Text Generation
Transformers
Safetensors
English
mistral
4-bit precision
AWQ
text-generation-inference
awq
Instructions to use solidrust/Garbage_9B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use solidrust/Garbage_9B-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="solidrust/Garbage_9B-AWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("solidrust/Garbage_9B-AWQ") model = AutoModelForCausalLM.from_pretrained("solidrust/Garbage_9B-AWQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use solidrust/Garbage_9B-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "solidrust/Garbage_9B-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "solidrust/Garbage_9B-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/solidrust/Garbage_9B-AWQ
- SGLang
How to use solidrust/Garbage_9B-AWQ 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 "solidrust/Garbage_9B-AWQ" \ --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": "solidrust/Garbage_9B-AWQ", "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 "solidrust/Garbage_9B-AWQ" \ --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": "solidrust/Garbage_9B-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use solidrust/Garbage_9B-AWQ with Docker Model Runner:
docker model run hf.co/solidrust/Garbage_9B-AWQ
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README.md
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library_name: transformers
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tags:
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- 4-bit
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- AWQ
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- Model creator: [jeiku](https://huggingface.co/jeiku)
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- Original model: [Garbage_9B](https://huggingface.co/jeiku/Garbage_9B)
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## How to use
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---
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base_model:
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- ChaoticNeutrals/InfinityNexus_9B
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- jeiku/luna_lora_9B
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library_name: transformers
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license: apache-2.0
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datasets:
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- ResplendentAI/Luna_Alpaca
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language:
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- en
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tags:
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- 4-bit
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- AWQ
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- Model creator: [jeiku](https://huggingface.co/jeiku)
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- Original model: [Garbage_9B](https://huggingface.co/jeiku/Garbage_9B)
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## Model Summary
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This is a finetune of InfinityNexus_9B. This is my first time tuning a frankenmerge, so hopefully it works out. The goal is to improve intelligence and RP ability beyond the 7B original models.
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## How to use
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