Text Generation
GGUF
English
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
Eval Results
Inference Endpoints
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-Q5_K_S-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-Q5_K_S-GGUF --hf-file llama3.2-3b-shiningvaliant2-q5_k_s.gguf -p "The meaning to life and the universe is" | |
``` | |
### Server: | |
```bash | |
llama-server --hf-repo Triangle104/Llama3.2-3B-ShiningValiant2-Q5_K_S-GGUF --hf-file llama3.2-3b-shiningvaliant2-q5_k_s.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-Q5_K_S-GGUF --hf-file llama3.2-3b-shiningvaliant2-q5_k_s.gguf -p "The meaning to life and the universe is" | |
``` | |
or | |
``` | |
./llama-server --hf-repo Triangle104/Llama3.2-3B-ShiningValiant2-Q5_K_S-GGUF --hf-file llama3.2-3b-shiningvaliant2-q5_k_s.gguf -c 2048 | |
``` | |