Instructions to use flytech/Ruckus-PyAssi-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flytech/Ruckus-PyAssi-13b with Transformers:

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

pipe = pipeline("text-generation", model="flytech/Ruckus-PyAssi-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("flytech/Ruckus-PyAssi-13b")
model = AutoModelForCausalLM.from_pretrained("flytech/Ruckus-PyAssi-13b", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use flytech/Ruckus-PyAssi-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flytech/Ruckus-PyAssi-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flytech/Ruckus-PyAssi-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/flytech/Ruckus-PyAssi-13b

SGLang

How to use flytech/Ruckus-PyAssi-13b 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 "flytech/Ruckus-PyAssi-13b" \
    --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": "flytech/Ruckus-PyAssi-13b",
		"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 "flytech/Ruckus-PyAssi-13b" \
        --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": "flytech/Ruckus-PyAssi-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use flytech/Ruckus-PyAssi-13b with Docker Model Runner:
```
docker model run hf.co/flytech/Ruckus-PyAssi-13b
```

Ruckus-PyAssi-13b / README.md

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	---
	base_model: meta-llama/Llama-2-13b-hf
	tags:
	- generated_from_trainer
	model-index:
	- name: Ruckus-PyAssi-13b
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# Ruckus-PyAssi-13b

	This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)
	on a 10 000 examples from flytech/llama-python-codes-30k dataset.

	## Model description

	Model trained in 4-bit architecture using SFT (Supervised Fine Tuning) and LoRA (Low-Rank Adaptation) methods,
	fine-tuning further is possible.

	## Intended uses & limitations

	Code-generation, but as like all Ruckus models
	- Created to serve as an executional layer
	- Rich in Python codes and instructional tasks
	- Specially formatted for chat (see inference)

	## Training procedure

	Model was being trained for 13 hours of A6000 single 48GB vRAM GPU

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 0.0002
	- train_batch_size: 32
	- eval_batch_size: 32 * 2
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: constant
	- num_epochs: 5

	## Inference

	- Make sure to format your prompt:
	[INST]This is my prompt[/INST]

	[INST]Ruckus, open google[/INST]

	### Framework versions

	- Transformers 4.34.0
	- Pytorch 2.0.1+cu118
	- Datasets 2.14.5
	- Tokenizers 0.14.1