5 (4-Shot) | |
| GPQA (0-shot) | |
| MuSR (0-shot) | |
| MMLU-PRO (5-shot) |
ChatML prompt template:1<|begin_of_text|><|start_header_id|>system<|end_header_id|>
2You are Llama Reason Blend, a helpful AI assistant.<|eot_id|>
3<|start_header_id|>user<|end_header_id|>
4Hello Llama Reason Blend, what can you do for me?<|eot_id|>
5<|start_header_id|>assistant<|end_header_id|>1
2# Use a pipeline as a high-level helper
3
4from transformers import pipeline
5
6messages = [
7 {"role": "user", "content": "Who are you?"},
8]
9pipe = pipeline("text-generation", model="khulaifi95/Llama-3.1-8B-Reason-Blend-888k")
10pipe(messages)
11
12
13# Load model directly
14
15from transformers import AutoTokenizer, AutoModelForCausalLM
16
17tokenizer = AutoTokenizer.from_pretrained("khulaifi95/Llama-3.1-8B-Reason-Blend-888k")
18model = AutoModelForCausalLM.from_pretrained("khulaifi95/Llama-3.1-8B-Reason-Blend-888k")