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Model: ValiantLabs/Llama3.1-8B-Fireplace2 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: llama3.1
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tags:
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- fireplace
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- fireplace-2
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- valiant
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- valiant-labs
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- llama
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- llama-3.1
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- llama-3.1-instruct
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- llama-3.1-instruct-8b
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- llama-3
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- llama-3-instruct
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- llama-3-instruct-8b
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- 8b
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- function-calling
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- sql
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- database
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- data-visualization
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- matplotlib
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- json
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- conversational
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- chat
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- instruct
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pipeline_tag: text-generation
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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model_type: llama
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model-index:
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- name: Llama3.1-8B-Fireplace2
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 54.83
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 24.07
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 5.82
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 5.15
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 4.38
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 15.63
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-Fireplace2
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name: Open LLM Leaderboard
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---
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**[Click here to support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource)**
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Fireplace 2 is a chat model, adding helpful structured outputs to Llama 3.1 8b Instruct.
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- an expansion pack of supplementary outputs - request them at will within your chat:
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- Inline function calls
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- SQL queries
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- JSON objects
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- Data visualization with matplotlib
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- Mix normal chat and structured outputs within the same conversation.
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- Fireplace 2 supplements the existing strengths of Llama 3.1, providing inline capabilities within the Llama 3 Instruct format.
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## Version
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This is the **2024-07-23** release of Fireplace 2 for Llama 3.1 8b.
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We're excited to bring further upgrades and releases to Fireplace 2 in the future.
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Help us and recommend Fireplace 2 to your friends!
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## Prompting Guide
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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:
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import transformers
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import torch
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model_id = "ValiantLabs/Llama3.1-8B-Fireplace2"
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pipeline = transformers.pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16},
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are Fireplace, an expert technical assistant."},
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{"role": "user", "content": "Hi, can you explain local area networking to me?"}, #general Llama 3.1 chat
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#{"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
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#{"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
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#{"role": "user", "content": "Show me an example of a histogram with a fixed bin size. Use attractive colors.<|request_matplotlib|>"}, #for data visualization
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#{"role": "user", "content": "Can you define the word 'presence' for me, thanks!<|request_json|>"}, #for JSON output
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]
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outputs = pipeline(
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messages,
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max_new_tokens=512,
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)
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print(outputs[0]["generated_text"][-1])
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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.
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For handling of function call responses, use the [Llama 3.1 Instruct tool response style.](https://huggingface.co/blog/llama31#custom-tool-calling)
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## Special Tokens
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Fireplace 2 utilizes special tokens applied to the Llama 3.1 tokenizer:
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- <|request_json|>
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- <|start_json|>
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- <|end_json|>
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- <|request_sql|>
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- <|start_sql|>
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- <|end_sql|>
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- <|request_matplotlib|>
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- <|start_matplotlib|>
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- <|end_matplotlib|>
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- <|request_function_call|>
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- <|start_function_call|>
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- <|end_function_call|>
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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.
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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.
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## The Model
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Fireplace 2 is built on top of Llama 3.1 8b Instruct.
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This version of Fireplace 2 uses data from the following datasets:
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- [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
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- [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
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- [sequelbox/Cadmium](https://huggingface.co/datasets/sequelbox/Cadmium)
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- [sequelbox/Harlequin](https://huggingface.co/datasets/sequelbox/Harlequin)
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- [migtissera/Tess-v1.5](https://huggingface.co/datasets/migtissera/Tess-v1.5)
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- [LDJnr/Pure-Dove](https://huggingface.co/datasets/LDJnr/Pure-Dove)
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Additional capabilities will be added to future releases.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ValiantLabs__Llama3.1-8B-Fireplace2)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |18.31|
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|IFEval (0-Shot) |54.83|
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|BBH (3-Shot) |24.07|
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|MATH Lvl 5 (4-Shot)| 5.82|
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|GPQA (0-shot) | 5.15|
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|MuSR (0-shot) | 4.38|
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|MMLU-PRO (5-shot) |15.63|
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Fireplace 2 is created by [Valiant Labs.](http://valiantlabs.ca/)
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[Check out our HuggingFace page for Shining Valiant 2 and our other models!](https://huggingface.co/ValiantLabs)
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We care about open source.
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For everyone to use.
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We encourage others to finetune further from our models.
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