Instructions to use sosoai/hansoldeco-mixtral-7BX2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sosoai/hansoldeco-mixtral-7BX2 with Transformers:

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

pipe = pipeline("text-generation", model="sosoai/hansoldeco-mixtral-7BX2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sosoai/hansoldeco-mixtral-7BX2")
model = AutoModelForCausalLM.from_pretrained("sosoai/hansoldeco-mixtral-7BX2", 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 sosoai/hansoldeco-mixtral-7BX2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sosoai/hansoldeco-mixtral-7BX2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sosoai/hansoldeco-mixtral-7BX2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sosoai/hansoldeco-mixtral-7BX2

SGLang

How to use sosoai/hansoldeco-mixtral-7BX2 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 "sosoai/hansoldeco-mixtral-7BX2" \
    --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": "sosoai/hansoldeco-mixtral-7BX2",
		"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 "sosoai/hansoldeco-mixtral-7BX2" \
        --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": "sosoai/hansoldeco-mixtral-7BX2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sosoai/hansoldeco-mixtral-7BX2 with Docker Model Runner:
```
docker model run hf.co/sosoai/hansoldeco-mixtral-7BX2
```

hansoldeco-mixtral-7BX2

File size: 1,574 Bytes

---
license: apache-2.0
language:
- ko
- en
tags:
- construction
- interior
- defective
- finished materials
---

주식회사 한솔데코의 공개 도메인 데이터셋을 토큰화 및 dpo 학습한 후, moe를 적용하였습니다.

1. davidkim205/komt-mistral-7b-v1
2. sosoai/hansoldeco-mistral-dpov1


## 실행 예제

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer, GenerationConfig

model_name='sosoai/hansoldeco-mistral-dpo-v1'
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
streamer = TextStreamer(tokenizer)

def gen(x):
    generation_config = GenerationConfig(
        temperature=0.1,
        top_p=0.8,
        top_k=100,
        max_new_tokens=256,
        early_stopping=True,
        do_sample=True,
        repetition_penalty=1.2,
    )
    q = f"[INST]{x} [/INST]"
    gened = model.generate(
        **tokenizer(
            q,
            return_tensors='pt',
            return_token_type_ids=False
        ).to('cuda'),
        generation_config=generation_config,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
        streamer=streamer,
    )
    result_str = tokenizer.decode(gened[0])

    start_tag = f"\n\n### Response: "
    start_index = result_str.find(start_tag)

    if start_index != -1:
        result_str = result_str[start_index + len(start_tag):].strip()
    return result_str

print(gen('마감하자는 어떤 종류가 있나요?'))
```