Instructions to use solidrust/Falkor-7b-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use solidrust/Falkor-7b-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="solidrust/Falkor-7b-AWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("solidrust/Falkor-7b-AWQ") model = AutoModelForCausalLM.from_pretrained("solidrust/Falkor-7b-AWQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use solidrust/Falkor-7b-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "solidrust/Falkor-7b-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/Falkor-7b-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/solidrust/Falkor-7b-AWQ
- SGLang
How to use solidrust/Falkor-7b-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/Falkor-7b-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/Falkor-7b-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/Falkor-7b-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/Falkor-7b-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use solidrust/Falkor-7b-AWQ with Docker Model Runner:
docker model run hf.co/solidrust/Falkor-7b-AWQ
Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array
perlthoughts/Falkor-7b AWQ
- Model creator: perlthoughts
- Original model: Falkor-7b
Model summary
dragon-mistral-7b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Mistral-7B base model.
DRAGON models have been fine-tuned with the specific objective of fact-based question-answering over complex business and legal documents with an emphasis on reducing hallucinations and providing short, clear answers for workflow automation.
- Downloads last month
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Collection including solidrust/Falkor-7b-AWQ
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.260
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.840
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard63.980
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard63.080
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard80.350
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard60.500