Instructions to use DarwinAnim8or/DailyChat-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DarwinAnim8or/DailyChat-350M with Transformers:

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

pipe = pipeline("text-generation", model="DarwinAnim8or/DailyChat-350M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DarwinAnim8or/DailyChat-350M")
model = AutoModelForCausalLM.from_pretrained("DarwinAnim8or/DailyChat-350M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DarwinAnim8or/DailyChat-350M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarwinAnim8or/DailyChat-350M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DarwinAnim8or/DailyChat-350M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DarwinAnim8or/DailyChat-350M

SGLang

How to use DarwinAnim8or/DailyChat-350M 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 "DarwinAnim8or/DailyChat-350M" \
    --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": "DarwinAnim8or/DailyChat-350M",
		"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 "DarwinAnim8or/DailyChat-350M" \
        --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": "DarwinAnim8or/DailyChat-350M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DarwinAnim8or/DailyChat-350M with Docker Model Runner:
```
docker model run hf.co/DarwinAnim8or/DailyChat-350M
```

DailyChat-350M

A finetuned version of Codegen-350M-nl on the 'daily_dialog' dataset. The idea of this model is to create one that is capable of holding a decent conversation.

Training Procedure

This was trained on Kaggle's servers using 1x NVIDIA P100. This model was trained for 1 epoch with learning rate 1e-2.

Biases & Limitations

This likely contains the same biases and limitations as the original model that it is based on, and additionally heavy biases from the dataset. It can generate offensive input when prompted, so user discretion is advised.

Intended Use

Dialog generation, chat agents.

Downloads last month: 15

Safetensors

Model size

0.4B params

Tensor type

F32

BOOL

DarwinAnim8or
/

DailyChat-350M

DailyChat-350M

Training Procedure

Biases & Limitations

Intended Use

Dataset used to train DarwinAnim8or/DailyChat-350M