Text Generation
Transformers
Safetensors
lfm2
dpo
preference-optimization
text-editing
rewriting
instruct
conversational
Instructions to use appvoid/a-cool-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/a-cool-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/a-cool-model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/a-cool-model") model = AutoModelForCausalLM.from_pretrained("appvoid/a-cool-model", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/a-cool-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/a-cool-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/a-cool-model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/appvoid/a-cool-model
- SGLang
How to use appvoid/a-cool-model 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 "appvoid/a-cool-model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/a-cool-model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "appvoid/a-cool-model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/a-cool-model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use appvoid/a-cool-model with Docker Model Runner:
docker model run hf.co/appvoid/a-cool-model
File size: 857 Bytes
e79b3cf 43d3704 92e7198 43d3704 8b50dab | 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 | ---
base_model: appvoid/v118-dpo-round-01
library_name: transformers
tags:
- dpo
- preference-optimization
- text-generation
- text-editing
- rewriting
- instruct
private: true
---
<style>
img {
width: auto;
height: 320px;
border-radius: 12px;
}
</style>

A cool model that follows instructions quite well, try it.
| Benchmark | a-cool-model | Qwen2.5 0.5B Instruct | Difference (a-cool-model vs Qwen) | Higher Score |
| :--- | :---: | :---: | :---: | :---: |
| **ARC Easy** | **61.45%** | 59.18% | +2.27% | **a-cool-model** |
| **PIQA** | 67.95% | **70.46%** | -2.51% | **Qwen2.5 0.5B Instruct** |
| **ARC Challenge** | **35.67%** | 33.28% | +2.39% | **a-cool-model** |
| **HellaSwag** | 47.32% | **52.42%** | -5.10% | **Qwen2.5 0.5B Instruct** | |