--- language: - en license: llama3.2 tags: - shining-valiant - shining-valiant-2 - valiant - valiant-labs - llama - llama-3.2 - llama-3.2-instruct - llama-3.2-instruct-3b - llama-3 - llama-3-instruct - llama-3-instruct-3b - 3b - science - physics - biology - chemistry - compsci - computer-science - engineering - technical - conversational - chat - instruct - llama-cpp - gguf-my-repo base_model: ValiantLabs/Llama3.2-3B-ShiningValiant2 datasets: - sequelbox/Celestia - sequelbox/Spurline - sequelbox/Supernova pipeline_tag: text-generation model_type: llama model-index: - name: Llama3.2-3B-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: 69.14 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: 64.58 name: acc - type: acc value: 70.32 name: acc - type: acc value: 44.0 name: acc - type: acc value: 50.25 name: acc - type: acc value: 42.16 name: acc - type: acc value: 35.76 name: acc - type: acc value: 53.19 name: acc - type: acc value: 53.0 name: acc - type: acc value: 61.0 name: acc - type: acc value: 60.53 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: 48.9 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-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: 19.11 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-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: 9.14 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-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: 3.02 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-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: 5.49 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-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: 19.1 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-ShiningValiant2 name: Open LLM Leaderboard --- # Triangle104/Llama3.2-3B-ShiningValiant2-Q4_K_M-GGUF This model was converted to GGUF format from [`ValiantLabs/Llama3.2-3B-ShiningValiant2`](https://huggingface.co/ValiantLabs/Llama3.2-3B-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/Llama3.2-3B-ShiningValiant2) for more details on the model. --- Model details: - Shining Valiant 2 is a chat model built on Llama 3.2 3b, finetuned on our data for friendship, insight, knowledge and enthusiasm. Finetuned on meta-llama/Llama-3.2-3B-Instruct for best available general performance Trained on a variety of high quality data; focused on science, engineering, technical knowledge, and structured reasoning Also available for Llama 3.1 70b and Llama 3.1 8b! Version - This is the 2024-09-27 release of Shining Valiant 2 for Llama 3.2 3b. We've improved and open-sourced our new baseline science-instruct dataset. This release features improvements in physics, chemistry, biology, and computer science. 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 3.2 Instruct prompt format. The example script below can be used as a starting point for general chat: import transformers import torch model_id = "ValiantLabs/Llama3.2-3B-ShiningValiant2" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are an AI assistant."}, {"role": "user", "content": "Describe the use of chiral auxiliaries in organic synthesis."} ] outputs = pipeline( messages, max_new_tokens=2048, ) print(outputs[0]["generated_text"][-1]) The Model - Shining Valiant 2 is built on top of Llama 3.2 3b 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/Llama3.2-3B-ShiningValiant2-Q4_K_M-GGUF --hf-file llama3.2-3b-shiningvaliant2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama3.2-3B-ShiningValiant2-Q4_K_M-GGUF --hf-file llama3.2-3b-shiningvaliant2-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/Llama3.2-3B-ShiningValiant2-Q4_K_M-GGUF --hf-file llama3.2-3b-shiningvaliant2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama3.2-3B-ShiningValiant2-Q4_K_M-GGUF --hf-file llama3.2-3b-shiningvaliant2-q4_k_m.gguf -c 2048 ```