Instructions to use bruhzair/prototype0.4x83 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bruhzair/prototype0.4x83 with Transformers:

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

pipe = pipeline("text-generation", model="bruhzair/prototype0.4x83")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bruhzair/prototype0.4x83")
model = AutoModelForCausalLM.from_pretrained("bruhzair/prototype0.4x83", 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/prototype0.4x83 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bruhzair/prototype0.4x83"
# 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/prototype0.4x83",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bruhzair/prototype0.4x83

SGLang

How to use bruhzair/prototype0.4x83 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/prototype0.4x83" \
    --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/prototype0.4x83",
		"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/prototype0.4x83" \
        --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/prototype0.4x83",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bruhzair/prototype0.4x83 with Docker Model Runner:
```
docker model run hf.co/bruhzair/prototype0.4x83
```

prototype0.4x83

File size: 1,712 Bytes

ce113fd

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

---
# prototype-0.4x83

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--perplexity-ai--r1-1776-distill-llama-70b/snapshots/fd075f491f3056f159984a89bfd5095773e5c911 as a base.

### Models Merged

The following models were included in the merge:
* /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c
* /workspace/cache/models--hitachi-nlp--Llama-3.1-70B-FLDx2/snapshots/051461669991c591aab9e96182b84bdc97733c7f
* /workspace/cache/models--Sao10K--L3.3-70B-Euryale-v2.3/snapshots/e5737724a37ae00926e95acf663ca73d430dc8ad

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: /workspace/cache/models--Sao10K--L3.3-70B-Euryale-v2.3/snapshots/e5737724a37ae00926e95acf663ca73d430dc8ad
  - model: /workspace/cache/models--hitachi-nlp--Llama-3.1-70B-FLDx2/snapshots/051461669991c591aab9e96182b84bdc97733c7f
  - model: /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c
  - model: /workspace/cache/models--perplexity-ai--r1-1776-distill-llama-70b/snapshots/fd075f491f3056f159984a89bfd5095773e5c911
base_model: /workspace/cache/models--perplexity-ai--r1-1776-distill-llama-70b/snapshots/fd075f491f3056f159984a89bfd5095773e5c911
merge_method: model_stock
tokenizer:
  source: union
int8_mask: true
dtype: float32
out_dtype: bfloat16
```