Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
Kukedlc/Neural-4-ARC-7B-slerp
Kukedlc/Neural-4-GSM8K-7B-slerp
Eval Results (legacy)
text-generation-inference
Instructions to use Kukedlc/Neural4gsm8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kukedlc/Neural4gsm8k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kukedlc/Neural4gsm8k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Kukedlc/Neural4gsm8k") model = AutoModelForCausalLM.from_pretrained("Kukedlc/Neural4gsm8k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Kukedlc/Neural4gsm8k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kukedlc/Neural4gsm8k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kukedlc/Neural4gsm8k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kukedlc/Neural4gsm8k
- SGLang
How to use Kukedlc/Neural4gsm8k 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 "Kukedlc/Neural4gsm8k" \ --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": "Kukedlc/Neural4gsm8k", "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 "Kukedlc/Neural4gsm8k" \ --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": "Kukedlc/Neural4gsm8k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kukedlc/Neural4gsm8k with Docker Model Runner:
docker model run hf.co/Kukedlc/Neural4gsm8k
Download sample_data/mnist_train_small.csv from Kukedlc/Neural4gsm8k: direct link, hf CLI and curl.
- Browser
- Download file 36.5 MB
-
https://huggingface.co/Kukedlc/Neural4gsm8k/resolve/main/sample_data/mnist_train_small.csv
- Command line
-
hf download hf://Kukedlc/Neural4gsm8k/sample_data/mnist_train_small.csv
-
curl -L -o mnist_train_small.csv https://huggingface.co/Kukedlc/Neural4gsm8k/resolve/main/sample_data/mnist_train_small.csv
36.5 MB
- Xet hash:
- 7abf407e9f8c5c70bc35de579582b5ba02a7ede091389676b21fe7305042e068
- Size of remote file:
- 36.5 MB
- SHA256:
- 1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
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