---
library_name: peft
widget:
- text: >-
    Below is an instruction that describes a task, paired with an input that
    provides further context. Write a response that appropriately completes the
    request. ### Instruction: Generate an SQL statement to add a row in the
    customers table where the columns are name, address, and city. ### Input:
    name = John, address = 123 Main Street, city = Winter Park ### Response: 
inference:
  parameters:
    temperature: 0.1
    max_new_tokens: 1024
base_model: meta-llama/Llama-2-7b-hf
license: llama2
datasets:
- sahil2801/CodeAlpaca-20k
language:
- en
tags:
- code
- text-generation-inference
- finetuned
- llama-2
- code-llama
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->


## How to Get Started with the Model

To use this adapter:
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

# Load base model in 4 bit
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", load_in_4bit=True)

# Wrap model with pretrained model weights
config = PeftConfig.from_pretrained("MaziyarPanahi/Llama-2-7b-hf-codealpaca-4bit")
model = PeftModel.from_pretrained(model, "MaziyarPanahi/Llama-2-7b-hf-codealpaca-4bit", config=config)
```

Prompt Template:
```
Below is an instruction that describes a task, paired with an input
that provides further context. Write a response that appropriately
completes the request.
### Instruction: {instruction}
### Input: {input}
### Response:
```

## Training procedure

The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
- 
### Framework versions

- PEFT 0.7.1