Text Generation
Transformers
Safetensors
Chinese
English
qwen3
qwen
scoring
grading
evaluation
llm-judge
conversational
text-generation-inference
Instructions to use blue-tundra-42/code_and_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blue-tundra-42/code_and_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="blue-tundra-42/code_and_model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("blue-tundra-42/code_and_model") model = AutoModelForCausalLM.from_pretrained("blue-tundra-42/code_and_model") 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
- vLLM
How to use blue-tundra-42/code_and_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "blue-tundra-42/code_and_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "blue-tundra-42/code_and_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/blue-tundra-42/code_and_model
- SGLang
How to use blue-tundra-42/code_and_model 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 "blue-tundra-42/code_and_model" \ --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": "blue-tundra-42/code_and_model", "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 "blue-tundra-42/code_and_model" \ --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": "blue-tundra-42/code_and_model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use blue-tundra-42/code_and_model with Docker Model Runner:
docker model run hf.co/blue-tundra-42/code_and_model
| from .base_dataset import BaseDataset | |
| from .unobench import UnoBenchDataset | |
| # Dataset name to class mapping | |
| DATASET_REGISTRY = { | |
| "UNO-Bench": UnoBenchDataset, | |
| # Add more datasets here, for example: | |
| # "MMMU": MMMUDataset, | |
| } | |
| def get_dataset(dataset_name: str) -> BaseDataset: | |
| """ | |
| Dataset factory function. | |
| :param dataset_name: Registered dataset name (e.g., 'UNO-Bench') | |
| :param split: Dataset split | |
| :return: Instance of BaseDataset | |
| """ | |
| if dataset_name not in DATASET_REGISTRY: | |
| raise ValueError(f"Unknown dataset: {dataset_name}. Available benchmarks: {list(DATASET_REGISTRY.keys())}") | |
| dataset_class = DATASET_REGISTRY[dataset_name] | |
| return dataset_class() |