Instructions to use Badgids/Gonzo-Code-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Badgids/Gonzo-Code-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Badgids/Gonzo-Code-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Badgids/Gonzo-Code-7B") model = AutoModelForCausalLM.from_pretrained("Badgids/Gonzo-Code-7B") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Badgids/Gonzo-Code-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Badgids/Gonzo-Code-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Badgids/Gonzo-Code-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Badgids/Gonzo-Code-7B
- SGLang
How to use Badgids/Gonzo-Code-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 "Badgids/Gonzo-Code-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": "Badgids/Gonzo-Code-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 "Badgids/Gonzo-Code-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": "Badgids/Gonzo-Code-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Badgids/Gonzo-Code-7B with Docker Model Runner:
docker model run hf.co/Badgids/Gonzo-Code-7B
| base_model: | |
| - eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO | |
| - Nondzu/Mistral-7B-Instruct-v0.2-code-ft | |
| - xingyaoww/CodeActAgent-Mistral-7b-v0.1 | |
| - beowolx/MistralHermes-CodePro-7B-v1 | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| license: apache-2.0 | |
| language: | |
| - en | |
| # Gonzo-Code-7B | |
| 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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO) as a base. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [Nondzu/Mistral-7B-Instruct-v0.2-code-ft](https://huggingface.co/Nondzu/Mistral-7B-Instruct-v0.2-code-ft) | |
| * [xingyaoww/CodeActAgent-Mistral-7b-v0.1](https://huggingface.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1) | |
| * [beowolx/MistralHermes-CodePro-7B-v1](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| models: | |
| - model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO | |
| # No parameters necessary for base model | |
| - model: xingyaoww/CodeActAgent-Mistral-7b-v0.1 | |
| parameters: | |
| density: 0.53 | |
| weight: 0.4 | |
| - model: Nondzu/Mistral-7B-Instruct-v0.2-code-ft | |
| parameters: | |
| density: 0.53 | |
| weight: 0.3 | |
| - model: beowolx/MistralHermes-CodePro-7B-v1 | |
| parameters: | |
| density: 0.53 | |
| weight: 0.3 | |
| merge_method: dare_ties | |
| base_model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO | |
| parameters: | |
| int8_mask: true | |
| dtype: bfloat16 | |
| ``` |