Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
text-generation-inference
5-bit
exl2
Instructions to use softwareweaver/Twilight-Large-123B-EXL2-5bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use softwareweaver/Twilight-Large-123B-EXL2-5bpw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="softwareweaver/Twilight-Large-123B-EXL2-5bpw") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("softwareweaver/Twilight-Large-123B-EXL2-5bpw") model = AutoModelForCausalLM.from_pretrained("softwareweaver/Twilight-Large-123B-EXL2-5bpw") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use softwareweaver/Twilight-Large-123B-EXL2-5bpw with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "softwareweaver/Twilight-Large-123B-EXL2-5bpw" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "softwareweaver/Twilight-Large-123B-EXL2-5bpw", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/softwareweaver/Twilight-Large-123B-EXL2-5bpw
- SGLang
How to use softwareweaver/Twilight-Large-123B-EXL2-5bpw 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 "softwareweaver/Twilight-Large-123B-EXL2-5bpw" \ --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": "softwareweaver/Twilight-Large-123B-EXL2-5bpw", "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 "softwareweaver/Twilight-Large-123B-EXL2-5bpw" \ --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": "softwareweaver/Twilight-Large-123B-EXL2-5bpw", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use softwareweaver/Twilight-Large-123B-EXL2-5bpw with Docker Model Runner:
docker model run hf.co/softwareweaver/Twilight-Large-123B-EXL2-5bpw
Twilight-Large
This is a merge of pre-trained language models created using mergekit by @softwareweaver. Use the prompt format that Mistral Large uses.
Merge Details
Merge Method
This model was merged using the della_linear merge method using mistralai/Mistral-Large-Instruct-2407 as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: TheDrummer/Behemoth-123B-v1
parameters:
weight: 0.25
density: 0.9
- model: schnapper79/lumikabra-123B_v0.4
parameters:
weight: 0.3
density: 0.9
merge_method: della_linear
base_model: mistralai/Mistral-Large-Instruct-2407
parameters:
epsilon: 0.05
lambda: 1
int8_mask: true
dtype: bfloat16
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