Text Generation
Transformers
Safetensors
qwen2
qwen
qwen2.5-coder
code
fine-tuned
russian
conversational
text-generation-inference
Instructions to use Vilyam888/Broken_Code_Generation.1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vilyam888/Broken_Code_Generation.1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vilyam888/Broken_Code_Generation.1.0") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Vilyam888/Broken_Code_Generation.1.0") model = AutoModelForCausalLM.from_pretrained("Vilyam888/Broken_Code_Generation.1.0") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Vilyam888/Broken_Code_Generation.1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vilyam888/Broken_Code_Generation.1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vilyam888/Broken_Code_Generation.1.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Vilyam888/Broken_Code_Generation.1.0
- SGLang
How to use Vilyam888/Broken_Code_Generation.1.0 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 "Vilyam888/Broken_Code_Generation.1.0" \ --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": "Vilyam888/Broken_Code_Generation.1.0", "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 "Vilyam888/Broken_Code_Generation.1.0" \ --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": "Vilyam888/Broken_Code_Generation.1.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Vilyam888/Broken_Code_Generation.1.0 with Docker Model Runner:
docker model run hf.co/Vilyam888/Broken_Code_Generation.1.0
File size: 927 Bytes
aa988a7 | 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 | from __future__ import annotations
from pathlib import Path
PROJECT_ROOT = Path(__file__).resolve().parents[2]
MODEL_ID = "Broken_Code_Generation.1.0"
BASE_MODEL_ID = "Qwen/Qwen2.5-Coder-3B-Instruct"
ADAPTER_DIR = PROJECT_ROOT / "outputs" / "qwen25-coder-3b-qlora"
TRAINER_STATE = ADAPTER_DIR / "checkpoint-501" / "trainer_state.json"
EVAL_FILE = PROJECT_ROOT / "prepared_data" / "test.json"
HUMAN_EVAL_SAMPLES = 40
GEN_TEMPERATURE = 0.2
GEN_TOP_P = 0.95
GEN_MAX_NEW_TOKENS = 1200
GEN_SEED = 42
DEFAULT_EVAL_LIMIT = 100
METRICS_OUT_DIR = PROJECT_ROOT / "outputs" / "metrics" / "broken_code_generation"
FILE_TRAINING = "01_training_perplexity.json"
FILE_JSON_VALIDITY = "02_json_validity.json"
FILE_BLEU_ROUGE = "03_bleu_rouge.json"
FILE_CODE = "04_code_metrics.json"
FILE_HUMAN = "05_human_evaluation.json"
FILE_APPENDIX_JSON = "appendix_full.json"
FILE_APPENDIX_TXT = "appendix_full.txt"
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