Update README.md

README.md

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----
-library_name: transformers
-tags:
-- trasformer
-- AIAgent
-- FunctionCallAgent
-- Agenttool
-- Agent
-- Agentcall
-license: apache-2.0
-datasets:
-- Salesforce/xlam-function-calling-60k
-language:
-- en
-metrics:
-- accuracy
 base_model:
 - mistralai/Mistral-7B-Instruct-v0.3
 ---
-Model Details
-Model Description
-This is a fine-tuned LLM based on Mistral-7B-Instruct-v0.3, optimized for agent function calling. Using LoRA (Low-Rank Adaptation), the model efficiently executes structured API calls, enabling AI agents to interact seamlessly with external tools and services.
-
-Developed by: Ritvik Gaur
-Funded by: Self-funded
-Shared by: Hugging Face
-Model type: Causal Language Model (CLM)
-Language(s): English (en)
-License: Apache 2.0
-Finetuned from: mistralai/Mistral-7B-Instruct-v0.3
-Model Sources
-Repository: Hugging Face Model Page
-Paper (related dataset): xLAM Function-Calling 60K
-Demo: [Coming soon]
-Uses
-Direct Use
-Agent function calling for structured API interaction
-AI assistants that automate tasks via tool execution
-RAG-based applications that require function-aware responses
-Downstream Use
-Fine-tuned for workflow automation and intelligent API calling
-Extendable for custom tool-call generation
-Out-of-Scope Use
-🚫 Not intended for general text generation
-🚫 Not designed for open-ended conversational AI
-
-How to Get Started with the Model
-Installation
-Install Hugging Face’s transformers library:
-
-bash
-Copy
-Edit
-pip install transformers torch
-Usage Example
-python
-Copy
-Edit
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
 
-model_name = "ritvik77/FineTune_LoRA__AgentToolCall_Mistral-7B_Transformer"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### 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. -->
 
-input_text = """User: What's the weather like in New York?
-Agent: <function_call> {"tool": "get_weather", "parameters": {"location": "New York"}}"""
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+
+
+
+
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-inputs = tokenizer(input_text, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
-outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.5, top_p=0.9)
 
 generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print("Generated Output:\n", generated_text)
-Training Details
-Training Data
-Dataset: Salesforce/xlam-function-calling-60k
-Data Structure: 60K+ function call examples in structured JSON format
-Purpose: Teach the model structured tool execution
-Training Procedure
-Fine-Tuned with LoRA (Low-Rank Adaptation)
-Mixed-Precision Training (BF16) for memory efficiency
-Trained with Hugging Face peft + transformers
-Training Hyperparameters
-Batch Size: 64
-Learning Rate: 2e-5
-Epochs: 3
-Optimizer: AdamW
-Evaluation
-Testing Data, Factors & Metrics
-Testing Data
-Function calling dataset tested against real-world API tasks
-Factors
-Function selection accuracy
-Parameter correctness
-Structured JSON compliance
-Metrics
-Function Call Accuracy: 88.2% on Berkeley Function-Calling Benchmark
-Execution Success Rate: 92.5% when tested with API services
-Results Summary
+
+### 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]