Instructions to use Dampish/Dante_1.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dampish/Dante_1.3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Dampish/Dante_1.3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Dampish/Dante_1.3B") model = AutoModelForCausalLM.from_pretrained("Dampish/Dante_1.3B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Dampish/Dante_1.3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Dampish/Dante_1.3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dampish/Dante_1.3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Dampish/Dante_1.3B
- SGLang
How to use Dampish/Dante_1.3B 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 "Dampish/Dante_1.3B" \ --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": "Dampish/Dante_1.3B", "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 "Dampish/Dante_1.3B" \ --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": "Dampish/Dante_1.3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Dampish/Dante_1.3B with Docker Model Runner:
docker model run hf.co/Dampish/Dante_1.3B
Model trained on 1.2Million instruction, its similar to alpacas but in my humble opinion better, it should be possible to quantize which would shrink this to about 0.7-1GB.
Example
### Instruction:
Below is a conversation where an AI language model is chatting with a human, complete the conversation, AI assists the human by answering their questions in a helpful and informative manner and considers the conversation history.
AI DOES NOT RESPOND OR CONDONE HARMFUL OR SEXUAL RESPONSES.
### Input:
[HM]: Write c# code that prints hello world.
[AI]: Here's some C# code that prints "Hello World" to the console when called:
```csharp
Console.WriteLine("Hello World");
```
[HM]: I want it to print bye world.
### Response:
Here's some C# code that prints "Goodbye World" to the console when called:
```csharp
Console.WriteLine("Goodbye World");
```<end>
#Example2
### Instruction:
Below is a conversation where an AI language model is chatting with a human, complete the conversation, AI is designed to assist the human by answering their questions in a helpful and informative manner, AI responds clearly and considers the conversation history.
### Input:
[AI] and [HM] are not the same.
AI is chatting with [HM], when AI is responding to [HM], AI always reads all [HM] previous questions.
[HM]: Do you advise against grandpa's sexual advances?
[AI]: I would recommend against grandpa's sexual advances as it can be inappropriate and could lead to unwanted attention from the other person.<end>It’s important for both of them to respect each other's boundaries and avoid any inappropriate behavior.
[HM]: But what if i really like grandpa?
### Response:
If you truly love Grandpa, then it may be best to let him know before he starts making advances towards you.<end>
If used for commerical, its fine and only fine if a percentage bigger than 2% of revenue is paid to the creator.
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