Update README.md

README.md

@@ -25,7 +25,7 @@ Phi-3 Mini 4k Instruct model finetuned on math datasets.
 
 Use the code below to get started with the model.
 
-```
+```python
 # Load model directly
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
@@ -38,7 +38,7 @@ model = AutoModelForCausalLM.from_pretrained("jrc/phi3-mini-math", trust_remote_
 Phi3 was trained using [torchtune]() and the training script + config file are located in this repository.
 
 CMD:
-```
+```bash
 tune run lora_finetune_distributed.py --config mini_lora.yaml 
 ```
 
@@ -57,7 +57,7 @@ tune run lora_finetune_distributed.py --config mini_lora.yaml
 The finetuned model is evaluated on [minerva-math](https://research.google/blog/minerva-solving-quantitative-reasoning-problems-with-language-models/) using [EleutherAI Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) through torchtune.
 
 CMD: 
-```
+```bash
 tune run eleuther_eval --config eleuther_evaluation \
           checkpoint.checkpoint_dir=./lora-phi3-math \
           tasks=["minerva_math"] \
@@ -65,7 +65,7 @@ tune run eleuther_eval --config eleuther_evaluation \
 ```
 
 RESULTS:
-```
+
 |               Tasks                |Version|Filter|n-shot|  Metric   |Value |   |Stderr|
 |------------------------------------|-------|------|-----:|-----------|-----:|---|-----:|
 |minerva_math                        |N/A    |none  |     4|exact_match|0.1670|±  |0.0051|
@@ -76,7 +76,7 @@ RESULTS:
 | - minerva_math_num_theory          |      1|none  |     4|exact_match|0.1148|±  |0.0137|
 | - minerva_math_prealgebra          |      1|none  |     4|exact_match|0.3077|±  |0.0156|
 | - minerva_math_precalc             |      1|none  |     4|exact_match|0.0623|±  |0.0104|
-```
+
 
 
 ## Technical Specifications [optional]