metadata
license: other
language:
- en
base_model:
- meta-llama/Meta-Llama-3.1-8B-Instruct
pipeline_tag: text-generation
inference: true
library_name: transformers
datasets:
- mlabonne/orca-agentinstruct-1M-v1-cleaned
- HuggingFaceTB/smoltalk
- Magpie-Align/Magpie-Qwen2.5-Pro-300K-Filtered
- Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese
- O1-OPEN/OpenO1-SFT
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.
khulaifi95/Llama-3.1-8B-Reason-Blend-888k
🏆 Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | |
| IFEval (0-Shot) | |
| BBH (3-Shot) | |
| MATH Lvl 5 (4-Shot) | |
| GPQA (0-shot) | |
| MuSR (0-shot) | |
| MMLU-PRO (5-shot) |
Prompt Template
This model uses ChatML prompt template:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are Llama Reason Blend, a helpful AI assistant.<|eot_id|>
<|start_header_id|>user<|end_header_id|>
Hello Llama Reason Blend, what can you do for me?<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
How to use
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="khulaifi95/Llama-3.1-8B-Reason-Blend-888k")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("khulaifi95/Llama-3.1-8B-Reason-Blend-888k")
model = AutoModelForCausalLM.from_pretrained("khulaifi95/Llama-3.1-8B-Reason-Blend-888k")
Ethical Considerations
As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.