Triangle104/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF

This model was converted to GGUF format from ValiantLabs/Llama3.1-8B-Fireplace2 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

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 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. 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
b-mc2/sql-create-context
sequelbox/Cadmium
sequelbox/Harlequin
migtissera/Tess-v1.5
LDJnr/Pure-Dove

Additional capabilities will be added to future releases.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF --hf-file llama3.1-8b-fireplace2-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF --hf-file llama3.1-8b-fireplace2-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF --hf-file llama3.1-8b-fireplace2-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF --hf-file llama3.1-8b-fireplace2-q4_k_m.gguf -c 2048