Instructions to use satvikag/chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use satvikag/chatbot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="satvikag/chatbot") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("satvikag/chatbot") model = AutoModelForCausalLM.from_pretrained("satvikag/chatbot") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use satvikag/chatbot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "satvikag/chatbot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "satvikag/chatbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/satvikag/chatbot
- SGLang
How to use satvikag/chatbot 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 "satvikag/chatbot" \ --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": "satvikag/chatbot", "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 "satvikag/chatbot" \ --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": "satvikag/chatbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use satvikag/chatbot with Docker Model Runner:
docker model run hf.co/satvikag/chatbot
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
tags:
|
| 3 |
+
- conversational
|
| 4 |
+
license: mit
|
| 5 |
+
---
|
| 6 |
+
# DialoGPT Trained on the Speech of a Game Character
|
| 7 |
+
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
|
| 8 |
+
Chat with the model:
|
| 9 |
+
```python
|
| 10 |
+
tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-small')
|
| 11 |
+
model = AutoModelWithLMHead.from_pretrained('output-small')
|
| 12 |
+
|
| 13 |
+
# Let's chat for 5 lines
|
| 14 |
+
for step in range(100):
|
| 15 |
+
# encode the new user input, add the eos_token and return a tensor in Pytorch
|
| 16 |
+
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
|
| 17 |
+
# print(new_user_input_ids)
|
| 18 |
+
|
| 19 |
+
# append the new user input tokens to the chat history
|
| 20 |
+
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
|
| 21 |
+
|
| 22 |
+
# generated a response while limiting the total chat history to 1000 tokens,
|
| 23 |
+
chat_history_ids = model.generate(
|
| 24 |
+
bot_input_ids, max_length=500,
|
| 25 |
+
pad_token_id=tokenizer.eos_token_id,
|
| 26 |
+
no_repeat_ngram_size=3,
|
| 27 |
+
do_sample=True,
|
| 28 |
+
top_k=100,
|
| 29 |
+
top_p=0.7,
|
| 30 |
+
temperature = 0.8
|
| 31 |
+
)
|
| 32 |
+
|
| 33 |
+
# pretty print last ouput tokens from bot
|
| 34 |
+
print("AI: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
|
| 35 |
+
```
|