Instructions to use bruhzair/Command-t4-111b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bruhzair/Command-t4-111b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bruhzair/Command-t4-111b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bruhzair/Command-t4-111b") model = AutoModelForCausalLM.from_pretrained("bruhzair/Command-t4-111b") 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 Settings
- vLLM
How to use bruhzair/Command-t4-111b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bruhzair/Command-t4-111b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bruhzair/Command-t4-111b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bruhzair/Command-t4-111b
- SGLang
How to use bruhzair/Command-t4-111b 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 "bruhzair/Command-t4-111b" \ --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": "bruhzair/Command-t4-111b", "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 "bruhzair/Command-t4-111b" \ --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": "bruhzair/Command-t4-111b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use bruhzair/Command-t4-111b with Docker Model Runner:
docker model run hf.co/bruhzair/Command-t4-111b
Test1
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the NuSLERP merge method.
Models Merged
The following models were included in the merge:
- /workspace/cache/models--TheDrummer--Fallen-Command-A-111B-v1/snapshots/5d2b4bdb35d7dff3a4eb51a5f2b231ba27943491
- /workspace/cache/models--CohereForAI--c4ai-command-a-03-2025/snapshots/6894b671d755c72573bb1a5722cfcfcd86b42b01
Configuration
The following YAML configuration was used to produce this model:
dtype: bfloat16
merge_method: nuslerp
modules:
default:
slices:
- sources:
- layer_range: [0, 64]
model: /workspace/cache/models--CohereForAI--c4ai-command-a-03-2025/snapshots/6894b671d755c72573bb1a5722cfcfcd86b42b01
parameters:
weight: [0.85, 0.8, 0.9, 0.95, 0.9, 0.8, 0.85]
- layer_range: [0, 64]
model: /workspace/cache/models--TheDrummer--Fallen-Command-A-111B-v1/snapshots/5d2b4bdb35d7dff3a4eb51a5f2b231ba27943491
parameters:
weight: [0.15, 0.2, 0.1, 0.05, 0.1, 0.2, 0.15]
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docker model run hf.co/bruhzair/Command-t4-111b