Create README.md

README.md

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+---
+library_name: transformers
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- trl
+- llama
+language:
+- en
+base_model: hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode
+---
+
+# QuantFactory/Meta-Llama-3-8B-Instruct-function-calling-json-mode-GGUF
+This is quantized version of [hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode](https://huggingface.co/hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode) created using llama.cpp
+
+## Model Description
+
+This model was fine-tuned on meta-llama/Meta-Llama-3-8B-Instruct for function calling and json mode.
+
+## Usage
+### JSON Mode
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant, answer in JSON with key \"message\""},
+    {"role": "user", "content": "Who are you?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+# >> {"message": "I am a helpful assistant, with access to a vast amount of information. I can help you with tasks such as answering questions, providing definitions, translating text, and more. Feel free to ask me anything!"}
+```
+
+### Function Calling
+Function calling requires two step inferences, below is the example:
+
+## Step 1:
+
+```python
+functions_metadata = [
+    {
+      "type": "function",
+      "function": {
+        "name": "get_temperature",
+        "description": "get temperature of a city",
+        "parameters": {
+          "type": "object",
+          "properties": {
+            "city": {
+              "type": "string",
+              "description": "name"
+            }
+          },
+          "required": [
+            "city"
+          ]
+        }
+      }
+    }
+]
+
+messages = [
+    { "role": "system", "content": f"""You are a helpful assistant with access to the following functions: \n {str(functions_metadata)}\n\nTo use these functions respond with:\n<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", "arg_1": "value_1", ... }} }} </functioncall>\n\nEdge cases you must handle:\n - If there are no functions that match the user request, you will respond politely that you cannot help."""},
+    { "role": "user", "content": "What is the temperature in Tokyo right now?"}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+# >> <functioncall> {"name": "get_temperature", "arguments": '{"city": "Tokyo"}'} </functioncall>"""}
+```
+## Step 2:
+
+```python
+messages = [
+    { "role": "system", "content": f"""You are a helpful assistant with access to the following functions: \n {str(functions_metadata)}\n\nTo use these functions respond with:\n<functioncall> {{ "name": "function_name", "arguments": {{ "arg_1": "value_1", "arg_1": "value_1", ... }} }} </functioncall>\n\nEdge cases you must handle:\n - If there are no functions that match the user request, you will respond politely that you cannot help."""},
+    { "role": "user", "content": "What is the temperature in Tokyo right now?"},
+    # You will get the previous prediction, extract it will the tag <functioncall>
+    # execute the function and append it to the messages like below:
+    { "role": "assistant", "content": """<functioncall> {"name": "get_temperature", "arguments": '{"city": "Tokyo"}'} </functioncall>"""},    
+    { "role": "user", "content": """<function_response> {"temperature":30 C} </function_response>"""}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=True))
+# >> The current temperature in Tokyo is 30 degrees Celsius.
+```
+
+# Uploaded  model
+
+- **Developed by:** hiieu
+
+This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)