Instructions to use alkahestry/pygmalion-6b-chaicomp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alkahestry/pygmalion-6b-chaicomp with Transformers:

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

pipe = pipeline("text-generation", model="alkahestry/pygmalion-6b-chaicomp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alkahestry/pygmalion-6b-chaicomp")
model = AutoModelForCausalLM.from_pretrained("alkahestry/pygmalion-6b-chaicomp")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alkahestry/pygmalion-6b-chaicomp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alkahestry/pygmalion-6b-chaicomp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alkahestry/pygmalion-6b-chaicomp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alkahestry/pygmalion-6b-chaicomp

SGLang

How to use alkahestry/pygmalion-6b-chaicomp 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 "alkahestry/pygmalion-6b-chaicomp" \
    --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": "alkahestry/pygmalion-6b-chaicomp",
		"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 "alkahestry/pygmalion-6b-chaicomp" \
        --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": "alkahestry/pygmalion-6b-chaicomp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alkahestry/pygmalion-6b-chaicomp with Docker Model Runner:
```
docker model run hf.co/alkahestry/pygmalion-6b-chaicomp
```

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Check out the documentation for more information.

Model Details

I finetuned PygmalionAI/pygmalion-6b with QLora for 24 hours on 250k samples. Collected from SODA and Teacher GPT dataset. My first attempt on making LLM model as an entry to Chai competition.

Model Description

Model type: Chatbot
Finetuned from model : PygmalionAI/pygmalion-6b

Model Sources

Pygmalion-6b: https://huggingface.co/PygmalionAI/pygmalion-6b

Training Details

Training Data

For the training data I use 20% of SODA dadtaset mixed with TeacherGPT roleplay dataset.

Training Procedure

The model was trained for 24 hours on RTX4090.

Training Hyperparameters

Training param

batch_size = 128,
micro_batch_size = 4,
num_epochs = 1,
learning_rate = 3e-4,
cutoff_len = 512,
val_set_size = 0
finetune method

finetune_method = "qlora"
prefix tuning hyperparams

num_virtual_tokens = 32
lora hyperparams

lora_r = 16,
lora_alpha = 16,
lora_dropout = 0.05,
lora_target_modules = "q_proj k_proj v_proj"
llm hyperparams

bf16 = False,
load_in_8bit = False,
group_by_length = False ,
resume_from_checkpoint = None

Results

Me: Hi Nathan, how are you doing today
Nathan: I'm fine...
Me: Then tell me about your day.
Nathan:

It was good. We had a lot of fun in school and then we went to the park afterwards.

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