Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use AlexCuadron/Qwen-32B-8a4e8f3a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexCuadron/Qwen-32B-8a4e8f3a with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AlexCuadron/Qwen-32B-8a4e8f3a") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AlexCuadron/Qwen-32B-8a4e8f3a") model = AutoModelForCausalLM.from_pretrained("AlexCuadron/Qwen-32B-8a4e8f3a", device_map="auto") 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 Settings
- vLLM
How to use AlexCuadron/Qwen-32B-8a4e8f3a with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AlexCuadron/Qwen-32B-8a4e8f3a" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AlexCuadron/Qwen-32B-8a4e8f3a", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AlexCuadron/Qwen-32B-8a4e8f3a
- SGLang
How to use AlexCuadron/Qwen-32B-8a4e8f3a 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 "AlexCuadron/Qwen-32B-8a4e8f3a" \ --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": "AlexCuadron/Qwen-32B-8a4e8f3a", "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 "AlexCuadron/Qwen-32B-8a4e8f3a" \ --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": "AlexCuadron/Qwen-32B-8a4e8f3a", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AlexCuadron/Qwen-32B-8a4e8f3a with Docker Model Runner:
docker model run hf.co/AlexCuadron/Qwen-32B-8a4e8f3a
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.9696969696969697, | |
| "eval_steps": 500, | |
| "global_step": 8, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.12121212121212122, | |
| "grad_norm": 2.50050950050354, | |
| "learning_rate": 2.5e-06, | |
| "loss": 0.8407, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.24242424242424243, | |
| "grad_norm": 2.2383944988250732, | |
| "learning_rate": 5e-06, | |
| "loss": 0.7878, | |
| "step": 2 | |
| }, | |
| { | |
| "epoch": 0.36363636363636365, | |
| "grad_norm": 2.1805531978607178, | |
| "learning_rate": 7.500000000000001e-06, | |
| "loss": 0.8026, | |
| "step": 3 | |
| }, | |
| { | |
| "epoch": 0.48484848484848486, | |
| "grad_norm": 2.0964150428771973, | |
| "learning_rate": 1e-05, | |
| "loss": 0.8133, | |
| "step": 4 | |
| }, | |
| { | |
| "epoch": 0.6060606060606061, | |
| "grad_norm": 1.5487000942230225, | |
| "learning_rate": 9.968561049466214e-06, | |
| "loss": 0.7608, | |
| "step": 5 | |
| }, | |
| { | |
| "epoch": 0.7272727272727273, | |
| "grad_norm": 1.7998461723327637, | |
| "learning_rate": 9.874639560909118e-06, | |
| "loss": 0.7272, | |
| "step": 6 | |
| }, | |
| { | |
| "epoch": 0.8484848484848485, | |
| "grad_norm": 1.9523899555206299, | |
| "learning_rate": 9.719416651541839e-06, | |
| "loss": 0.6923, | |
| "step": 7 | |
| }, | |
| { | |
| "epoch": 0.9696969696969697, | |
| "grad_norm": 4.303847312927246, | |
| "learning_rate": 9.504844339512096e-06, | |
| "loss": 0.6853, | |
| "step": 8 | |
| } | |
| ], | |
| "logging_steps": 1, | |
| "max_steps": 32, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 4, | |
| "save_steps": 8, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 21013697134592.0, | |
| "train_batch_size": 2, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |