Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
conversational
text-generation-inference
Instructions to use bunnycore/Chimera-Apex-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bunnycore/Chimera-Apex-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bunnycore/Chimera-Apex-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bunnycore/Chimera-Apex-7B") model = AutoModelForCausalLM.from_pretrained("bunnycore/Chimera-Apex-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bunnycore/Chimera-Apex-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bunnycore/Chimera-Apex-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bunnycore/Chimera-Apex-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bunnycore/Chimera-Apex-7B
- SGLang
How to use bunnycore/Chimera-Apex-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 "bunnycore/Chimera-Apex-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bunnycore/Chimera-Apex-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "bunnycore/Chimera-Apex-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bunnycore/Chimera-Apex-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use bunnycore/Chimera-Apex-7B with Docker Model Runner:
docker model run hf.co/bunnycore/Chimera-Apex-7B
Chimera-Apex-7B
Chimera-Apex-7B is an experimental large language model (LLM) created by merging several high-performance models with the goal of achieving exceptional capabilities.
GGUF: https://huggingface.co/mradermacher/Chimera-Apex-7B-GGUF
Tasks:
Due to the inclusion of various models, Chimera-Apex-7B is intended to be a general-purpose model capable of handling a wide range of tasks, including:
- Conversation
- Question Answering
- Code Generation
- (Possibly) NSFW content generation
Limitations:
- As an experimental model, Chimera-Apex-7B's outputs may not always be perfect or accurate.
- The merged models might introduce biases present in their training data.
- It's important to be aware of this limitation when interpreting its outputs.
π§© Configuration
models:
- model: Azazelle/Half-NSFW_Noromaid-7b
- model: Endevor/InfinityRP-v1-7B
- model: FuseAI/FuseChat-7B-VaRM
merge_method: model_stock
base_model: cognitivecomputations/dolphin-2.0-mistral-7b
dtype: bfloat16
Chimera-Apex-7B is a merge of the following models using mergekit:
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