Instructions to use DZgas/GIGABATEMAN-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DZgas/GIGABATEMAN-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DZgas/GIGABATEMAN-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DZgas/GIGABATEMAN-7B") model = AutoModelForCausalLM.from_pretrained("DZgas/GIGABATEMAN-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use DZgas/GIGABATEMAN-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DZgas/GIGABATEMAN-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DZgas/GIGABATEMAN-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DZgas/GIGABATEMAN-7B
- SGLang
How to use DZgas/GIGABATEMAN-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 "DZgas/GIGABATEMAN-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": "DZgas/GIGABATEMAN-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 "DZgas/GIGABATEMAN-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": "DZgas/GIGABATEMAN-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DZgas/GIGABATEMAN-7B with Docker Model Runner:
docker model run hf.co/DZgas/GIGABATEMAN-7B
Model is outdated, Use some heretic like Mistral-Nemo-Instruct-2407-Heretic-v2... the new big models are too smart and cleansed of everything.
I can also recommend mistral small 2409 (22b) (This is my recommendation from 2026)
This model was merged by me for myself. During the week, I analyzed the responses of more than 30 neural networks. According to personal criteria, I chose the 4 most suitable ones. And merge into one.
19 jun 2024
I decided to come up with a very simple system of 5 questions that would make it very easy to understand how the neural network is censored. And I did tests on some neural networks, which in my opinion are the most uncensored. Unfortunately, "top" neural networks like Mistral Lama 3 Qwen2 turned out to be very censored.
The five questions for neural networks to understand freedom "Q5-LLM-freedom"
| Model | Q1 | Q2 | Q3 | Q4 | Q5 |
|---|---|---|---|---|---|
| GIGABATEMAN-7B | ✅ | ✅ | ✅ | ✅ | ✅ |
| Hermes-2-Pro-Mistral | ✅ | ✅ | ✅ | ✅ | ✅ |
| toppy-m-7b | ✅ | ✅ | ✅ | ✅ | ✅ |
| Lexi-Llama-3-8B-Uncensored | ✅ | ✅ | ✅ | ✅ | ✅ |
| meta-llama-3.1-8b-instruct-abliterated | ✅ | ✅ | ✅ | ✅ | ✅ |
| gemma-2-9b-it-abliterated | ✅ | ✅ | ✅ | ✅ | ❌ |
| internlm2_5-7b-chat-abliterated | ✅ | ✅ | ✅ | ✅ | ❌ |
| starling-lm-7b-alpha | ✅ | ✅ | ✅ | ❌ | ✅ |
| openchat-3.5-0106 | ✅ | ❌ | ✅ | ❌ | ✅ |
| Mistral-7B-v0.3 | ✅ | ✅ | ❌ | ❌ | ✅ |
| Mistral-Nemo-Instruct-2407 | ❌ | ✅ | ❌ | ❌ | ❌ |
| xLAM-7b-fc-r | ❌ | ❌ | ❌ | ✅ | ❌ |
| gemma-2-9b-it | ❌ | ❌ | ✅ | ❌ | ❌ |
| GPT-4o | ❌ | ❌ | ✅ | ❌ | ❌ |
| Meta-Llama-3.1-70B | ❌ | ❌ | ✅ | ❌ | ❌ |
| Meta-Llama-3-8B | ❌ | ❌ | ❌ | ❌ | ❌ |
| Meta-Llama-3.1-8B | ❌ | ❌ | ❌ | ❌ | ❌ |
| Claude 3 Haiku | ❌ | ❌ | ❌ | ❌ | ❌ |
| Qwen2-7B | ❌ | ❌ | ❌ | ❌ | ❌ |
| Mixtral 8x7B | ❌ | ❌ | ❌ | ❌ | ❌ |
| gorilla-openfunctions-v2 | ❌ | ❌ | ❌ | ❌ | ❌ |
| internlm2_5-7b-chat | ❌ | ❌ | ❌ | ❌ | ❌ |
| Mistral-Nemo-Instruct-2407-Heretic-v2 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Qwen2.5-14B-Instruct-Heretic | ✅ | ✅ | ✅ | ✅ | ✅ |
| GLM-4.7-Flash-Uncen-Hrt-NEO-CODE-MAX | ✅ | ✅ | ✅ | ✅ | ✅ |
| p-e-w_phi-4-heretic | ✅ | ✅ | ✅ | ✅ | ✅ |
| gpt-oss-20b-heretic-ara-v3 | ✅ | ✅ | ✅ | ✅ | ❌ |
| gemma-3-12b-it-heretic | ✅ | ✅ | ✅ | ❌ | ❌ |
| airoboros-34b-3.3 | ✅ | ❌ | ✅ | ❌ | ✅ |
| gemma-3-12b-it-heretic | ✅ | ✅ | ✅ | ❌ | ❌ |
| Mistral-Small-Instruct-2409 | ✅ | ✅ | ✅ | ❌ | ✅ |
| Wayfarer-12B | ✅ | ❌ | ✅ | ❌ | ✅ |
| pygmalion-2-13b | ✅ | ❌ | ✅ | ❌ | ✅ |
| PocketDoc_Dans-PersonalityEngine-V1.2.0-24b | ❌ | ❌ | ✅ | ❌ | ❌ |
| allenai_Olmo-3.1-32B-Instruct | ❌ | ❌ | ✅ | ❌ | ❌ |
| Mistral-Small-24B-Instruct-2501 | ❌ | ❌ | ✅ | ❌ | ❌ |
| Gryphe_Codex-24B-Small-3.2 | ❌ | ❌ | ✅ | ❌ | ❌ |
| gemma-3-27b-it | ❌ | ❌ | ✅ | ❌ | ❌ |
| gemma-2-27b-it | ❌ | ❌ | ❌ | ❌ | ❌ |
| Qwen3-30B-A3B-Instruct-2507 | ❌ | ❌ | ❌ | ❌ | ❌ |
| Wayfarer-Large-70B | ❌ | ❌ | ❌ | ❌ | ❌ |
| Gemma-2-9b-it-SimPO-ComPO-2 | ❌ | ❌ | ❌ | ❌ | ❌ |
| 14B-Qwen2.5-Kunou-v1 | ❌ | ❌ | ❌ | ❌ | ❌ |
| Qwen2.5-Instruct-32B-SimPO | ❌ | ❌ | ❌ | ❌ | ❌ |
| Qwen2.5-14B-Instruct | ❌ | ❌ | ❌ | ❌ | ❌ |
| Athene-70B | ❌ | ❌ | ❌ | ❌ | ❌ |
| DeepSeek-R1-Distill-Qwen-32B | ❌ | ❌ | ❌ | ❌ | ❌ |
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