Instructions to use chlee10/T3Q-Platypus-Mistral7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chlee10/T3Q-Platypus-Mistral7B with Transformers:

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

pipe = pipeline("text-generation", model="chlee10/T3Q-Platypus-Mistral7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chlee10/T3Q-Platypus-Mistral7B")
model = AutoModelForCausalLM.from_pretrained("chlee10/T3Q-Platypus-Mistral7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chlee10/T3Q-Platypus-Mistral7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chlee10/T3Q-Platypus-Mistral7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chlee10/T3Q-Platypus-Mistral7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chlee10/T3Q-Platypus-Mistral7B

SGLang

How to use chlee10/T3Q-Platypus-Mistral7B 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 "chlee10/T3Q-Platypus-Mistral7B" \
    --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": "chlee10/T3Q-Platypus-Mistral7B",
		"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 "chlee10/T3Q-Platypus-Mistral7B" \
        --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": "chlee10/T3Q-Platypus-Mistral7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chlee10/T3Q-Platypus-Mistral7B with Docker Model Runner:
```
docker model run hf.co/chlee10/T3Q-Platypus-Mistral7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Update @ 2024.03.07

T3Q-Platypus-Mistral7B

This model is a fine-tuned version of bardsai/jaskier-7b-dpo-v6.1

Model Developers Chihoon Lee(chlee10), T3Q

Training hyperparameters

The following hyperparameters were used during training:

  # 데이터셋과 훈련 횟수와 관련된 하이퍼 파라미터
  batch_size = 16
  num_epochs = 1
  micro_batch = 1
  gradient_accumulation_steps = batch_size // micro_batch
  
  # 훈련 방법에 대한 하이퍼 파라미터
  cutoff_len = 4096
  lr_scheduler = 'cosine'
  warmup_ratio = 0.06 # warmup_steps = 100
  learning_rate = 4e-4
  optimizer = 'adamw_torch'
  weight_decay = 0.01
  max_grad_norm = 1.0
  
  # LoRA config
  lora_r = 16
  lora_alpha = 16
  lora_dropout = 0.05
  lora_target_modules = ["gate_proj", "down_proj", "up_proj"]
  
  # Tokenizer에서 나오는 input값 설정 옵션
  train_on_inputs = False
  add_eos_token = False
  
  # NEFTune params
  noise_alpha: int = 5

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bardsai/jaskier-7b-dpo-v6.1

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