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---
language:
- en
license: llama3.1
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
pipeline_tag: text-generation
model_type: llama
model-index:
- name: Llama3.1-8B-Fireplace2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 54.83
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 24.07
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 5.82
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 5.15
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 4.38
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 15.63
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
name: Open LLM Leaderboard
---
![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
# QuantFactory/Llama3.1-8B-Fireplace2-GGUF
This is quantized version of [ValiantLabs/Llama3.1-8B-Fireplace2](https://huggingface.co/ValiantLabs/Llama3.1-8B-Fireplace2) created using llama.cpp
# Original Model Card
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/JYkaXrk2DqpXhaL9WymKY.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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ValiantLabs__Llama3.1-8B-Fireplace2)
| Metric |Value|
|-------------------|----:|
|Avg. |18.31|
|IFEval (0-Shot) |54.83|
|BBH (3-Shot) |24.07|
|MATH Lvl 5 (4-Shot)| 5.82|
|GPQA (0-shot) | 5.15|
|MuSR (0-shot) | 4.38|
|MMLU-PRO (5-shot) |15.63|
![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.