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 Settings
- 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
| # models/base.py | |
| from abc import ABC, abstractmethod | |
| from typing import List | |
| class BaseModel(ABC): | |
| """ | |
| Abstract base class for all model implementations. | |
| """ | |
| def __init__(self, model_name: str, model_path: str, **kwargs): | |
| """ | |
| Initialize the model. | |
| :param model_path: Model path or API name (e.g., 'qwen-turbo', '/path/to/local/model') | |
| :param kwargs: Other parameters such as api_key, temperature, etc. | |
| """ | |
| self.model_name = model_name | |
| self.model_path = model_path | |
| self.kwargs = kwargs | |
| def load_model(self): | |
| pass | |
| def generate(self, prompt: str) -> str: | |
| """ | |
| Generate response for a single prompt. | |
| :param prompt: Input prompt string | |
| :return: Text generated by the model | |
| """ | |
| pass | |
| def generate_batch(self, prompts: List[str]) -> List[str]: | |
| """ | |
| Generate responses for a batch of prompts (optional, but recommended for efficiency). | |
| :param prompts: List of input prompt strings | |
| :return: List of texts generated by the model | |
| """ | |
| # Default implementation calls generate serially, subclasses should override for true batch processing | |
| return [self.generate(prompt) for prompt in prompts] |