Instructions to use bruhzair/proxy2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bruhzair/proxy2 with Transformers:

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

pipe = pipeline("text-generation", model="bruhzair/proxy2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bruhzair/proxy2")
model = AutoModelForCausalLM.from_pretrained("bruhzair/proxy2", device_map="auto")
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/proxy2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bruhzair/proxy2"
# 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/proxy2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bruhzair/proxy2

SGLang

How to use bruhzair/proxy2 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/proxy2" \
    --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/proxy2",
		"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/proxy2" \
        --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/proxy2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bruhzair/proxy2 with Docker Model Runner:
```
docker model run hf.co/bruhzair/proxy2
```

proxy2 / README.md

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metadata

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

proxy2

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

Merge Details

Merge Method

This model was merged using the Multi-SLERP merge method using /workspace/author-base3 as a base.

Models Merged

The following models were included in the merge:

/workspace/cache/models--Steelskull--L3.3-Electra-R1-70b/snapshots/26c8d595ecd941ca908c49d7ae5b2dd146465341
/workspace/cache/models--ArliAI--Llama-3.3-70B-ArliAI-RPMax-v1.4/snapshots/4288519ba279872651e29e430a85c728277cb71b
/workspace/cache/models--Sao10K--Llama-3.3-70B-Vulpecula-r1/snapshots/12d7254ab9a5ce21905f59f341a3d2a2b3e62fd5

Configuration

The following YAML configuration was used to produce this model:

base_model: /workspace/author-base3
models:
  - model: /workspace/author-base3
  - model: /workspace/cache/models--Sao10K--Llama-3.3-70B-Vulpecula-r1/snapshots/12d7254ab9a5ce21905f59f341a3d2a2b3e62fd5
    parameters:
      weight: [0.05, 0.25, 0.45, 0.65, 0.85, 0.65, 0.45, 0.25, 0.05]
  - model: /workspace/cache/models--ArliAI--Llama-3.3-70B-ArliAI-RPMax-v1.4/snapshots/4288519ba279872651e29e430a85c728277cb71b
    parameters:
      weight: [0.05, 0.25, 0.45, 0.60, 0.45, 0.25, 0.05]
  - model: /workspace/cache/models--Steelskull--L3.3-Electra-R1-70b/snapshots/26c8d595ecd941ca908c49d7ae5b2dd146465341
    parameters:
      weight: [0.05, 0.2, 0.3, 0.35, 0.4, 0.35, 0.3, 0.2, 0.05]
merge_method: multislerp
parameters:
  normalize: false
  eps: 1e-8
tokenizer:
  source: union
int8_mask: true
dtype: bfloat16