---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.1
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- autoquant
- gptq
- 8 bit
widget:
- messages:
  - role: user
    content: Can you provide ways to eat combinations of bananas and dragonfruits?
base_model:
- meta-llama/Meta-Llama-3.1-8B-Instruct
---

This is 8-bit GPTQ  version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct).
Quantization has been done using [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library.

### Use with transformers

Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.

Make sure to update your transformers installation via `pip install --upgrade transformers` and you have Autogptq, optimum installed.

```bash
!pip install auto-gptq optimum --quiet
!pip install -q --upgrade transformers --quiet
```

```python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "iqbalamo93/Meta-Llama-3.1-8B-Instruct-GPTQ-Q_8"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device)
tokenizer = AutoTokenizer.from_pretrained(model_id) 

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline(
    "text-generation",
    model=model,
    device_map="auto",
    tokenizer=tokenizer,
)

generation_args = { 
    "max_new_tokens": 500, 
    "return_full_text": False, 
    "temperature": 0.1, 
    "do_sample": False,
    "pad_token_id": 128001 
} 

output = pipe(messages, **generation_args) 
print(output[0]['generated_text'])

```

Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)

### Tool use with transformers

LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).

Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. 
Here is a quick example showing a single simple tool:

```python
# First, define a tool
def get_current_temperature(location: str) -> float:
    """
    Get the current temperature at a location.
    
    Args:
        location: The location to get the temperature for, in the format "City, Country"
    Returns:
        The current temperature at the specified location in the specified units, as a float.
    """
    return 22.  # A real function should probably actually get the temperature!

# Next, create a chat and apply the chat template
messages = [
  {"role": "system", "content": "You are a bot that responds to weather queries."},
  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]

inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
```

You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:

```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```

and then call the tool and append the result, with the `tool` role, like so:

```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```

After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).


### Use with `llama`

Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)

To download Original checkpoints, see the example command below leveraging `huggingface-cli`:

```
huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
```



#### Llama 3.1 instruct 

Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. 

**Calibration data**

As done by  AutoGPTQ.

TODO: Study the impact of calibration data on Instruction-tuned models.


### Evaluations

TODO wrt 8-bit model