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README.md

@@ -17,63 +17,6 @@ library_name: transformers
 This model was converted to GGUF format from [`nvidia/OpenMath2-Llama3.1-8B`](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B) for more details on the model.
 
----
-Model details:
--
-OpenMath2-Llama3.1-8B is obtained by finetuning Llama3.1-8B-Base with OpenMathInstruct-2.
-
-The model outperforms Llama3.1-8B-Instruct on all the popular math benchmarks we evaluate on, especially on MATH by 15.9%. 
-
-How to use the models?
--
-Our models are trained with the same "chat format" as Llama3.1-instruct models (same system/user/assistant tokens). Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain.
-
-We recommend using instructions in our repo to run inference with these models, but here is an example of how to do it through transformers api:
-
-import transformers
-import torch
-
-model_id = "nvidia/OpenMath2-Llama3.1-8B"
-
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model_id,
-    model_kwargs={"torch_dtype": torch.bfloat16},
-    device_map="auto",
-)
-
-messages = [
-    {
-        "role": "user", 
-        "content": "Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}.\n\n" + 
-        "What is the minimum value of $a^2+6a-7$?"},
-]
-
-outputs = pipeline(
-    messages,
-    max_new_tokens=4096,
-)
-print(outputs[0]["generated_text"][-1]['content'])
-
-Reproducing our results
--
-We provide all instructions to fully reproduce our results.
-Citation
-
-If you find our work useful, please consider citing us!
-
-@article{toshniwal2024openmath2,
-  title   = {OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data},
-  author  = {Shubham Toshniwal and Wei Du and Ivan Moshkov and  Branislav Kisacanin and Alexan Ayrapetyan and Igor Gitman},
-  year    = {2024},
-  journal = {arXiv preprint arXiv:2410.01560}
-}
-
-Terms of use
--
-By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the license, acceptable use policy and Meta’s privacy policy
-
----
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)