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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

TinyChat-1776K / README.md

raincandy-u

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metadata

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

A tiny LM trained on TinyChat dataset from scratch.

The aim is to try to achieve natural responses on the smallest possible model. Trained using a dataset of 3 year old children level English conversations.

Note: It has no world knowledge, so you should not ask it any intellectual questions.

Model Spec

config = AutoConfig.for_model(
    model_type="llama",
    hidden_size=192,
    intermediate_size=640,
    num_attention_heads=16,
    num_hidden_layers=3,
    num_key_value_heads=4,
    tie_word_embeddings=True,
    vocab_size=2048,
    max_position_embeddings=256
)

Template

<A>Hi, Tom. How are you? <end>
<B>I'm fine, thank you. And you? <end>
<A>Fine. What's your favorite color? <end>
<B>My favorite color is black. <end>
<A>Do you like cats? <end>
<B>

Example output:

Yes, I do. I like it too. They are good for me.

Generation Param

top_k=40,
top_p=0.8,
temperature=1