Instructions to use bruhzair/prototype-0.4x261 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bruhzair/prototype-0.4x261 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bruhzair/prototype-0.4x261")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bruhzair/prototype-0.4x261")
model = AutoModelForCausalLM.from_pretrained("bruhzair/prototype-0.4x261")
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/prototype-0.4x261 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bruhzair/prototype-0.4x261"
# 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/prototype-0.4x261",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bruhzair/prototype-0.4x261

SGLang

How to use bruhzair/prototype-0.4x261 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-0.4x261" \
    --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/prototype-0.4x261",
		"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/prototype-0.4x261" \
        --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/prototype-0.4x261",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bruhzair/prototype-0.4x261 with Docker Model Runner:
```
docker model run hf.co/bruhzair/prototype-0.4x261
```

prototype-0.4x261 / README.md

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metadata

base_model: []
library_name: transformers
tags:
  - mergekit
  - merge

prototype-0.4x261

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Model Stock merge method using /workspace/cache/models--Ppoyaa--MythoNemo-L3.1-70B-v1.0/snapshots/faaa4e992764eb4667b8f541dcf75ce8b7aaadcc as a base.

Models Merged

The following models were included in the merge:

/workspace/cache/models--ArliAI--Llama-3.3-70B-ArliAI-RPMax-v1.4/snapshots/4288519ba279872651e29e430a85c728277cb71b
/workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83
/workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c
/workspace/cache/models--NousResearch--Hermes-3-Llama-3.1-70B/snapshots/1fc86da0ce9cdb14cd775ad270bc7d1b4bf70ede
/workspace/cache/models--watt-ai--watt-tool-70B/snapshots/dbe19344ec6ee4b9e1636e9e6ce24fc6a85a725e

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: /workspace/cache/models--NousResearch--Hermes-3-Llama-3.1-70B/snapshots/1fc86da0ce9cdb14cd775ad270bc7d1b4bf70ede
  - model: /workspace/cache/models--watt-ai--watt-tool-70B/snapshots/dbe19344ec6ee4b9e1636e9e6ce24fc6a85a725e
  - model: /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c
  - model: /workspace/cache/models--ArliAI--Llama-3.3-70B-ArliAI-RPMax-v1.4/snapshots/4288519ba279872651e29e430a85c728277cb71b
  - model: /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83
base_model: /workspace/cache/models--Ppoyaa--MythoNemo-L3.1-70B-v1.0/snapshots/faaa4e992764eb4667b8f541dcf75ce8b7aaadcc
merge_method: model_stock
tokenizer:
  source: base
chat_template: llama3
int8_mask: true
pad_to_multiple_of: 8
dtype: float32