inference, language, library_name, pipeline_tag, tags, datasets, base_model

inference	language	library_name	pipeline_tag	tags	datasets	base_model
false	en	transformers	text-generation	llama TensorBlock GGUF	LDJnr/Capybara jondurbin/airoboros-3.2 unalignment/toxic-dpo-v0.1 LDJnr/Verified-Camel HuggingFaceH4/no_robots Doctor-Shotgun/no-robots-sharegpt Doctor-Shotgun/capybara-sharegpt	Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct

Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct - GGUF

This repo contains GGUF format model files for Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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## Prompt template

<s>
### Instruction:
{system_prompt}

### Input:
{prompt}

### Response:

Model file specification

Filename	Quant type	File Size	Description
TinyLlama-1.1B-32k-Instruct-Q2_K.gguf	Q2_K	0.402 GB	smallest, significant quality loss - not recommended for most purposes
TinyLlama-1.1B-32k-Instruct-Q3_K_S.gguf	Q3_K_S	0.465 GB	very small, high quality loss
TinyLlama-1.1B-32k-Instruct-Q3_K_M.gguf	Q3_K_M	0.511 GB	very small, high quality loss
TinyLlama-1.1B-32k-Instruct-Q3_K_L.gguf	Q3_K_L	0.551 GB	small, substantial quality loss
TinyLlama-1.1B-32k-Instruct-Q4_0.gguf	Q4_0	0.593 GB	legacy; small, very high quality loss - prefer using Q3_K_M
TinyLlama-1.1B-32k-Instruct-Q4_K_S.gguf	Q4_K_S	0.596 GB	small, greater quality loss
TinyLlama-1.1B-32k-Instruct-Q4_K_M.gguf	Q4_K_M	0.622 GB	medium, balanced quality - recommended
TinyLlama-1.1B-32k-Instruct-Q5_0.gguf	Q5_0	0.713 GB	legacy; medium, balanced quality - prefer using Q4_K_M
TinyLlama-1.1B-32k-Instruct-Q5_K_S.gguf	Q5_K_S	0.713 GB	large, low quality loss - recommended
TinyLlama-1.1B-32k-Instruct-Q5_K_M.gguf	Q5_K_M	0.728 GB	large, very low quality loss - recommended
TinyLlama-1.1B-32k-Instruct-Q6_K.gguf	Q6_K	0.841 GB	very large, extremely low quality loss
TinyLlama-1.1B-32k-Instruct-Q8_0.gguf	Q8_0	1.089 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/TinyLlama-1.1B-32k-Instruct-GGUF --include "TinyLlama-1.1B-32k-Instruct-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/TinyLlama-1.1B-32k-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'