Instructions to use bruhzair/prototype-0.4x125-Q6_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bruhzair/prototype-0.4x125-Q6_K with Transformers:

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

pipe = pipeline("text-generation", model="bruhzair/prototype-0.4x125-Q6_K")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bruhzair/prototype-0.4x125-Q6_K")
model = AutoModelForCausalLM.from_pretrained("bruhzair/prototype-0.4x125-Q6_K", 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

llama.cpp

How to use bruhzair/prototype-0.4x125-Q6_K with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bruhzair/prototype-0.4x125-Q6_K:Q6_K
# Run inference directly in the terminal:
llama cli -hf bruhzair/prototype-0.4x125-Q6_K:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bruhzair/prototype-0.4x125-Q6_K:Q6_K
# Run inference directly in the terminal:
llama cli -hf bruhzair/prototype-0.4x125-Q6_K:Q6_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf bruhzair/prototype-0.4x125-Q6_K:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf bruhzair/prototype-0.4x125-Q6_K:Q6_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf bruhzair/prototype-0.4x125-Q6_K:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bruhzair/prototype-0.4x125-Q6_K:Q6_K

Use Docker

docker model run hf.co/bruhzair/prototype-0.4x125-Q6_K:Q6_K

LM Studio
Jan

vLLM

How to use bruhzair/prototype-0.4x125-Q6_K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bruhzair/prototype-0.4x125-Q6_K"
# 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-0.4x125-Q6_K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bruhzair/prototype-0.4x125-Q6_K:Q6_K

SGLang

How to use bruhzair/prototype-0.4x125-Q6_K 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-0.4x125-Q6_K" \
    --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-0.4x125-Q6_K",
		"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-0.4x125-Q6_K" \
        --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-0.4x125-Q6_K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bruhzair/prototype-0.4x125-Q6_K with Ollama:
```
ollama run hf.co/bruhzair/prototype-0.4x125-Q6_K:Q6_K
```

Unsloth Studio

How to use bruhzair/prototype-0.4x125-Q6_K with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for bruhzair/prototype-0.4x125-Q6_K to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for bruhzair/prototype-0.4x125-Q6_K to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bruhzair/prototype-0.4x125-Q6_K to start chatting

Docker Model Runner
How to use bruhzair/prototype-0.4x125-Q6_K with Docker Model Runner:
```
docker model run hf.co/bruhzair/prototype-0.4x125-Q6_K:Q6_K
```

Lemonade

How to use bruhzair/prototype-0.4x125-Q6_K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bruhzair/prototype-0.4x125-Q6_K:Q6_K

Run and chat with the model

lemonade run user.prototype-0.4x125-Q6_K-Q6_K

List all available models

lemonade list

Atomic Chat

prototype-0.4x125

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

Merge Details

Merge Method

This model was merged using the SCE merge method using /workspace/prototype-0.4x122 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--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c
/workspace/cache/models--Envoid--Llama-3-TenyxChat-DaybreakStorywriter-70B/snapshots/2416e680265cfe7818defa218fb8e9fdac04a8c1
/workspace/cache/models--EVA-UNIT-01--EVA-LLaMA-3.33-70B-v0.1/snapshots/7cd63fd3a5519383bfa57bf1f9f2cb008f366f90

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: /workspace/cache/models--Envoid--Llama-3-TenyxChat-DaybreakStorywriter-70B/snapshots/2416e680265cfe7818defa218fb8e9fdac04a8c1
    parameters:
      select_topk: 0.1
  - model: /workspace/cache/models--Sao10K--L3.1-70B-Hanami-x1/snapshots/f054d970fe9119d0237ce97029e6f5b9fce630eb
    parameters:
      select_topk: 0.3
  - model: /workspace/cache/models--tdrussell--Llama-3-70B-Instruct-Storywriter/snapshots/19be2a7c6382a9150e126cf144e2b2964e700d3c
    parameters:
      select_topk: 0.2
  - model: /workspace/cache/models--EVA-UNIT-01--EVA-LLaMA-3.33-70B-v0.1/snapshots/7cd63fd3a5519383bfa57bf1f9f2cb008f366f90
    parameters:
      select_topk: 0.4
  - model: /workspace/prototype-0.4x122
    parameters:
      select_topk: 0.65
base_model: /workspace/prototype-0.4x122
merge_method: sce
tokenizer:
  source: base
chat_template: llama3
int8_mask: true
dtype: bfloat16

Downloads last month: 12

Safetensors

Model size

71B params

Tensor type

BF16

Paper for bruhzair/prototype-0.4x125-Q6_K

FuseChat: Knowledge Fusion of Chat Models

Paper • 2408.07990 • Published Aug 15, 2024 • 15