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---
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 |