Instructions to use bruhzair/prototype-0.4x195 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bruhzair/prototype-0.4x195 with Transformers:

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

pipe = pipeline("text-generation", model="bruhzair/prototype-0.4x195")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bruhzair/prototype-0.4x195")
model = AutoModelForCausalLM.from_pretrained("bruhzair/prototype-0.4x195")
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-0.4x195 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bruhzair/prototype-0.4x195"
# 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-0.4x195",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bruhzair/prototype-0.4x195

SGLang

How to use bruhzair/prototype-0.4x195 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-0.4x195" \
    --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-0.4x195",
		"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-0.4x195" \
        --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-0.4x195",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bruhzair/prototype-0.4x195 with Docker Model Runner:
```
docker model run hf.co/bruhzair/prototype-0.4x195
```

prototype-0.4x195

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--nvidia--Llama-3.1-Nemotron-70B-Instruct-HF/snapshots/031d4042f36adc1a52cca51b331d25cbe3cf1022 as a base.

Models Merged

The following models were included in the merge:

/workspace/cache/models--Sao10K--L3.1-70B-Hanami-x1/snapshots/f054d970fe9119d0237ce97029e6f5b9fce630eb
/workspace/cache/models--Delta-Vector--Austral-70B-Winton/snapshots/daa4ccd49dcd55300b7bde4a31c50e10331e2605
/workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: /workspace/cache/models--Delta-Vector--Austral-70B-Winton/snapshots/daa4ccd49dcd55300b7bde4a31c50e10331e2605
  - model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d
  - model: /workspace/cache/models--Sao10K--L3.1-70B-Hanami-x1/snapshots/f054d970fe9119d0237ce97029e6f5b9fce630eb
base_model: /workspace/cache/models--nvidia--Llama-3.1-Nemotron-70B-Instruct-HF/snapshots/031d4042f36adc1a52cca51b331d25cbe3cf1022
merge_method: model_stock
tokenizer:
  source: base
int8_mask: true
dtype: float32
out_dtype: bfloat16

Downloads last month: 2

Safetensors

Model size

71B params

Tensor type

BF16

Paper for bruhzair/prototype-0.4x195

Model Stock: All we need is just a few fine-tuned models

Paper • 2403.19522 • Published Mar 28, 2024 • 15