Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
llama
Generated from Trainer
text-generation-inference
Instructions to use flytech/Ruckus-PyAssi-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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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
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