Instructions to use QuantFactory/gemma2-gutenberg-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/gemma2-gutenberg-9B-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/gemma2-gutenberg-9B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/gemma2-gutenberg-9B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M
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 QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M
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 QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/gemma2-gutenberg-9B-GGUF with Ollama:
ollama run hf.co/QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use QuantFactory/gemma2-gutenberg-9B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/gemma2-gutenberg-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/gemma2-gutenberg-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma2-gutenberg-9B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
ADDED
|
@@ -0,0 +1,140 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
---
|
| 3 |
+
|
| 4 |
+
license: gemma
|
| 5 |
+
library_name: transformers
|
| 6 |
+
base_model:
|
| 7 |
+
- UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
|
| 8 |
+
datasets:
|
| 9 |
+
- jondurbin/gutenberg-dpo-v0.1
|
| 10 |
+
model-index:
|
| 11 |
+
- name: gemma2-gutenberg-9B
|
| 12 |
+
results:
|
| 13 |
+
- task:
|
| 14 |
+
type: text-generation
|
| 15 |
+
name: Text Generation
|
| 16 |
+
dataset:
|
| 17 |
+
name: IFEval (0-Shot)
|
| 18 |
+
type: HuggingFaceH4/ifeval
|
| 19 |
+
args:
|
| 20 |
+
num_few_shot: 0
|
| 21 |
+
metrics:
|
| 22 |
+
- type: inst_level_strict_acc and prompt_level_strict_acc
|
| 23 |
+
value: 27.96
|
| 24 |
+
name: strict accuracy
|
| 25 |
+
source:
|
| 26 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/gemma2-gutenberg-9B
|
| 27 |
+
name: Open LLM Leaderboard
|
| 28 |
+
- task:
|
| 29 |
+
type: text-generation
|
| 30 |
+
name: Text Generation
|
| 31 |
+
dataset:
|
| 32 |
+
name: BBH (3-Shot)
|
| 33 |
+
type: BBH
|
| 34 |
+
args:
|
| 35 |
+
num_few_shot: 3
|
| 36 |
+
metrics:
|
| 37 |
+
- type: acc_norm
|
| 38 |
+
value: 42.36
|
| 39 |
+
name: normalized accuracy
|
| 40 |
+
source:
|
| 41 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/gemma2-gutenberg-9B
|
| 42 |
+
name: Open LLM Leaderboard
|
| 43 |
+
- task:
|
| 44 |
+
type: text-generation
|
| 45 |
+
name: Text Generation
|
| 46 |
+
dataset:
|
| 47 |
+
name: MATH Lvl 5 (4-Shot)
|
| 48 |
+
type: hendrycks/competition_math
|
| 49 |
+
args:
|
| 50 |
+
num_few_shot: 4
|
| 51 |
+
metrics:
|
| 52 |
+
- type: exact_match
|
| 53 |
+
value: 1.44
|
| 54 |
+
name: exact match
|
| 55 |
+
source:
|
| 56 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/gemma2-gutenberg-9B
|
| 57 |
+
name: Open LLM Leaderboard
|
| 58 |
+
- task:
|
| 59 |
+
type: text-generation
|
| 60 |
+
name: Text Generation
|
| 61 |
+
dataset:
|
| 62 |
+
name: GPQA (0-shot)
|
| 63 |
+
type: Idavidrein/gpqa
|
| 64 |
+
args:
|
| 65 |
+
num_few_shot: 0
|
| 66 |
+
metrics:
|
| 67 |
+
- type: acc_norm
|
| 68 |
+
value: 11.74
|
| 69 |
+
name: acc_norm
|
| 70 |
+
source:
|
| 71 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/gemma2-gutenberg-9B
|
| 72 |
+
name: Open LLM Leaderboard
|
| 73 |
+
- task:
|
| 74 |
+
type: text-generation
|
| 75 |
+
name: Text Generation
|
| 76 |
+
dataset:
|
| 77 |
+
name: MuSR (0-shot)
|
| 78 |
+
type: TAUR-Lab/MuSR
|
| 79 |
+
args:
|
| 80 |
+
num_few_shot: 0
|
| 81 |
+
metrics:
|
| 82 |
+
- type: acc_norm
|
| 83 |
+
value: 16.71
|
| 84 |
+
name: acc_norm
|
| 85 |
+
source:
|
| 86 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/gemma2-gutenberg-9B
|
| 87 |
+
name: Open LLM Leaderboard
|
| 88 |
+
- task:
|
| 89 |
+
type: text-generation
|
| 90 |
+
name: Text Generation
|
| 91 |
+
dataset:
|
| 92 |
+
name: MMLU-PRO (5-shot)
|
| 93 |
+
type: TIGER-Lab/MMLU-Pro
|
| 94 |
+
config: main
|
| 95 |
+
split: test
|
| 96 |
+
args:
|
| 97 |
+
num_few_shot: 5
|
| 98 |
+
metrics:
|
| 99 |
+
- type: acc
|
| 100 |
+
value: 35.47
|
| 101 |
+
name: accuracy
|
| 102 |
+
source:
|
| 103 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/gemma2-gutenberg-9B
|
| 104 |
+
name: Open LLM Leaderboard
|
| 105 |
+
|
| 106 |
+
---
|
| 107 |
+
|
| 108 |
+

|
| 109 |
+
|
| 110 |
+
# QuantFactory/gemma2-gutenberg-9B-GGUF
|
| 111 |
+
This is quantized version of [nbeerbower/gemma2-gutenberg-9B](https://huggingface.co/nbeerbower/gemma2-gutenberg-9B) created using llama.cpp
|
| 112 |
+
|
| 113 |
+
# Original Model Card
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
# gemma2-gutenberg-9B
|
| 117 |
+
|
| 118 |
+
[UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3) finetuned on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1).
|
| 119 |
+
|
| 120 |
+
### Method
|
| 121 |
+
|
| 122 |
+
Finetuned using an RTX 4090 using ORPO for 3 epochs.
|
| 123 |
+
|
| 124 |
+
[Fine-tune Llama 3 with ORPO](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html)
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
|
| 128 |
+
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__gemma2-gutenberg-9B)
|
| 129 |
+
|
| 130 |
+
| Metric |Value|
|
| 131 |
+
|-------------------|----:|
|
| 132 |
+
|Avg. |22.61|
|
| 133 |
+
|IFEval (0-Shot) |27.96|
|
| 134 |
+
|BBH (3-Shot) |42.36|
|
| 135 |
+
|MATH Lvl 5 (4-Shot)| 1.44|
|
| 136 |
+
|GPQA (0-shot) |11.74|
|
| 137 |
+
|MuSR (0-shot) |16.71|
|
| 138 |
+
|MMLU-PRO (5-shot) |35.47|
|
| 139 |
+
|
| 140 |
+
|