---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
library_name: peft
license: mit
language:
- ar
metrics:
- accuracy
new_version: unsloth/Phi-3.5-mini-instruct-bnb-4bit
pipeline_tag: text-generation
tags:
- NLP
---

# Fine-tuned Phi-3.5-mini Model

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.

## Model Details

### Model Description

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.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Causal Language Model (CLM)
- **Language(s) (NLP):** [More Information Needed]
- **License:** This model inherits the license from the base model unsloth/phi-3.5-mini-instruct-bnb-4bit.
- **Finetuned from model [optional]:** unsloth/phi-3.5-mini-instruct-bnb-4bit

### Model Sources [optional]

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

### Direct Use

Here's how to use the model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer
model_name = "belal271/fine_tunned_phi3.5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_4bit=True
)

# Example prompt
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate response
outputs = model.generate(
    **inputs,
    max_length=512,
    temperature=0.7,
    top_p=0.95,
    do_sample=True
)

# Decode and print response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Downstream Use [optional]

[More Information Needed]

### Out-of-Scope Use

[More Information Needed]

## Bias, Risks, and Limitations

[More Information Needed]

### Recommendations

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 above to get started with the model.

## Training Details

### Training Data

[More Information Needed]

### 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]

[More Information Needed]

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

[More Information Needed]

#### Factors

[More Information Needed]

#### Metrics

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

[More Information Needed]

## Environmental Impact

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]

[More Information Needed]

## Glossary [optional]

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]

### Framework versions

- PEFT 0.14.0

## Quantization Configuration

The model uses 4-bit quantization with the following configuration:
- Bits: 4
- Compute dtype: float16
- Quantization type: NF4
- Double quantization: Enabled