Text Generation
	
	
	
	
	GGUF
	
	
	
		
	
	English
	
	
	
	
	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
	
	
	
	
	TensorBlock
	
	
	
	
	GGUF
	
	
	
		
	
	
		Eval Results
	
	
 
ValiantLabs/Llama3.1-8B-ShiningValiant2 - GGUF
This repo contains GGUF format model files for ValiantLabs/Llama3.1-8B-ShiningValiant2.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Our projects
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Model file specification
| Filename | Quant type | File Size | Description | 
|---|---|---|---|
| Llama3.1-8B-ShiningValiant2-Q2_K.gguf | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes | 
| Llama3.1-8B-ShiningValiant2-Q3_K_S.gguf | Q3_K_S | 3.413 GB | very small, high quality loss | 
| Llama3.1-8B-ShiningValiant2-Q3_K_M.gguf | Q3_K_M | 3.743 GB | very small, high quality loss | 
| Llama3.1-8B-ShiningValiant2-Q3_K_L.gguf | Q3_K_L | 4.025 GB | small, substantial quality loss | 
| Llama3.1-8B-ShiningValiant2-Q4_0.gguf | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M | 
| Llama3.1-8B-ShiningValiant2-Q4_K_S.gguf | Q4_K_S | 4.370 GB | small, greater quality loss | 
| Llama3.1-8B-ShiningValiant2-Q4_K_M.gguf | Q4_K_M | 4.583 GB | medium, balanced quality - recommended | 
| Llama3.1-8B-ShiningValiant2-Q5_0.gguf | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M | 
| Llama3.1-8B-ShiningValiant2-Q5_K_S.gguf | Q5_K_S | 5.215 GB | large, low quality loss - recommended | 
| Llama3.1-8B-ShiningValiant2-Q5_K_M.gguf | Q5_K_M | 5.339 GB | large, very low quality loss - recommended | 
| Llama3.1-8B-ShiningValiant2-Q6_K.gguf | Q6_K | 6.143 GB | very large, extremely low quality loss | 
| Llama3.1-8B-ShiningValiant2-Q8_0.gguf | Q8_0 | 7.954 GB | very large, extremely low quality loss - not recommended | 
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/Llama3.1-8B-ShiningValiant2-GGUF --include "Llama3.1-8B-ShiningValiant2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/Llama3.1-8B-ShiningValiant2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Downloads last month
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Model tree for tensorblock/Llama3.1-8B-ShiningValiant2-GGUF
Base model
meta-llama/Llama-3.1-8B
				Finetuned
	
	
meta-llama/Llama-3.1-8B-Instruct
						
				Finetuned
	
	
ValiantLabs/Llama3.1-8B-ShiningValiant2
						Datasets used to train tensorblock/Llama3.1-8B-ShiningValiant2-GGUF
Evaluation results
- acc on Winogrande (5-Shot)self-reported75.850
- acc on MMLU College Biology (5-Shot)self-reported68.750
- acc on MMLU College Biology (5-Shot)self-reported73.230
- acc on MMLU College Biology (5-Shot)self-reported46.000
- acc on MMLU College Biology (5-Shot)self-reported44.330
- acc on MMLU College Biology (5-Shot)self-reported53.190
- acc on MMLU College Biology (5-Shot)self-reported37.250
- acc on MMLU College Biology (5-Shot)self-reported42.380
- acc on MMLU College Biology (5-Shot)self-reported56.000
- acc on MMLU College Biology (5-Shot)self-reported63.000
