Instructions to use BinksSachary/ShaxxBot2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BinksSachary/ShaxxBot2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BinksSachary/ShaxxBot2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BinksSachary/ShaxxBot2")
model = AutoModelForCausalLM.from_pretrained("BinksSachary/ShaxxBot2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BinksSachary/ShaxxBot2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BinksSachary/ShaxxBot2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BinksSachary/ShaxxBot2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BinksSachary/ShaxxBot2

SGLang

How to use BinksSachary/ShaxxBot2 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 "BinksSachary/ShaxxBot2" \
    --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": "BinksSachary/ShaxxBot2",
		"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 "BinksSachary/ShaxxBot2" \
        --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": "BinksSachary/ShaxxBot2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BinksSachary/ShaxxBot2 with Docker Model Runner:
```
docker model run hf.co/BinksSachary/ShaxxBot2
```

BinksSachary commited on Jun 3, 2021

Commit

f2eccf5

1 Parent(s): e5835fd

Update README.md

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Files changed (1) hide show

README.md +10 -1

README.md CHANGED Viewed

@@ -1,3 +1,10 @@
 from transformers import AutoTokenizer, AutoModelWithLMHead
 tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
@@ -25,4 +32,6 @@ for step in range(4):
     )
     # pretty print last ouput tokens from bot
-    print("JoshuaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

+---
+tags:
+- conversational
+---
+# My Awesome Model
 from transformers import AutoTokenizer, AutoModelWithLMHead
 tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua")
     )
     # pretty print last ouput tokens from bot
+    print("JoshuaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))