Text Generation
PEFT
Safetensors
Transformers
lora
sft
trl
code
code-repair
sysmlv2
patch-generation
conversational
Instructions to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct") model = PeftModel.from_pretrained(base_model, "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA") - Transformers
How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA
- SGLang
How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA 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 "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA" \ --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": "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA", "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 "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA" \ --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": "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA with Docker Model Runner:
docker model run hf.co/rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-LoRA
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pipeline_tag: text-generation
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# Model Card for patch
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This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl) on [this dataset](https://huggingface.co/datasets/rohhaiil/SysMLv2_Repair_with_SLMs).
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It generates diff patches for repairing the code.
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pipeline_tag: text-generation
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This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl) on [this dataset](https://huggingface.co/datasets/rohhaiil/SysMLv2_Repair_with_SLMs).
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It generates diff patches for repairing the code.
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