Instructions to use mradermacher/SRA-LLM-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/SRA-LLM-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/SRA-LLM-GGUF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use mradermacher/SRA-LLM-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mradermacher/SRA-LLM-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mradermacher/SRA-LLM-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/SRA-LLM-GGUF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="mradermacher/SRA-LLM-GGUF", max_seq_length=2048, )
About
static quants of https://huggingface.co/Daemontatox/SRA-LLM
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SRA-LLM-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| PART 1 PART 2 | Q3_K_S | 66.2 | |
| PART 1 PART 2 | Q2_K | 66.3 | |
| PART 1 PART 2 | IQ4_XS | 67.1 | |
| PART 1 PART 2 | Q3_K_M | 71.2 | lower quality |
| PART 1 PART 2 | Q3_K_L | 73.5 | |
| PART 1 PART 2 | Q4_K_S | 81.0 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 88.0 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 88.1 | |
| PART 1 PART 2 | Q5_K_M | 94.0 | |
| PART 1 PART 2 PART 3 | Q6_K | 124.3 | very good quality |
| PART 1 PART 2 PART 3 | Q8_0 | 124.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.
Model tree for mradermacher/SRA-LLM-GGUF
Base model
Daemontatox/SRA-LLM