Instructions to use ryandt/MusingCaterpillar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryandt/MusingCaterpillar with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ryandt/MusingCaterpillar")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ryandt/MusingCaterpillar")
model = AutoModelForCausalLM.from_pretrained("ryandt/MusingCaterpillar", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ryandt/MusingCaterpillar with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryandt/MusingCaterpillar"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryandt/MusingCaterpillar",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ryandt/MusingCaterpillar

SGLang

How to use ryandt/MusingCaterpillar 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 "ryandt/MusingCaterpillar" \
    --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": "ryandt/MusingCaterpillar",
		"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 "ryandt/MusingCaterpillar" \
        --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": "ryandt/MusingCaterpillar",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ryandt/MusingCaterpillar with Docker Model Runner:
```
docker model run hf.co/ryandt/MusingCaterpillar
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Finetune of CultriX/MistralTrix-v1 on Symbolic Logic content from Lewis Carrol (at a very low learning rate because of the very small dataset - I'm just experimenting and have no idea if this was effective at changing the model output).

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.33
AI2 Reasoning Challenge (25-Shot)	72.53
HellaSwag (10-Shot)	88.34
MMLU (5-Shot)	65.26
TruthfulQA (0-shot)	70.93
Winogrande (5-shot)	80.66
GSM8k (5-shot)	62.24

Downloads last month: 85

Safetensors

Model size

9B params

Tensor type

F16

Model tree for ryandt/MusingCaterpillar

Quantizations

2 models

Dataset used to train ryandt/MusingCaterpillar

Spaces using ryandt/MusingCaterpillar 21

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

72.530
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.340
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

65.260
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

70.930
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

80.660
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

62.240