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Add comprehensive model card for Llama-3.1-8B-Instruct fine-tuned on xLAM

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  library_name: transformers
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- tags: []
 
 
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- # Model Card for Model ID
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  ## Model Details
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Bias, Risks, and Limitations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- ## Evaluation
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- ## Model Examination [optional]
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- 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).
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- ## Technical Specifications [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ## Model Card Contact
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+
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  ---
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+ license: cc-by-nc-4.0
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+ tags:
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+ - text-generation
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+ - llama-3.1-8b-instruct
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+ - function-calling
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+ - finetuned-model
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+ - trl
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+ - lora
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+ - Salesforce/xlam-function-calling-60k
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+ datasets:
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+ - Salesforce/xlam-function-calling-60k
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+ base_model: meta-llama/Llama-3.1-8B-Instruct
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  library_name: transformers
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+ languages:
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+ - en
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+ pipeline_tag: text-generation
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  ---
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+ # Llama-3.1-8B-Instruct Fine-tuned on xLAM
 
 
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+ ## Overview
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+ This is a fine-tuned version of the Llama-3.1-8B-Instruct model. The model was trained using Hugging Face's TRL library on the xLAM dataset for function calling capabilities.
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  ## Model Details
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+ - **Developed by:** ermiaazarkhalili
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+ - **License:** cc-by-nc-4.0
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+ - **languages:** en
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+ - **Finetuned from model:** meta-llama/Llama-3.1-8B-Instruct
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+ - **Model size:** Llama-3.1-8B-Instruct parameters
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+ - **Vocab size:** 128,256 tokens
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+ - **Max sequence length:** 2,048 tokens
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+ - **Tensor type:** BF16
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+ - **Pad token:** `<|eot_id|>` (ID: 128009)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Training Information
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+ The model was fine-tuned using the following configuration:
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+ ### Training Libraries
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+ - **Hugging Face TRL Library** for advanced training techniques
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+ - **LoRA (Low-Rank Adaptation)** for parameter-efficient training
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+ - **4-bit quantization** for memory efficiency
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+
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+ ### Training Parameters
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+ - **Learning Rate:** 0.0001
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+ - **Batch Size:** 16
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+ - **Gradient Accumulation Steps:** 8
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+ - **Max Training Steps:** 1,000
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+ - **Warmup Ratio:** 0.1
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+ - **Max Sequence Length:** 2,048
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+ - **Output Directory:** ./Llama_3_1_8B_Instruct_xLAM
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+
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+ ### LoRA Configuration
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+ - **LoRA Rank (r):** 16
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+ - **LoRA Alpha:** 32
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+ - **Target Modules:** Query and Value projections
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+ - **LoRA Dropout:** 0.1
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+
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+ # Load model and tokenizer
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+ model = AutoModelForCausalLM.from_pretrained(
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+ "ermiaazarkhalili/Llama-3.1-8B-Instruct_Function_Calling_xLAM",
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+ torch_dtype=torch.bfloat16,
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+ device_map="auto",
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+ trust_remote_code=True
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+ )
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+
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+ tokenizer = AutoTokenizer.from_pretrained(
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+ "ermiaazarkhalili/Llama-3.1-8B-Instruct_Function_Calling_xLAM",
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+ trust_remote_code=True
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+ )
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+
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+ text= "<user>Check if the numbers 8 and 1233 are powers of two.</user>\n\n<tools>"
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+
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+ # Tokenize and generate
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+ inputs = tokenizer(text, return_tensors="pt").to(model.device)
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+
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+ outputs = model.generate(
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+ **inputs,
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+ max_new_tokens=512,
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+ temperature=0.7,
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+ do_sample=True,
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+ pad_token_id=tokenizer.pad_token_id,
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+ eos_token_id=tokenizer.eos_token_id
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+ )
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+
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+ # Decode response
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+ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+ generated_text = response[len(text):].strip()
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+ print(generated_text)
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+ ```
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+ ## Dataset
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+ The model was trained on the **xLAM** dataset.
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+ ## Model Performance
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+ This fine-tuned model demonstrates improved capabilities in:
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+ - **Function Detection:** Identifying when to call functions
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+ - **Parameter Extraction:** Extracting correct parameters from user queries
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+ - **Output Formatting:** Generating properly structured function calls
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+ - **Tool Integration:** Working with external APIs and tools
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+ ## Credits
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+ This model was developed by [ermiaazarkhalili](https://huggingface.co/ermiaazarkhalili) and leverages the capabilities of:
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+ - **Llama-3.1-8B-Instruct** base model
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+ - **Hugging Face TRL** for advanced fine-tuning techniques
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+ - **LoRA** for parameter-efficient adaptation
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+
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+ ## Contact
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+ For any inquiries or support, please reach out to the developer at [ermiaazarkhalili](https://huggingface.co/ermiaazarkhalili).
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+ ## Acknowledgments
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+ We would like to thank the creators of:
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+ - **Llama-3.1-8B-Instruct** for the excellent base model
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+ - **Hugging Face** for the TRL library and infrastructure
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+ - **xLAM** dataset contributors
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+ - **LoRA** researchers for parameter-efficient fine-tuning methods
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+ ## Citation
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+ If you use this model, please cite:
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+ ```bibtex
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+ @misc{ermiaazarkhalili_Llama-3.1-8B-Instruct_Function_Calling_xLAM,
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+ author = {ermiaazarkhalili},
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+ title = { Fine-tuning Llama-3.1-8B-Instruct on xLAM for Function Calling},
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+ year = {2025},
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+ publisher = {Hugging Face},
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+ howpublished = {\url{https://huggingface.co/ermiaazarkhalili/Llama-3.1-8B-Instruct_Function_Calling_xLAM}}
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+ }
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+ ```