Instructions to use junghwannkim/mistral-7b-arc-sci-lora_09161401 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use junghwannkim/mistral-7b-arc-sci-lora_09161401 with Transformers:

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

pipe = pipeline("text-generation", model="junghwannkim/mistral-7b-arc-sci-lora_09161401")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("junghwannkim/mistral-7b-arc-sci-lora_09161401")
model = AutoModelForCausalLM.from_pretrained("junghwannkim/mistral-7b-arc-sci-lora_09161401", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use junghwannkim/mistral-7b-arc-sci-lora_09161401 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "junghwannkim/mistral-7b-arc-sci-lora_09161401"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "junghwannkim/mistral-7b-arc-sci-lora_09161401",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/junghwannkim/mistral-7b-arc-sci-lora_09161401

SGLang

How to use junghwannkim/mistral-7b-arc-sci-lora_09161401 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 "junghwannkim/mistral-7b-arc-sci-lora_09161401" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "junghwannkim/mistral-7b-arc-sci-lora_09161401",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "junghwannkim/mistral-7b-arc-sci-lora_09161401" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "junghwannkim/mistral-7b-arc-sci-lora_09161401",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use junghwannkim/mistral-7b-arc-sci-lora_09161401 with Docker Model Runner:
```
docker model run hf.co/junghwannkim/mistral-7b-arc-sci-lora_09161401
```

ARC 과학 QA 실험 모델

최종 조건: S2000, 학습 seed: 42. 데이터 revision·표본 구성·레시피는 함께 첨부한 experiment_contract.json 및 training_record.json을 참조한다. 데이터 사용 조건과 후보 선정 근거는 dataset_selection.md에 기록했다. 실행하지 않은 후보의 우열은 주장하지 않는다.

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for junghwannkim/mistral-7b-arc-sci-lora_09161401

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(948)

this model