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--- |
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base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit |
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library_name: peft |
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license: mit |
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language: |
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- ar |
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metrics: |
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- accuracy |
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new_version: unsloth/Phi-3.5-mini-instruct-bnb-4bit |
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pipeline_tag: text-generation |
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tags: |
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- NLP |
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--- |
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# Fine-tuned Phi-3.5-mini Model |
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This is a fine-tuned version of the [unsloth/phi-3.5-mini-instruct-bnb-4bit](https://huggingface.co/unsloth/phi-3.5-mini-instruct-bnb-4bit) model. The model has been quantized to 4-bits for efficient inference while maintaining performance. |
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## Model Details |
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### Model Description |
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The model is a fine-tuned version of the unsloth/phi-3.5-mini-instruct-bnb-4bit model, quantized to 4-bits for efficient inference. |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** Causal Language Model (CLM) |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** This model inherits the license from the base model unsloth/phi-3.5-mini-instruct-bnb-4bit. |
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- **Finetuned from model [optional]:** unsloth/phi-3.5-mini-instruct-bnb-4bit |
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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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### Direct Use |
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Here's how to use the model: |
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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 the model and tokenizer |
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model_name = "belal271/fine_tunned_phi3.5" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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device_map="auto", |
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torch_dtype=torch.float16, |
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load_in_4bit=True |
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) |
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# Example prompt |
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prompt = "Your prompt here" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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# Generate response |
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outputs = model.generate( |
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**inputs, |
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max_length=512, |
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temperature=0.7, |
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top_p=0.95, |
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do_sample=True |
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) |
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# Decode and print response |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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### Downstream Use [optional] |
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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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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 above to get started with the model. |
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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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#### 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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[More Information Needed] |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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[More Information Needed] |
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#### Factors |
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[More Information Needed] |
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#### Metrics |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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[More Information Needed] |
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## Environmental Impact |
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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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- **Compute Region:** [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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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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[More Information Needed] |
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## Glossary [optional] |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
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### Framework versions |
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- PEFT 0.14.0 |
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## Quantization Configuration |
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The model uses 4-bit quantization with the following configuration: |
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- Bits: 4 |
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- Compute dtype: float16 |
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- Quantization type: NF4 |
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- Double quantization: Enabled |
