belal271/fine_tunned_phi3.5

Fine-tuned Phi-3.5-mini Model

This is a fine-tuned version of the 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:

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

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Bias, Risks, and Limitations

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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

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• 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

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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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Glossary [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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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