Text Generation
Transformers
Safetensors
opt
galactica
Eval Results (legacy)
text-generation-inference
Instructions to use KnutJaegersberg/Galpaca-30b-MiniOrca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KnutJaegersberg/Galpaca-30b-MiniOrca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KnutJaegersberg/Galpaca-30b-MiniOrca")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/Galpaca-30b-MiniOrca") model = AutoModelForCausalLM.from_pretrained("KnutJaegersberg/Galpaca-30b-MiniOrca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KnutJaegersberg/Galpaca-30b-MiniOrca with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KnutJaegersberg/Galpaca-30b-MiniOrca" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KnutJaegersberg/Galpaca-30b-MiniOrca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KnutJaegersberg/Galpaca-30b-MiniOrca
- SGLang
How to use KnutJaegersberg/Galpaca-30b-MiniOrca with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "KnutJaegersberg/Galpaca-30b-MiniOrca" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KnutJaegersberg/Galpaca-30b-MiniOrca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "KnutJaegersberg/Galpaca-30b-MiniOrca" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KnutJaegersberg/Galpaca-30b-MiniOrca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KnutJaegersberg/Galpaca-30b-MiniOrca with Docker Model Runner:
docker model run hf.co/KnutJaegersberg/Galpaca-30b-MiniOrca
File size: 3,853 Bytes
b015db3 4857655 b35ed4f f321284 b35ed4f 73a12ed b35ed4f f6af4b0 f321284 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | ---
license: cc-by-nc-4.0
tags:
- galactica
datasets:
- TinyPixel/orca-mini
pipeline_tag: text-generation
model-index:
- name: Galpaca-30b-MiniOrca
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 48.89
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Galpaca-30b-MiniOrca
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 57.8
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Galpaca-30b-MiniOrca
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 43.72
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Galpaca-30b-MiniOrca
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 41.1
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Galpaca-30b-MiniOrca
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 60.06
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Galpaca-30b-MiniOrca
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 1.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=KnutJaegersberg/Galpaca-30b-MiniOrca
name: Open LLM Leaderboard
---
Galpaca trained for 2.7 epochs on the 50k shortest records of miniorca dataset with NEFTune.
Prompt Example:
```
### System:
You are an AI assistant. You will be given a task. You must generate a detailed and long answer.
### User:
What is AGI?
### Assistant:
```

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Galpaca-30b-MiniOrca)
| Metric |Value|
|---------------------------------|----:|
|Avg. |42.23|
|AI2 Reasoning Challenge (25-Shot)|48.89|
|HellaSwag (10-Shot) |57.80|
|MMLU (5-Shot) |43.72|
|TruthfulQA (0-shot) |41.10|
|Winogrande (5-shot) |60.06|
|GSM8k (5-shot) | 1.82|
|