Add comprehensive model card for Qwen2.5-0.5B-Instruct fine-tuned on xLAM
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library_name: transformers
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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- **
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical 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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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
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## Model Examination [optional]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**APA:**
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## Glossary [optional]
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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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- qwen2.5-0.5b-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: Qwen/Qwen2.5-0.5B-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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# Qwen2.5-0.5B-Instruct Fine-tuned on xLAM
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## Overview
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This is a fine-tuned version of the Qwen2.5-0.5B-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:** Qwen/Qwen2.5-0.5B-Instruct
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- **Model size:** Qwen2.5-0.5B-Instruct parameters
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- **Vocab size:** 151,936 tokens
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- **Max sequence length:** 2,048 tokens
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- **Tensor type:** BF16
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- **Pad token:** `<|im_end|>` (ID: 151645)
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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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### 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:** ./Qwen2_5_0_5B_Instruct_xLAM
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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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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"ermiaazarkhalili/Qwen2.5-0.5B-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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tokenizer = AutoTokenizer.from_pretrained(
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"ermiaazarkhalili/Qwen2.5-0.5B-Instruct_Function_Calling_xLAM",
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trust_remote_code=True
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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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# Tokenize and generate
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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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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# 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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- **Qwen2.5-0.5B-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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## 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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- **Qwen2.5-0.5B-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_Qwen2.5-0.5B-Instruct_Function_Calling_xLAM,
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author = {ermiaazarkhalili},
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title = { Fine-tuning Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct_Function_Calling_xLAM}}
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}
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```
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