Instructions to use axxd/wizardllama-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use axxd/wizardllama-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="axxd/wizardllama-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("axxd/wizardllama-7b") model = AutoModelForCausalLM.from_pretrained("axxd/wizardllama-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use axxd/wizardllama-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "axxd/wizardllama-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "axxd/wizardllama-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/axxd/wizardllama-7b
- SGLang
How to use axxd/wizardllama-7b 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 "axxd/wizardllama-7b" \ --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": "axxd/wizardllama-7b", "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 "axxd/wizardllama-7b" \ --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": "axxd/wizardllama-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use axxd/wizardllama-7b with Docker Model Runner:
docker model run hf.co/axxd/wizardllama-7b
| base_model: | |
| - codellama/CodeLlama-7b-Python-hf | |
| - WizardLM/WizardCoder-Python-7B-V1.0 | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| - code | |
| license: apache-2.0 | |
| # merge | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the SLERP merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | |
| * [WizardLM/WizardCoder-Python-7B-V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| slices: | |
| - sources: | |
| - model: WizardLM/WizardCoder-Python-7B-V1.0 | |
| layer_range: [0, 32] | |
| - model: codellama/CodeLlama-7b-Python-hf | |
| layer_range: [0, 32] | |
| merge_method: slerp | |
| base_model: codellama/CodeLlama-7b-Python-hf | |
| parameters: | |
| t: | |
| - filter: self_attn | |
| value: [0, 0.5, 0.3, 0.7, 1] | |
| - filter: mlp | |
| value: [1, 0.5, 0.7, 0.3, 0] | |
| - value: 0.5 | |
| dtype: bfloat16 | |
| ``` |