Text Generation
Transformers
Safetensors
English
llama
inference
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use kaipybara/hackping-2025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaipybara/hackping-2025 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kaipybara/hackping-2025")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kaipybara/hackping-2025") model = AutoModelForCausalLM.from_pretrained("kaipybara/hackping-2025") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use kaipybara/hackping-2025 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kaipybara/hackping-2025" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaipybara/hackping-2025", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kaipybara/hackping-2025
- SGLang
How to use kaipybara/hackping-2025 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 "kaipybara/hackping-2025" \ --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": "kaipybara/hackping-2025", "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 "kaipybara/hackping-2025" \ --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": "kaipybara/hackping-2025", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kaipybara/hackping-2025 with Docker Model Runner:
docker model run hf.co/kaipybara/hackping-2025
Update README.md
Browse files
README.md
CHANGED
|
@@ -6,4 +6,24 @@ language:
|
|
| 6 |
- en
|
| 7 |
base_model:
|
| 8 |
- meta-llama/Llama-3.2-3B
|
| 9 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 6 |
- en
|
| 7 |
base_model:
|
| 8 |
- meta-llama/Llama-3.2-3B
|
| 9 |
+
tags:
|
| 10 |
+
- text-generation
|
| 11 |
+
- inference
|
| 12 |
+
---
|
| 13 |
+
|
| 14 |
+
# Hackping 2025 Model
|
| 15 |
+
|
| 16 |
+
This is a test model uploaded for Hackping 2025.
|
| 17 |
+
|
| 18 |
+
## Usage
|
| 19 |
+
|
| 20 |
+
```python
|
| 21 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
| 22 |
+
|
| 23 |
+
tokenizer = AutoTokenizer.from_pretrained("kaipybara/hackping-2025")
|
| 24 |
+
model = AutoModelForCausalLM.from_pretrained("kaipybara/hackping-2025")
|
| 25 |
+
|
| 26 |
+
input_text = "Hello, my name is"
|
| 27 |
+
inputs = tokenizer(input_text, return_tensors="pt")
|
| 28 |
+
outputs = model.generate(**inputs)
|
| 29 |
+
print(tokenizer.decode(outputs[0]))
|