Updated some of the docs

docs/ContributingGuide.md

@@ -33,11 +33,11 @@ When adding the feature, please take care of the namespace and the naming conven
 
 ## Find the problem and fix the BUG
 
-If the issue is related to the LLM internal behaviors, such as endless generating the response, the best way to find the problem is to do comparison test between llama.cpp and LLamaSharp.
+If the issue is related to the LLM internal behaviour, such as endless generating the response, the best way to find the problem is to do comparison test between llama.cpp and LLamaSharp.
 
 You could use exactly the same prompt, the same model and the same parameters to run the inference in llama.cpp and LLamaSharp respectively to see if it's really a problem caused by the implementation in LLamaSharp.
 
-If the experiment showed that it worked well in llama.cpp but didn't in LLamaSharp, a the search for the problem could be started. While the reason of the problem could be various, the best way I think is to add log-print in the code of llama.cpp and use it in LLamaSharp after compilation. Thus, when running LLamaSharp, you could see what happened in the native library.
+If the experiment showed that it worked well in llama.cpp but didn't in LLamaSharp, a search for the problem could be started. While the reason of the problem could be various, the best way I think is to add log-print in the code of llama.cpp and use it in LLamaSharp after compilation. Thus, when running LLamaSharp, you could see what happened in the native library.
 
 After finding out the reason, a painful but happy process comes. When working on the BUG fix, there's only one rule to follow, that is keeping the examples working well. If the modification fixed the BUG but impact on other functions, it would not be a good fix.
 

docs/GetStarted.md

@@ -54,8 +54,16 @@ using LLama;
 string modelPath = "<Your model path>" // change it to your own model path
 var prompt = "Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\r\n\r\nUser: Hello, Bob.\r\nBob: Hello. How may I help you today?\r\nUser: Please tell me the largest city in Europe.\r\nBob: Sure. The largest city in Europe is Moscow, the capital of Russia.\r\nUser:"; // use the "chat-with-bob" prompt here.
 
+// Load model
+var parameters = new ModelParams(modelPath)
+{
+    ContextSize = 1024
+};
+using var model = LLamaWeights.LoadFromFile(parameters);
+
 // Initialize a chat session
-var ex = new InteractiveExecutor(new LLamaModel(new ModelParams(modelPath, contextSize: 1024, seed: 1337, gpuLayerCount: 5)));
+using var context = model.CreateContext(parameters);
+var ex = new InteractiveExecutor(context);
 ChatSession session = new ChatSession(ex);
 
 // show the prompt

docs/Tricks.md

@@ -1,11 +1,11 @@
 # Tricks for FAQ
 
-Sometimes, your application with LLM and LLamaSharp may have strange behaviors. Before opening an issue to report the BUG, the following tricks may worth a try.
+Sometimes, your application with LLM and LLamaSharp may have strange behaviours. Before opening an issue to report the BUG, the following tricks may worth a try.
 
 
 ## Carefully set the anti-prompts
 
-Anti-prompt can also be called as "Stop-keyword", which decides when to stop the response generation. Under interactive mode, the maximum tokens count is always not set, which makes the LLM generates responses infinitively. Therefore, setting anti-prompt correctly helps a lot to avoid the strange behaviors. For example, the prompt file `chat-with-bob.txt` has the following content:
+Anti-prompt can also be called as "Stop-keyword", which decides when to stop the response generation. Under interactive mode, the maximum tokens count is always not set, which makes the LLM generates responses infinitively. Therefore, setting anti-prompt correctly helps a lot to avoid the strange behaviours. For example, the prompt file `chat-with-bob.txt` has the following content:
 
 ```
 Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
@@ -19,7 +19,7 @@ User:
 
 Therefore, the anti-prompt should be set as "User:". If the last line of the prompt is removed, LLM will automatically generate a question (user) and a response (bob) for one time when running the chat session. Therefore, the antiprompt is suggested to be appended to the prompt when starting a chat session.
 
-What if an extra line is appended? The string "User:" in the prompt will be followed with a char "\n". Thus when running the model, the automatic generation of a pair of question and response may appear because the anti-prompt is "User:" but the last token is "User:\n". As for whether it will appear, it's an undefined behavior, which depends on the implementation inside the `LLamaExecutor`. Anyway, since it may leads to unexpected behaviors, it's recommended to trim your prompt or carefully keep consistent with your anti-prompt.
+What if an extra line is appended? The string "User:" in the prompt will be followed with a char "\n". Thus when running the model, the automatic generation of a pair of question and response may appear because the anti-prompt is "User:" but the last token is "User:\n". As for whether it will appear, it's an undefined behaviour, which depends on the implementation inside the `LLamaExecutor`. Anyway, since it may leads to unexpected behaviors, it's recommended to trim your prompt or carefully keep consistent with your anti-prompt.
 
 ## Pay attention to the length of prompt
 
@@ -37,7 +37,7 @@ If your chat bot has bad performance, trying different executor will possibly ma
 
 ## Choose models weight depending on you task
 
-The differences between modes may lead to much different behaviors under the same task. For example, if you're building a chat bot with non-English, a fine-tuned model specially for the language you want to use will have huge effect on the performance.
+The differences between modes may lead to much different behaviours under the same task. For example, if you're building a chat bot with non-English, a fine-tuned model specially for the language you want to use will have huge effect on the performance.
 
 ## Set the layer count you want to offload to GPU