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REAME.md

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+# Quantize Llama 2 models using GGUF and llama.cpp
+> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)
+
+
+## Usage
+
+* `MODEL_ID`: The ID of the model to quantize (e.g., `mlabonne/EvolCodeLlama-7b`).
+* `QUANTIZATION_METHOD`: The quantization method to use.
+
+## Quantization methods
+
+The names of the quantization methods follow the naming convention: "q" + the number of bits + the variant used (detailed below). Here is a list of all the possible quant methods and their corresponding use cases, based on model cards made by [TheBloke](https://huggingface.co/TheBloke/):
+
+* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
+* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
+* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
+* `q3_k_s`: Uses Q3_K for all tensors
+* `q4_0`: Original quant method, 4-bit.
+* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
+* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
+* `q4_k_s`: Uses Q4_K for all tensors
+* `q5_0`: Higher accuracy, higher resource usage and slower inference.
+* `q5_1`: Even higher accuracy, resource usage and slower inference.
+* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
+* `q5_k_s`:  Uses Q5_K for all tensors
+* `q6_k`: Uses Q8_K for all tensors
+* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
+
+As a rule of thumb, **I recommend using Q5_K_M** as it preserves most of the model's performance. Alternatively, you can use Q4_K_M if you want to save some memory. In general, K_M versions are better than K_S versions. I cannot recommend Q2_K or Q3_* versions, as they drastically decrease model performance.
+
 
 ## Source Code