metadata
license: apache-2.0
tags:
- TensorBlock
- GGUF
base_model: w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
model-index:
- name: SOLAR-10.7B-Instruct-v1.0-uncensored
results:
- 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: 38.84
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
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: 33.86
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
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: 0.23
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
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: 5.93
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
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: 18.49
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
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: 26.04
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
name: Open LLM Leaderboard

w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored - GGUF
This repo contains GGUF format model files for w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.
Our projects
Forge | |
---|---|
![]() |
|
An OpenAI-compatible multi-provider routing layer. | |
π Try it now! π | |
Awesome MCP Servers | TensorBlock Studio |
![]() |
![]() |
A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
π See what we built π | π See what we built π |
Prompt template
Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.
Model file specification
Filename | Quant type | File Size | Description |
---|---|---|---|
SOLAR-10.7B-Instruct-v1.0-uncensored-Q2_K.gguf | Q2_K | 4.003 GB | smallest, significant quality loss - not recommended for most purposes |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q3_K_S.gguf | Q3_K_S | 4.665 GB | very small, high quality loss |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q3_K_M.gguf | Q3_K_M | 5.196 GB | very small, high quality loss |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q3_K_L.gguf | Q3_K_L | 5.651 GB | small, substantial quality loss |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q4_0.gguf | Q4_0 | 6.072 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q4_K_S.gguf | Q4_K_S | 6.119 GB | small, greater quality loss |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q4_K_M.gguf | Q4_K_M | 6.462 GB | medium, balanced quality - recommended |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q5_0.gguf | Q5_0 | 7.397 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q5_K_S.gguf | Q5_K_S | 7.397 GB | large, low quality loss - recommended |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q5_K_M.gguf | Q5_K_M | 7.598 GB | large, very low quality loss - recommended |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q6_K.gguf | Q6_K | 8.805 GB | very large, extremely low quality loss |
SOLAR-10.7B-Instruct-v1.0-uncensored-Q8_0.gguf | Q8_0 | 11.404 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/w4r10ck_SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF --include "SOLAR-10.7B-Instruct-v1.0-uncensored-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/w4r10ck_SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'