Instructions to use chanwit/flux-base-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chanwit/flux-base-optimized with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chanwit/flux-base-optimized")
model = AutoModelForCausalLM.from_pretrained("chanwit/flux-base-optimized")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chanwit/flux-base-optimized with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chanwit/flux-base-optimized"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chanwit/flux-base-optimized",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chanwit/flux-base-optimized

SGLang

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

Docker Model Runner
How to use chanwit/flux-base-optimized with Docker Model Runner:
```
docker model run hf.co/chanwit/flux-base-optimized
```

Flux-Base-Optimized

flux-base-optimized is the base model for finetuning the series of flux-7b models. It is hierarchical SLERP merged from the following models

mistralai/Mistral-7B-v0.1 (Apache 2.0)
teknium/OpenHermes-2.5-Mistral-7B (Apache 2.0)
Intel/neural-chat-7b-v3-3 (Apache 2.0)
meta-math/MetaMath-Mistral-7B (Apache 2.0)
openchat/openchat-3.5-0106 was openchat/openchat-3.5-1210 (Apache 2.0)

Here's how we did the hierarchical SLERP merge.

                [flux-base-optimized]
                         ↑
                         |
               [stage-1]-+-[openchat]
                   ↑
                   |
         [stage-0]-+-[meta-math]
             ↑
             |
[openhermes]-+-[neural-chat]

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Safetensors

Model size

7B params

Tensor type

BF16