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
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
name: acc
- type: acc
value: 63
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-Q4_K_M-GGUF
This model was converted to GGUF format from ValiantLabs/Llama3.1-8B-ShiningValiant2 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card 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)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Triangle104/Llama3.1-8B-ShiningValiant2-Q4_K_M-GGUF --hf-file llama3.1-8b-shiningvaliant2-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Llama3.1-8B-ShiningValiant2-Q4_K_M-GGUF --hf-file llama3.1-8b-shiningvaliant2-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps 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-Q4_K_M-GGUF --hf-file llama3.1-8b-shiningvaliant2-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Llama3.1-8B-ShiningValiant2-Q4_K_M-GGUF --hf-file llama3.1-8b-shiningvaliant2-q4_k_m.gguf -c 2048