Text Generation
Transformers
Safetensors
OpenVINO
English
qwen2
openvino-export
conversational
text-generation-inference
Instructions to use themightywolfie/DeepCoder-1.5B-Preview-openvino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use themightywolfie/DeepCoder-1.5B-Preview-openvino with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="themightywolfie/DeepCoder-1.5B-Preview-openvino") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("themightywolfie/DeepCoder-1.5B-Preview-openvino") model = AutoModelForCausalLM.from_pretrained("themightywolfie/DeepCoder-1.5B-Preview-openvino") 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
- vLLM
How to use themightywolfie/DeepCoder-1.5B-Preview-openvino with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "themightywolfie/DeepCoder-1.5B-Preview-openvino" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "themightywolfie/DeepCoder-1.5B-Preview-openvino", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/themightywolfie/DeepCoder-1.5B-Preview-openvino
- SGLang
How to use themightywolfie/DeepCoder-1.5B-Preview-openvino 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 "themightywolfie/DeepCoder-1.5B-Preview-openvino" \ --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": "themightywolfie/DeepCoder-1.5B-Preview-openvino", "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 "themightywolfie/DeepCoder-1.5B-Preview-openvino" \ --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": "themightywolfie/DeepCoder-1.5B-Preview-openvino", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use themightywolfie/DeepCoder-1.5B-Preview-openvino with Docker Model Runner:
docker model run hf.co/themightywolfie/DeepCoder-1.5B-Preview-openvino
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: mit
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library_name: transformers
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datasets:
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- PrimeIntellect/verifiable-coding-problems
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- likaixin/TACO-verified
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- livecodebench/code_generation_lite
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language:
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- en
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base_model: agentica-org/DeepCoder-1.5B-Preview
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pipeline_tag: text-generation
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tags:
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- openvino
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- openvino-export
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---
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This model was converted to OpenVINO from [`agentica-org/DeepCoder-1.5B-Preview`](https://huggingface.co/agentica-org/DeepCoder-1.5B-Preview) using [optimum-intel](https://github.com/huggingface/optimum-intel)
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via the [export](https://huggingface.co/spaces/echarlaix/openvino-export) space.
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First make sure you have optimum-intel installed:
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```bash
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pip install optimum[openvino]
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```
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To load your model you can do as follows:
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```python
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from optimum.intel import OVModelForCausalLM
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model_id = "themightywolfie/DeepCoder-1.5B-Preview-openvino"
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model = OVModelForCausalLM.from_pretrained(model_id)
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```
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