Text Generation
Transformers
TensorBoard
Safetensors
Chinese
English
code
qwen
lora
repository-understanding
code-assistant
fine-tuning
multi-agent-systems
Eval Results (legacy)
Instructions to use tensense/code_repo_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensense/code_repo_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tensense/code_repo_finetuning")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensense/code_repo_finetuning", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use tensense/code_repo_finetuning with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensense/code_repo_finetuning" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensense/code_repo_finetuning", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tensense/code_repo_finetuning
- SGLang
How to use tensense/code_repo_finetuning 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 "tensense/code_repo_finetuning" \ --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": "tensense/code_repo_finetuning", "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 "tensense/code_repo_finetuning" \ --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": "tensense/code_repo_finetuning", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tensense/code_repo_finetuning with Docker Model Runner:
docker model run hf.co/tensense/code_repo_finetuning
File size: 1,184 Bytes
4e909c7 | 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 | # Makefile
.PHONY: all config analyze generate train merge evaluate clean
all: config analyze generate train merge evaluate
config:
@echo "Step 0: Updating configuration..."
python utils/config_manager.py
analyze:
@echo "Step 1: Analyzing repository..."
python scripts/01_analyze_repo.py
generate:
@echo "Step 2: Generating training data..."
python scripts/02_generate_data.py
train:
@echo "Step 3: Fine-tuning model..."
deepspeed --num_gpus=2 scripts/03_train_model.py
merge:
@echo "Step 4: Merging LoRA weights..."
python scripts/04_merge_weights.py
evaluate:
@echo "Step 5: Evaluating model..."
python scripts/05_evaluate.py
clean:
@echo "Cleaning output files..."
rm -rf output/finetuned_model/checkpoints/*
rm -rf data/training_data/*
help:
@echo "Available targets:"
@echo " make all - Run complete pipeline"
@echo " make config - Update repository config"
@echo " make analyze - Analyze code repository"
@echo " make generate - Generate training data"
@echo " make train - Fine-tune model"
@echo " make merge - Merge LoRA weights"
@echo " make evaluate - Evaluate model"
@echo " make clean - Clean output files" |