Text Generation
Transformers
Safetensors
mistral
alignment-handbook
trl
sft
Generated from Trainer
conversational
text-generation-inference
Instructions to use interview-eval/zephyr-7b-math-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interview-eval/zephyr-7b-math-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="interview-eval/zephyr-7b-math-test") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("interview-eval/zephyr-7b-math-test") model = AutoModelForCausalLM.from_pretrained("interview-eval/zephyr-7b-math-test", 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 interview-eval/zephyr-7b-math-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "interview-eval/zephyr-7b-math-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "interview-eval/zephyr-7b-math-test", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/interview-eval/zephyr-7b-math-test
- SGLang
How to use interview-eval/zephyr-7b-math-test 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 "interview-eval/zephyr-7b-math-test" \ --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": "interview-eval/zephyr-7b-math-test", "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 "interview-eval/zephyr-7b-math-test" \ --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": "interview-eval/zephyr-7b-math-test", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use interview-eval/zephyr-7b-math-test with Docker Model Runner:
docker model run hf.co/interview-eval/zephyr-7b-math-test
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 10.0, | |
| "eval_steps": 500, | |
| "global_step": 50, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.2, | |
| "grad_norm": 18.864488225514396, | |
| "learning_rate": 2.0000000000000003e-06, | |
| "loss": 0.9524, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "grad_norm": 16.624666091321274, | |
| "learning_rate": 1e-05, | |
| "loss": 0.8684, | |
| "step": 5 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "eval_loss": 0.7913689017295837, | |
| "eval_runtime": 5.7172, | |
| "eval_samples_per_second": 54.397, | |
| "eval_steps_per_second": 1.749, | |
| "step": 5 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "grad_norm": 4.401290119342954, | |
| "learning_rate": 9.698463103929542e-06, | |
| "loss": 0.7031, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "eval_loss": 0.5348705053329468, | |
| "eval_runtime": 5.7154, | |
| "eval_samples_per_second": 54.414, | |
| "eval_steps_per_second": 1.75, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "grad_norm": 3.029002967994018, | |
| "learning_rate": 8.83022221559489e-06, | |
| "loss": 0.4888, | |
| "step": 15 | |
| }, | |
| { | |
| "epoch": 3.0, | |
| "eval_loss": 0.34116050601005554, | |
| "eval_runtime": 5.7277, | |
| "eval_samples_per_second": 54.298, | |
| "eval_steps_per_second": 1.746, | |
| "step": 15 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "grad_norm": 3.1549630858744604, | |
| "learning_rate": 7.500000000000001e-06, | |
| "loss": 0.302, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 4.0, | |
| "eval_loss": 0.19024085998535156, | |
| "eval_runtime": 5.7234, | |
| "eval_samples_per_second": 54.338, | |
| "eval_steps_per_second": 1.747, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 5.0, | |
| "grad_norm": 4.126405291248831, | |
| "learning_rate": 5.8682408883346535e-06, | |
| "loss": 0.1643, | |
| "step": 25 | |
| }, | |
| { | |
| "epoch": 5.0, | |
| "eval_loss": 0.08896404504776001, | |
| "eval_runtime": 5.7273, | |
| "eval_samples_per_second": 54.301, | |
| "eval_steps_per_second": 1.746, | |
| "step": 25 | |
| }, | |
| { | |
| "epoch": 6.0, | |
| "grad_norm": 2.870513503322164, | |
| "learning_rate": 4.131759111665349e-06, | |
| "loss": 0.0778, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 6.0, | |
| "eval_loss": 0.04157733544707298, | |
| "eval_runtime": 5.7247, | |
| "eval_samples_per_second": 54.326, | |
| "eval_steps_per_second": 1.747, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 7.0, | |
| "grad_norm": 1.7040194155687554, | |
| "learning_rate": 2.5000000000000015e-06, | |
| "loss": 0.0404, | |
| "step": 35 | |
| }, | |
| { | |
| "epoch": 7.0, | |
| "eval_loss": 0.027967050671577454, | |
| "eval_runtime": 5.7274, | |
| "eval_samples_per_second": 54.301, | |
| "eval_steps_per_second": 1.746, | |
| "step": 35 | |
| }, | |
| { | |
| "epoch": 8.0, | |
| "grad_norm": 1.2638859662186757, | |
| "learning_rate": 1.1697777844051105e-06, | |
| "loss": 0.0279, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 8.0, | |
| "eval_loss": 0.021927356719970703, | |
| "eval_runtime": 5.7381, | |
| "eval_samples_per_second": 54.199, | |
| "eval_steps_per_second": 1.743, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 9.0, | |
| "grad_norm": 0.9658748602996367, | |
| "learning_rate": 3.015368960704584e-07, | |
| "loss": 0.0214, | |
| "step": 45 | |
| }, | |
| { | |
| "epoch": 9.0, | |
| "eval_loss": 0.018554512411355972, | |
| "eval_runtime": 5.7334, | |
| "eval_samples_per_second": 54.244, | |
| "eval_steps_per_second": 1.744, | |
| "step": 45 | |
| }, | |
| { | |
| "epoch": 10.0, | |
| "grad_norm": 0.5265240066937839, | |
| "learning_rate": 0.0, | |
| "loss": 0.0183, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 10.0, | |
| "eval_loss": 0.017760511487722397, | |
| "eval_runtime": 5.6974, | |
| "eval_samples_per_second": 54.586, | |
| "eval_steps_per_second": 1.755, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 10.0, | |
| "step": 50, | |
| "total_flos": 10468982784000.0, | |
| "train_loss": 0.2729184678196907, | |
| "train_runtime": 352.114, | |
| "train_samples_per_second": 8.832, | |
| "train_steps_per_second": 0.142 | |
| } | |
| ], | |
| "logging_steps": 5, | |
| "max_steps": 50, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 10, | |
| "save_steps": 100, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 10468982784000.0, | |
| "train_batch_size": 16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |