Instructions to use raincandy-u/TinyChat-1776K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raincandy-u/TinyChat-1776K with Transformers:

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

pipe = pipeline("text-generation", model="raincandy-u/TinyChat-1776K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raincandy-u/TinyChat-1776K")
model = AutoModelForCausalLM.from_pretrained("raincandy-u/TinyChat-1776K", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raincandy-u/TinyChat-1776K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raincandy-u/TinyChat-1776K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raincandy-u/TinyChat-1776K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raincandy-u/TinyChat-1776K

SGLang

How to use raincandy-u/TinyChat-1776K 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 "raincandy-u/TinyChat-1776K" \
    --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": "raincandy-u/TinyChat-1776K",
		"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 "raincandy-u/TinyChat-1776K" \
        --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": "raincandy-u/TinyChat-1776K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raincandy-u/TinyChat-1776K with Docker Model Runner:
```
docker model run hf.co/raincandy-u/TinyChat-1776K
```

raincandy-u commited on Jun 13, 2024

Commit

2b9e602

verified ·

1 Parent(s): 9562ac2

Update README.md

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@@ -1,8 +1,19 @@
 ---
 license: apache-2.0
 widget:
-- text: "<A>We should have a pet. <end>\n<B>I don't think so. <end>\n<A>Why not? <end>\n<B>Because pets make a mess. <end>\n<A>But dogs are cute! <end>\n<B>Cats are cute too. <end>\n<A>We can get a cat then. <end>\n<B>"
   example_title: Sample 1
 ---
 # raincandy-u/TinyChat-1776K

 ---
 license: apache-2.0
 widget:
+- text: |-
+    <A>We should have a pet. <end>
+    <B>I don't think so. <end>
+    <A>Why not? <end>
+    <B>Because pets make a mess. <end>
+    <A>But dogs are cute! <end>
+    <B>Cats are cute too. <end>
+    <A>We can get a cat then. <end>
+    <B>
   example_title: Sample 1
+datasets:
+- raincandy-u/TinyChat
+pipeline_tag: text-generation
 ---
 # raincandy-u/TinyChat-1776K