Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/bloom-i1-GGUF:# Run inference directly in the terminal:
llama cli -hf mradermacher/bloom-i1-GGUF:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mradermacher/bloom-i1-GGUF:# Run inference directly in the terminal:
./llama-cli -hf mradermacher/bloom-i1-GGUF:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mradermacher/bloom-i1-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/bloom-i1-GGUF:Use Docker
docker model run hf.co/mradermacher/bloom-i1-GGUF:About
weighted/imatrix quants of https://huggingface.co/bigscience/bloom
static quants are available at https://huggingface.co/mradermacher/bloom-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 |
|---|---|---|---|
| GGUF | i1-IQ1_S | 39.3 | for the desperate |
| GGUF | i1-IQ1_M | 42.9 | mostly desperate |
| GGUF | i1-IQ2_XXS | 48.8 | |
| PART 1 PART 2 | i1-IQ2_XS | 54.0 | |
| PART 1 PART 2 | i1-IQ2_S | 57.0 | |
| PART 1 PART 2 | i1-IQ2_M | 61.8 | |
| PART 1 PART 2 | i1-Q2_K_S | 62.5 | very low quality |
| PART 1 PART 2 | i1-Q2_K | 68.2 | IQ3_XXS probably better |
| PART 1 PART 2 | i1-IQ3_XXS | 70.9 | lower quality |
| PART 1 PART 2 | i1-IQ3_XS | 76.8 | |
| PART 1 PART 2 | i1-IQ3_S | 78.8 | beats Q3_K* |
| PART 1 PART 2 | i1-Q3_K_S | 78.8 | IQ3_XS probably better |
| PART 1 PART 2 | i1-IQ3_M | 87.4 | |
| PART 1 PART 2 | i1-Q3_K_M | 94.5 | IQ3_S probably better |
| PART 1 PART 2 | i1-IQ4_XS | 96.7 | |
| PART 1 PART 2 PART 3 | i1-Q4_0 | 102.7 | fast, low quality |
| PART 1 PART 2 PART 3 | i1-Q4_K_S | 103.1 | optimal size/speed/quality |
| PART 1 PART 2 PART 3 | i1-Q3_K_L | 103.1 | IQ3_M probably better |
| PART 1 PART 2 PART 3 | i1-Q4_1 | 113.3 | |
| PART 1 PART 2 PART 3 | i1-Q4_K_M | 114.8 | fast, recommended |
| PART 1 PART 2 PART 3 | i1-Q5_K_S | 124.3 | |
| PART 1 PART 2 PART 3 | i1-Q5_K_M | 133.7 | |
| PART 1 PART 2 PART 3 | i1-Q6_K | 147.7 | practically like static Q6_K |
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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
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Base model
bigscience/bloom
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/bloom-i1-GGUF:# Run inference directly in the terminal: llama cli -hf mradermacher/bloom-i1-GGUF: