Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
open-r1
trl
sft
conversational
text-generation-inference
Instructions to use flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic") model = AutoModelForCausalLM.from_pretrained("flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic") 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 flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic
- SGLang
How to use flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic 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 "flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic" \ --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": "flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic", "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 "flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic" \ --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": "flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic with Docker Model Runner:
docker model run hf.co/flyingbugs/Qwen2.5-1.5B-Open-R1-Distill-eos-epic
Training in progress, step 100
Browse files- config.json +1 -1
- model.safetensors +1 -1
- training_args.bin +1 -1
config.json
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"tie_word_embeddings": true,
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"transformers_version": "4.54.0",
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"vocab_size": 151936
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"tie_word_embeddings": true,
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