--- 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 - llama-cpp - gguf-my-repo pipeline_tag: text-generation base_model: ValiantLabs/Llama3.1-8B-Fireplace2 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 --- # Triangle104/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF This model was converted to GGUF format from [`ValiantLabs/Llama3.1-8B-Fireplace2`](https://huggingface.co./ValiantLabs/Llama3.1-8B-Fireplace2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co./spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co./ValiantLabs/Llama3.1-8B-Fireplace2) for more details on the model. --- 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. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash 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: ```bash 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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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 ```