Text Generation
Transformers
Safetensors
qwen3
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use DCAgent/a1-stack_bash_withtests_gpt5mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DCAgent/a1-stack_bash_withtests_gpt5mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DCAgent/a1-stack_bash_withtests_gpt5mini") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DCAgent/a1-stack_bash_withtests_gpt5mini") model = AutoModelForCausalLM.from_pretrained("DCAgent/a1-stack_bash_withtests_gpt5mini") 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 DCAgent/a1-stack_bash_withtests_gpt5mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DCAgent/a1-stack_bash_withtests_gpt5mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DCAgent/a1-stack_bash_withtests_gpt5mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DCAgent/a1-stack_bash_withtests_gpt5mini
- SGLang
How to use DCAgent/a1-stack_bash_withtests_gpt5mini 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 "DCAgent/a1-stack_bash_withtests_gpt5mini" \ --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": "DCAgent/a1-stack_bash_withtests_gpt5mini", "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 "DCAgent/a1-stack_bash_withtests_gpt5mini" \ --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": "DCAgent/a1-stack_bash_withtests_gpt5mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DCAgent/a1-stack_bash_withtests_gpt5mini with Docker Model Runner:
docker model run hf.co/DCAgent/a1-stack_bash_withtests_gpt5mini
| { | |
| "achieved_tflops_per_gpu": 0.003018290962007847, | |
| "achieved_tflops_per_gpu_theoretical": 504.797779860652, | |
| "epoch": 7.0, | |
| "loss_nan_ranks": 0, | |
| "loss_rank_avg": 0.19364851713180542, | |
| "mfu_percent": 0.000213306781767339, | |
| "mfu_percent_theoretical": 35.67475476047011, | |
| "total_flos": 1481219089170432.0, | |
| "train_loss": 0.25906227524797987, | |
| "train_runtime": 30671.7259, | |
| "train_samples_per_second": 2.374, | |
| "train_steps_per_second": 0.149, | |
| "valid_targets_mean": 4248.2, | |
| "valid_targets_min": 914 | |
| } |