---
language:
- en
pipeline_tag: text-generation
tags:
- fireplace
- fireplace-2
- valiant
- valiant-labs
- llama
- llama-3.1
- llama-3.1-instruct
- llama-3.1-instruct-8b
- llama-3
- llama-3-instruct
- llama-3-instruct-8b
- 8b
- function-calling
- sql
- database
- data-visualization
- matplotlib
- json
- conversational
- chat
- instruct
model_type: llama
license: llama3.1
---


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/qg49GOlx8zogDOrMTnb89.jpeg)


Fireplace 2 is a chat model, adding helpful structured outputs to Llama 3.1 8b Instruct.
  - an expansion pack of supplementary outputs - request them at will within your chat:
    - Inline function calls
    - SQL queries
    - JSON objects
    - Data visualization with matplotlib
  - Mix normal chat and structured outputs within the same conversation.
  - Fireplace 2 supplements the existing strengths of Llama 3.1, providing inline capabilities within the Llama 3 Instruct format.


## Version

This is the **2024-07-23** release of Fireplace 2 for Llama 3.1 8b.

We're excited to bring further upgrades and releases to Fireplace 2 in the future. 

Help us and recommend Fireplace 2 to your friends!


## Prompting Guide
Fireplace uses the [Llama 3.1 Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) prompt format. The example script below can be used as a starting point for general chat with Llama 3.1 and also includes the different special tokens used for Fireplace 2's added features:


import transformers
import torch

model_id = "ValiantLabs/Llama3.1-8B-Fireplace2"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are Fireplace, an expert technical assistant."},
    {"role": "user", "content": "Hi, can you explain local area networking to me?"}, #general Llama 3.1 chat
    #{"role": "user", "content": "I have the following SQL table: employees (job_id VARCHAR, salary INTEGER)\n\nCan you find all employees with a salary above $75000?<|request_sql|>"}, #for SQL query
    #{"role": "user", "content": "{""name"": ""get_news_headlines"",""description"": ""Get the latest news headlines"",""parameters"": {""type"": ""object"",""properties"": {""country"": {""type"": ""string"",""description"": ""The country for which news headlines are to be retrieved""}},""required"": [""country""]}}\n\nHi, can you get me the latest news headlines for the United States?<|request_function_call|>"}, # for function call
    #{"role": "user", "content": "Show me an example of a histogram with a fixed bin size. Use attractive colors.<|request_matplotlib|>"}, #for data visualization
    #{"role": "user", "content": "Can you define the word 'presence' for me, thanks!<|request_json|>"}, #for JSON output
]

outputs = pipeline(
    messages,
    max_new_tokens=512,
)
print(outputs[0]["generated_text"][-1])


While Fireplace 2 is trained to minimize incorrect structured outputs, they can still occur occasionally. Production uses of Fireplace 2 should verify the structure of all model outputs and remove any unneeded components of the output.

For handling of function call responses, use the [Llama 3.1 Instruct tool response style.](https://huggingface.co/blog/llama31#custom-tool-calling)


## Special Tokens

Fireplace 2 utilizes special tokens applied to the Llama 3.1 tokenizer:

- <|request_json|>
- <|start_json|>
- <|end_json|>
- <|request_sql|>
- <|start_sql|>
- <|end_sql|>
- <|request_matplotlib|>
- <|start_matplotlib|>
- <|end_matplotlib|>
- <|request_function_call|>
- <|start_function_call|>
- <|end_function_call|>

These are supplemental to the existing special tokens used by Llama 3.1, such as <|python_tag|> and <|start_header_id|>. Fireplace 2 has been trained using the Llama 3.1 Instruct chat structure, with new special tokens added within the conversation.

The 'request' tokens are used by the user to request a specific type of structured output. They should be appended to the end of the user's message and can be alternated with normal chat responses throughout the conversation.

## The Model
Fireplace 2 is built on top of Llama 3.1 8b Instruct.

This version of Fireplace 2 uses data from the following datasets:

- [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
- [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
- [sequelbox/Cadmium](https://huggingface.co/datasets/sequelbox/Cadmium)
- [sequelbox/Harlequin](https://huggingface.co/datasets/sequelbox/Harlequin)
- [migtissera/Tess-v1.5](https://huggingface.co/datasets/migtissera/Tess-v1.5)
- [LDJnr/Pure-Dove](https://huggingface.co/datasets/LDJnr/Pure-Dove)

Additional capabilities will be added to future releases.


![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/VCJ8Fmefd8cdVhXSSxJiD.jpeg)


Fireplace 2 is created by [Valiant Labs.](http://valiantlabs.ca/)

[Check out our HuggingFace page for Shining Valiant 2 and our other models!](https://huggingface.co/ValiantLabs)

[Follow us on X for updates on our models!](https://twitter.com/valiant_labs)

We care about open source.
For everyone to use.

We encourage others to finetune further from our models.