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
metadata
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.
Merge Details
Merge Method
This model was merged using the DARE TIES merge method using 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
- xingyaoww/CodeActAgent-Mistral-7b-v0.1
- beowolx/MistralHermes-CodePro-7B-v1
Configuration
The following YAML configuration was used to produce this model:
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