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
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f1f682e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | # 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
@abstractmethod
def load_model(self):
pass
@abstractmethod
def generate(self, prompt: str) -> str:
"""
Generate response for a single prompt.
:param prompt: Input prompt string
:return: Text generated by the model
"""
pass
@abstractmethod
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] |