Update README.md

README.md

@@ -24,8 +24,7 @@ quantized_by: Elkhayyat
 
 
 
-# CodeLlama 7B - GGUF
-- Model creator: [Meta](https://huggingface.co/meta-llama)
+# Llama2-Med - GGUF
 - Original model: [meta](meta-llama/Llama-2-7b-chat-hf)
 
 <!-- description start -->
@@ -107,7 +106,7 @@ The following clients/libraries will automatically download models for you, prov
 
 ### In `text-generation-webui`
 
-Under Download Model, you can enter the model repo: Elkhayyat17/llama2-Med-gguf and below it, a specific filename to download, such as: codellama-7b.q4_K_M.gguf.
+Under Download Model, you can enter the model repo: Elkhayyat17/llama2-Med-gguf and below it, a specific filename to download, such as: ggml-model-Q4_K_M.gguf
 
 Then click Download.
 
@@ -158,7 +157,7 @@ Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running
 Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 
 ```shell
-./main -ngl 32 -m codellama-7b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
+./main -ngl 32 -m c ggml-model-Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
 ```
 
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
@@ -231,7 +230,7 @@ from transformers import AutoTokenizer
 import transformers
 import torch
 
-model = "codellama/CodeLlama-7b-hf"
+model = "meta-llama/Llama-2-7b-chat-hf"
 
 tokenizer = AutoTokenizer.from_pretrained(model)
 pipeline = transformers.pipeline(
@@ -256,25 +255,20 @@ for seq in sequences:
 ```
 
 ## Model Details
-*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
 
 **Model Developers** Meta
 
 **Variations** 
 ## Training Data
 
-All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
 
 ## Evaluation Results
 
-See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
 
 
 ## Ethical Considerations and Limitations
 
-Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
 
-Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide).
 
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