Update README.md

README.md

@@ -15,190 +15,217 @@ tags:
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-
-## Config
-200 steps
+This models fine-tuning for function calling task, and communicate
+
+Training time: more than 1 hour
+Steps: 200
+
+### Usage:
+```
+import json
+from typing import Any, Dict, List
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+models = [
+    'beyoru/Neeru'
+]
+model_name = models[-1]
+model = AutoModelForCausalLM.from_pretrained(
+    model_name
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+import json
+from typing import List, Dict, Any
+
+SYSTEM_PROMPT = """You are a helpful assistant with access to the following functions. Use them if required
+
+You may call one or more functions to assist with the user query.
+
+Before calling a function, ensure that all required parameters are provided. If any required parameter is missing, ask the user for the missing information before proceeding.
+
+You are provided with function:
+
+{tools}
+
+For each function call, return a json object with function name and arguments:
+
+[{{"name": "<function-name>", "arguments": <args-json-object>}}]
+"""
+TOOL_PROMPT = "{tool_text}"
+
+get_weather_api = {
+    "type": "function",
+    "function": {
+        "name": "get_weather",
+        "description": "Get the current weather for a location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "str",
+                    "description": "The city and state",
+                },
+                "unit": {
+                    "type": "str",
+                    "enum": ["celsius", "fahrenheit"],
+                    "description": "The unit of temperature to return",
+                },
+            },
+            "required": ["location"],
+        },
+    },
+}
+
+
+get_search_api = {
+    "type": "function",
+    "function": {
+        "name": "get_search",
+        "description": "Search the web for a query",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "The search query"
+                }
+            },
+            "required": ["query"]
+        }
+    }
+}
+
+import json
+from typing import List, Dict, Any
+
+TOOL_PROMPT = "{tool_text}"
+
+get_weather_api = {
+    "type": "function",
+    "function": {
+        "name": "get_weather",
+        "description": "Get the current weather for a location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {"type": "string", "description": "The city and state"},
+                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "description": "The unit of temperature to return"},
+            },
+            "required": ["location"],
+        },
+    },
+}
+
+get_search_api = {
+    "type": "function",
+    "function": {
+        "name": "get_search",
+        "description": "Search the web for a query",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {"type": "string", "description": "The search query"}
+            },
+            "required": ["query"],
+        },
+    },
+}
+
+get_warehouse_api = {
+    "type": "function",
+    "function": {
+        "name": "get_warehouse_info",
+        "description": "Get warehouse information based on multiple parameters",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "warehouse_id": {
+                    "type": "string",
+                    "description": "Unique identifier of the warehouse"
+                },
+                "location": {
+                    "type": "string",
+                    "description": "Location of the warehouse"
+                },
+                "status": {
+                    "type": "string",
+                    "enum": ["active", "inactive", "under maintenance"],
+                    "description": "Operational status of the warehouse"
+                },
+                "capacity": {
+                    "type": "integer",
+                    "description": "Total storage capacity of the warehouse"
+                },
+                "current_stock": {
+                    "type": "integer",
+                    "description": "Current stock available in the warehouse"
+                },
+                "manager": {
+                    "type": "string",
+                    "description": "Name of the warehouse manager"
+                },
+                "contact": {
+                    "type": "string",
+                    "description": "Contact details of the warehouse"
+                },
+                "operating_hours": {
+                    "type": "string",
+                    "description": "Operating hours of the warehouse"
+                }
+            },
+            "required": ["warehouse_id", "location", "status"],
+        },
+    },
+}
+
+
+
+TOOLS = [get_search_api, get_weather_api, get_warehouse_api]
+
+def convert_tools(tools: List[Dict[str, Any]]) -> str:
+    return json.dumps(tools, indent=2)
+
+
+def format_prompt(tools: List[Dict[str, Any]]) -> str:
+    tool_text = convert_tools(tools)
+    return f"{SYSTEM_PROMPT.format(tools=tool_text)}"
+
+
+system_prompt = format_prompt(TOOLS)
+
+# print(system_prompt)
+
+"""
+Simple loop for conversations
+
+"""
+
+while True:
+    user_input = input("User: ")
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_input},
+    ]
+
+    inputs = tokenizer.apply_chat_template(
+        messages[-10:], add_generation_prompt=True, return_tensors="pt" 
+    ) # You can added more history conversations here
+
+    outputs = model.generate(
+        inputs,
+        max_new_tokens=512,
+        top_p=0.95,
+        temperature=0.7,
+    ) # Suggest parameters
+
+    response_text = tokenizer.decode(outputs[0][len(inputs[0]) :], skip_special_tokens=True)
+
+    print("Assistant:", response_text)
+```
+
+### Config
+```
+# Update
+```