Instructions to use lmiconsulting/liger-general-medium-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmiconsulting/liger-general-medium-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lmiconsulting/liger-general-medium-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lmiconsulting/liger-general-medium-v1") model = AutoModelForCausalLM.from_pretrained("lmiconsulting/liger-general-medium-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lmiconsulting/liger-general-medium-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmiconsulting/liger-general-medium-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lmiconsulting/liger-general-medium-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lmiconsulting/liger-general-medium-v1
- SGLang
How to use lmiconsulting/liger-general-medium-v1 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 "lmiconsulting/liger-general-medium-v1" \ --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": "lmiconsulting/liger-general-medium-v1", "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 "lmiconsulting/liger-general-medium-v1" \ --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": "lmiconsulting/liger-general-medium-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lmiconsulting/liger-general-medium-v1 with Docker Model Runner:
docker model run hf.co/lmiconsulting/liger-general-medium-v1
Model Card
We provide a low-rank adapter for an instruction-tuned 12B-parameter GPT3-style language model.
Prompting
For instructions that do not require extraneous inputs, the recommended prompt is:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
< -- instruction goes here --- >
### Response:
For instructions that do require extraneous inputs, the recommended prompt is:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
< -- instruction goes here -- >
### Input:
< -- extraneous input goes here -- >
### Response:
Since the model performs causal language modeling, the model's response to the prompt is the text completing the sequence beginning with the prompt.
Instruction-Tuning
This model was instruction-tuned on an internally-curated Alpaca-style dataset.
- Epochs: 3
- Batch size: 128
- Cutoff length: 2048
- Learning rate: 3.2e-5, cosine decay
- LoRA r: 8
- LoRA alpha: 16
- LoRA dropout: 0.0
- LoRA target modules: 'query_key_value', 'dense_h_to_4h', 'dense_4h_to_h', 'dense'
- License: The instruction-tuning data is subject to the Creative Commons 4.0 license.
Base Model
This model was instruction-tuned from a 12B variant from the Pythia family.
- Repository: EleutherAI/pythia-12b
- Paper: arxiv:2304.01373
- License: The base model is subject to the Apache 2.0 license.
- Model type: Transformer-based Language Model
Licensing Information
We release this adapter under the Creative Commons NonCommercial (CC BY-NC 4.0) license.
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