--- 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.