Update README.md

README.md

@@ -87,25 +87,30 @@ outputs = model.generate(**input_ids)
 print(tokenizer.decode(outputs[0]))
 ```
 
-### Running the model with llama.cpp
+### Running the model with mlx-lm on an Apple computer
 
-We converted Dragoman PT adapter into the [GGUF format](https://huggingface.co/lang-uk/dragoman/blob/main/ggml-adapter-model.bin).
 
-You can download the [Mistral-7B-v0.1 base model in the GGUF format](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF) (e.g. mistral-7b-v0.1.Q4_K_M.gguf)
-and use `ggml-adapter-model.bin` from this repository like this:
+We merged Dragoman PT adapter into the base model and uploaded the quantized version of the model into https://huggingface.co/lang-uk/dragoman-4bit.
+
+You can run the model using [mlx-lm](https://pypi.org/project/mlx-lm/).
+
 
 ```
-./main -ngl 32 -m mistral-7b-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0 --repeat_penalty 1.1 -n -1 -p "[INST] who holds this neighborhood? [/INST]" --lora ./ggml-adapter-model.bin
+python -m mlx_lm.generate --model lang-uk/dragoman-4bit --prompt '[INST] who holds this neighborhood? [/INST]' --temp 0 --max-tokens 100
 ```
 
-### Running the model with mlx-lm
+MLX is a recommended way of using the language model on an Apple computer with an M1 chip and newer.
 
-We merged Dragoman PT adapter into the base model and uploaded the quantized version of the model into https://huggingface.co/lang-uk/dragoman-4bit.
 
-You can run the model using [mlx-lm](https://pypi.org/project/mlx-lm/):
+### Running the model with llama.cpp
+
+We converted Dragoman PT adapter into the [GGUF format](https://huggingface.co/lang-uk/dragoman/blob/main/ggml-adapter-model.bin).
+
+You can download the [Mistral-7B-v0.1 base model in the GGUF format](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF) (e.g. mistral-7b-v0.1.Q4_K_M.gguf)
+and use `ggml-adapter-model.bin` from this repository like this:
 
 ```
-python -m mlx_lm.generate --model lang-uk/dragoman-4bit --prompt '[INST] who holds this neighborhood? [/INST]' --temp 0 --max-tokens 100
+./main -ngl 32 -m mistral-7b-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0 --repeat_penalty 1.1 -n -1 -p "[INST] who holds this neighborhood? [/INST]" --lora ./ggml-adapter-model.bin
 ```
 
 ### Training Dataset and Resources