Instructions to use bruhzair/prototype-0.4x110 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bruhzair/prototype-0.4x110 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bruhzair/prototype-0.4x110")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bruhzair/prototype-0.4x110") model = AutoModelForCausalLM.from_pretrained("bruhzair/prototype-0.4x110", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bruhzair/prototype-0.4x110 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bruhzair/prototype-0.4x110" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bruhzair/prototype-0.4x110", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bruhzair/prototype-0.4x110
- SGLang
How to use bruhzair/prototype-0.4x110 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/prototype-0.4x110" \ --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": "bruhzair/prototype-0.4x110", "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 "bruhzair/prototype-0.4x110" \ --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": "bruhzair/prototype-0.4x110", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bruhzair/prototype-0.4x110 with Docker Model Runner:
docker model run hf.co/bruhzair/prototype-0.4x110
prototype-0.4x110
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Passthrough merge method.
Models Merged
The following models were included in the merge:
- /workspace/prototype-0.4x109
Configuration
The following YAML configuration was used to produce this model:
dtype: float32
out_dtype: bfloat16
merge_method: passthrough
int8_mask: true
modules:
default:
slices:
- sources:
- layer_range: [0, 4]
model: /workspace/prototype-0.4x109
- sources:
- layer_range: [2, 4]
model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [4, 8]
model: /workspace/prototype-0.4x109
- sources:
- layer_range: [6, 8]
model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 12]
model: /workspace/prototype-0.4x109
- sources:
- layer_range: [10, 12]
model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [12, 16]
model: /workspace/prototype-0.4x109
- sources:
- layer_range: [14, 16]
model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [16, 20]
model: /workspace/prototype-0.4x109
- sources:
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model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [20, 24]
model: /workspace/prototype-0.4x109
- sources:
- layer_range: [22, 24]
model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 28]
model: /workspace/prototype-0.4x109
- sources:
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model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [28, 32]
model: /workspace/prototype-0.4x109
- sources:
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model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [32, 36]
model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
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- layer_range: [36, 40]
model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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value: 0.0
- filter: down_proj
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- value: 1.0
- sources:
- layer_range: [44, 48]
model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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value: 0.0
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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value: 0.0
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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- filter: down_proj
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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- filter: down_proj
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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model: /workspace/prototype-0.4x109
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model: /workspace/prototype-0.4x109
parameters:
scale:
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- value: 1.0
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