Update README.md

README.md

@@ -4,21 +4,25 @@ base_model:
 tags:
 - mergekit
 - merge
-
+- meta-math/MetaMath-Mistral-7B
+- Mixture of Experts
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
 ---
-# merge
-
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+This is MetaMath-Mistral-2x7B Mixture of Experts (MOE) model created using [mergekit](https://github.com/cg123/mergekit) for experimental and learning purpose of MOE.
 
 ## Merge Details
 ### Merge Method
 
-This model was merged using the SLERP merge method.
+This model was merged using the SLERP merge method using [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B) as the base model.
 
 ### Models Merged
 
 The following models were included in the merge:
-* [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B)
+* [meta-math/MetaMath-Mistral-7B](https://huggingface.co/meta-math/MetaMath-Mistral-7B) x 2
 
 ### Configuration
 
@@ -44,3 +48,76 @@ parameters:
 dtype: bfloat16
 
 ```
+
+## Inference Code
+```python
+
+## install dependencies
+## !pip install -q -U git+https://github.com/huggingface/transformers.git
+## !pip install -q -U git+https://github.com/huggingface/accelerate.git
+## !pip install -q -U sentencepiece
+
+## load model
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+model_name = "harshitv804/MetaMath-Mistral-2x7B"
+
+# load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name, 
+    trust_remote_code=True
+)
+
+tokenizer.pad_token = tokenizer.eos_token
+
+## inference
+
+query = "Maximoff's monthly bill is $60 per month. His monthly bill increased by thirty percent when he started working at home. How much is his total monthly bill working from home?"
+prompt =f"""
+Below is an instruction that describes a task. Write a response that appropriately completes the request.\n
+### Instruction:\n
+{query}\n
+### Response: Let's think step by step.
+"""
+
+# tokenize the input string
+inputs = tokenizer(
+    prompt, 
+    return_tensors="pt", 
+    return_attention_mask=False
+)
+
+# generate text using the model
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+outputs = model.generate(**inputs, max_length=2048, streamer=streamer)
+
+# decode and print the output
+text = tokenizer.batch_decode(outputs)[0]
+
+```
+
+## Citation
+
+```bibtex
+@article{yu2023metamath,
+  title={MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models},
+  author={Yu, Longhui and Jiang, Weisen and Shi, Han and Yu, Jincheng and Liu, Zhengying and Zhang, Yu and Kwok, James T and Li, Zhenguo and Weller, Adrian and Liu, Weiyang},
+  journal={arXiv preprint arXiv:2309.12284},
+  year={2023}
+}
+```
+
+```bibtex
+@article{jiang2023mistral,
+  title={Mistral 7B},
+  author={Jiang, Albert Q and Sablayrolles, Alexandre and Mensch, Arthur and Bamford, Chris and Chaplot, Devendra Singh and Casas, Diego de las and Bressand, Florian and Lengyel, Gianna and Lample, Guillaume and Saulnier, Lucile and others},
+  journal={arXiv preprint arXiv:2310.06825},
+  year={2023}
+}
+```