SupraLabs/SupraThink-Dataset-500x
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How to use Hoglet-33/Hogleto with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Hoglet-33/Hogleto", trust_remote_code=True) # Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Hoglet-33/Hogleto", trust_remote_code=True, device_map="auto")How to use Hoglet-33/Hogleto with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hoglet-33/Hogleto"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Hoglet-33/Hogleto",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Hoglet-33/Hogleto
How to use Hoglet-33/Hogleto with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Hoglet-33/Hogleto" \
--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": "Hoglet-33/Hogleto",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "Hoglet-33/Hogleto" \
--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": "Hoglet-33/Hogleto",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Hoglet-33/Hogleto with Docker Model Runner:
docker model run hf.co/Hoglet-33/Hogleto
Hogleto is a 1.4M param model finetuned from BananaMind 2.1 Pico Preview. This model is an artifact from testing out the BananaAll app
LoRA was used to train this model, everything was kept as default unless the setting is listed below.
LoRA rank: 8 LoRA alpha: 16 Dataset: SupraThink Training steps: 5
| Benchmark | Accuracy | Random Baseline |
|---|---|---|
| PIQA | 53.86% | 50.00% |
| ARC-Easy | 29.55% | 25.00% |
| ARC-Challenge | 22.61% | 25.00% |
| HellaSwag | 27.03% | 25.00% |
| ArithMark-3.0 | 32.50% | 25.00% |
Base model
BananaMind/BananaMind-2.1-Pico-Preview