Instructions to use behzatindustries/BehzatOne-8B-A1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use behzatindustries/BehzatOne-8B-A1B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="behzatindustries/BehzatOne-8B-A1B",
	filename="gguf/BehzatOne-8B-A1B-v107.BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use behzatindustries/BehzatOne-8B-A1B 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 behzatindustries/BehzatOne-8B-A1B:BF16
# Run inference directly in the terminal:
llama cli -hf behzatindustries/BehzatOne-8B-A1B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf behzatindustries/BehzatOne-8B-A1B:BF16
# Run inference directly in the terminal:
llama cli -hf behzatindustries/BehzatOne-8B-A1B:BF16

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 behzatindustries/BehzatOne-8B-A1B:BF16
# Run inference directly in the terminal:
./llama-cli -hf behzatindustries/BehzatOne-8B-A1B:BF16

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 behzatindustries/BehzatOne-8B-A1B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf behzatindustries/BehzatOne-8B-A1B:BF16

Use Docker

docker model run hf.co/behzatindustries/BehzatOne-8B-A1B:BF16

LM Studio
Jan

vLLM

How to use behzatindustries/BehzatOne-8B-A1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "behzatindustries/BehzatOne-8B-A1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "behzatindustries/BehzatOne-8B-A1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/behzatindustries/BehzatOne-8B-A1B:BF16

Ollama
How to use behzatindustries/BehzatOne-8B-A1B with Ollama:
```
ollama run hf.co/behzatindustries/BehzatOne-8B-A1B:BF16
```

Unsloth Studio

How to use behzatindustries/BehzatOne-8B-A1B 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 behzatindustries/BehzatOne-8B-A1B 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 behzatindustries/BehzatOne-8B-A1B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for behzatindustries/BehzatOne-8B-A1B to start chatting

Atomic Chat new
Docker Model Runner
How to use behzatindustries/BehzatOne-8B-A1B with Docker Model Runner:
```
docker model run hf.co/behzatindustries/BehzatOne-8B-A1B:BF16
```

Lemonade

How to use behzatindustries/BehzatOne-8B-A1B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull behzatindustries/BehzatOne-8B-A1B:BF16

Run and chat with the model

lemonade run user.BehzatOne-8B-A1B-BF16

List all available models

lemonade list

BehzatOne-8B-A1B v109

v109 GGUF conversion and Ollama release built from the latest fixed repo Modelfile.

Version Lineage

v105: ~109k-sample release with SFT plus anti-hallucination DPO.
v106: agentic SFT plus DPO release.
v107: merge release built from the v105 BF16 base plus the current v106 SFT and DPO adapters.
v108: targeted agentic-format repair release on top of v107, focused on exact patch/file/JSON output and anti-schema-regurgitation.

What changed

Merged BF16 weights for this release.
GGUF artifacts generated for both BF16 and Q4_K_M.
Ollama Modelfiles included for BF16 and Q4 deployment.
See the release summary above for the training focus of this version.

Artifacts

/v109/ - merged BF16 HF model shards
gguf/BehzatOne-8B-A1B-v109.BF16.gguf - BF16 GGUF
gguf/BehzatOne-8B-A1B-v109.Q4_K_M.gguf - Q4_K_M GGUF
Modelfile.v109-bf16
Modelfile.v109-q4
Modelfile.v109-agent-q4

Status

Evaluation for this release should be run after upload. Previous release numbers are not reused here.

Usage

For Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "behzatindustries/BehzatOne-8B-A1B",
    subfolder="v109",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).cuda()
tokenizer = AutoTokenizer.from_pretrained(
    "behzatindustries/BehzatOne-8B-A1B",
    subfolder="v109",
    trust_remote_code=True,
)

Downloads last month: 1,804

GGUF

Model size

8B params

Architecture

lfm2moe

Hardware compatibility

4-bit

16-bit

Model tree for behzatindustries/BehzatOne-8B-A1B

Base model

LiquidAI/LFM2.5-8B-A1B-Base

Finetuned

LiquidAI/LFM2.5-8B-A1B

Quantized

(59)

this model