--- language: - en pipeline_tag: text-generation tags: - shining-valiant - shining-valiant-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 - science - physics - biology - chemistry - compsci - computer-science - engineering - technical - conversational - chat - instruct - llama-cpp - gguf-my-repo base_model: ValiantLabs/Llama3.1-8B-ShiningValiant2 datasets: - sequelbox/Celestia - sequelbox/Spurline - sequelbox/Supernova model_type: llama license: llama3.1 model-index: - name: Llama3.1-8B-ShiningValiant2 results: - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-Shot) type: Winogrande args: num_few_shot: 5 metrics: - type: acc value: 75.85 name: acc - task: type: text-generation name: Text Generation dataset: name: MMLU College Biology (5-Shot) type: MMLU args: num_few_shot: 5 metrics: - type: acc value: 68.75 name: acc - type: acc value: 73.23 name: acc - type: acc value: 46.0 name: acc - type: acc value: 44.33 name: acc - type: acc value: 53.19 name: acc - type: acc value: 37.25 name: acc - type: acc value: 42.38 name: acc - type: acc value: 56.0 name: acc - type: acc value: 63.0 name: acc - type: acc value: 63.16 name: acc - 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: 65.24 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2 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: 26.35 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2 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: 11.63 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2 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: 8.95 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2 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: 7.19 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2 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: 26.38 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2 name: Open LLM Leaderboard --- # Triangle104/Llama3.1-8B-ShiningValiant2-Q8_0-GGUF This model was converted to GGUF format from [`ValiantLabs/Llama3.1-8B-ShiningValiant2`](https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2) 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-ShiningValiant2) for more details on the model. --- Model details: - Shining Valiant 2 is a chat model built on Llama 3.1 8b, finetuned on our data for friendship, insight, knowledge and enthusiasm. Finetuned on meta-llama/Meta-Llama-3.1-8B-Instruct for best available general performance Trained on a variety of our high quality open source data; focused on science, engineering, technical knowledge, and structured reasoning Also available for Llama 3.1 70b and Llama 3.2 3b! Version - This is the 2024-11-04 release of Shining Valiant 2 for Llama 3.1 8b. This release uses our newest datasets, open-sourced for everyone's use, including our expanded science-instruct dataset. This release features improvements in logical thinking and structured reasoning as well as physics, chemistry, biology, astronomy, Earth science, computer science, and information theory. Future upgrades will continue to expand Shining Valiant's technical knowledge base. Help us and recommend Shining Valiant 2 to your friends! Prompting Guide - Shining Valiant 2 uses the Llama 3.1 Instruct prompt format. The example script below can be used as a starting point for general chat: import transformers import torch model_id = "ValiantLabs/Llama3.1-8B-ShiningValiant2" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are Shining Valiant, a highly capable chat AI."}, {"role": "user", "content": "Describe the role of transformation matrices in 3D graphics."} ] outputs = pipeline( messages, max_new_tokens=2048, ) print(outputs[0]["generated_text"][-1]) The Model - Shining Valiant 2 is built on top of Llama 3.1 8b Instruct. The current version of Shining Valiant 2 is trained on technical knowledge using sequelbox/Celestia, complex reasoning using sequelbox/Spurline, and general chat capability using sequelbox/Supernova. We're super excited that Shining Valiant's dataset has been fully open-sourced! She's friendly, enthusiastic, insightful, knowledgeable, and loves to learn! Magical. Shining Valiant 2 is created by Valiant Labs. Check out our HuggingFace page for our open-source Build Tools models, including the newest version of code-specialist Enigma! Follow us on X for updates on our models! We care about open source. For everyone to use. We encourage others to finetune further from our models. --- ## 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-ShiningValiant2-Q8_0-GGUF --hf-file llama3.1-8b-shiningvaliant2-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama3.1-8B-ShiningValiant2-Q8_0-GGUF --hf-file llama3.1-8b-shiningvaliant2-q8_0.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-ShiningValiant2-Q8_0-GGUF --hf-file llama3.1-8b-shiningvaliant2-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama3.1-8B-ShiningValiant2-Q8_0-GGUF --hf-file llama3.1-8b-shiningvaliant2-q8_0.gguf -c 2048 ```