Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use adpretko/ml815-model5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adpretko/ml815-model5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="adpretko/ml815-model5") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("adpretko/ml815-model5") model = AutoModelForCausalLM.from_pretrained("adpretko/ml815-model5", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use adpretko/ml815-model5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "adpretko/ml815-model5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adpretko/ml815-model5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/adpretko/ml815-model5
- SGLang
How to use adpretko/ml815-model5 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 "adpretko/ml815-model5" \ --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": "adpretko/ml815-model5", "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 "adpretko/ml815-model5" \ --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": "adpretko/ml815-model5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use adpretko/ml815-model5 with Docker Model Runner:
docker model run hf.co/adpretko/ml815-model5
Download trainer_log.jsonl from adpretko/ml815-model5: direct link, hf CLI and curl.
- Browser
- Download file 3.03 kB
-
https://huggingface.co/adpretko/ml815-model5/resolve/main/trainer_log.jsonl
- Command line
-
hf download hf://adpretko/ml815-model5/trainer_log.jsonl
-
curl -L -o trainer_log.jsonl https://huggingface.co/adpretko/ml815-model5/resolve/main/trainer_log.jsonl
3.03 kB
| {"current_steps": 10, "total_steps": 155, "loss": 0.5392, "lr": 1.125e-05, "epoch": 0.06472491909385113, "percentage": 6.45, "elapsed_time": "0:02:11", "remaining_time": "0:31:51"} | |
| {"current_steps": 20, "total_steps": 155, "loss": 0.1627, "lr": 1.99770217861636e-05, "epoch": 0.12944983818770225, "percentage": 12.9, "elapsed_time": "0:04:16", "remaining_time": "0:28:49"} | |
| {"current_steps": 30, "total_steps": 155, "loss": 0.0999, "lr": 1.9571451231564523e-05, "epoch": 0.1941747572815534, "percentage": 19.35, "elapsed_time": "0:06:28", "remaining_time": "0:26:58"} | |
| {"current_steps": 40, "total_steps": 155, "loss": 0.0729, "lr": 1.86790270392905e-05, "epoch": 0.2588996763754045, "percentage": 25.81, "elapsed_time": "0:08:28", "remaining_time": "0:24:22"} | |
| {"current_steps": 50, "total_steps": 155, "loss": 0.0725, "lr": 1.734514253098589e-05, "epoch": 0.32362459546925565, "percentage": 32.26, "elapsed_time": "0:10:31", "remaining_time": "0:22:05"} | |
| {"current_steps": 60, "total_steps": 155, "loss": 0.0641, "lr": 1.5637645988943008e-05, "epoch": 0.3883495145631068, "percentage": 38.71, "elapsed_time": "0:12:39", "remaining_time": "0:20:02"} | |
| {"current_steps": 70, "total_steps": 155, "loss": 0.0564, "lr": 1.3643389540670963e-05, "epoch": 0.45307443365695793, "percentage": 45.16, "elapsed_time": "0:14:40", "remaining_time": "0:17:49"} | |
| {"current_steps": 80, "total_steps": 155, "loss": 0.0511, "lr": 1.1463811409361667e-05, "epoch": 0.517799352750809, "percentage": 51.61, "elapsed_time": "0:16:36", "remaining_time": "0:15:33"} | |
| {"current_steps": 90, "total_steps": 155, "loss": 0.0515, "lr": 9.209776239900453e-06, "epoch": 0.5825242718446602, "percentage": 58.06, "elapsed_time": "0:18:42", "remaining_time": "0:13:30"} | |
| {"current_steps": 100, "total_steps": 155, "loss": 0.0483, "lr": 6.995935948193294e-06, "epoch": 0.6472491909385113, "percentage": 64.52, "elapsed_time": "0:20:45", "remaining_time": "0:11:24"} | |
| {"current_steps": 110, "total_steps": 155, "loss": 0.0442, "lr": 4.934897930252887e-06, "epoch": 0.7119741100323624, "percentage": 70.97, "elapsed_time": "0:23:14", "remaining_time": "0:09:30"} | |
| {"current_steps": 120, "total_steps": 155, "loss": 0.0456, "lr": 3.1314972661673572e-06, "epoch": 0.7766990291262136, "percentage": 77.42, "elapsed_time": "0:25:24", "remaining_time": "0:07:24"} | |
| {"current_steps": 130, "total_steps": 155, "loss": 0.0476, "lr": 1.6774642643563955e-06, "epoch": 0.8414239482200647, "percentage": 83.87, "elapsed_time": "0:27:27", "remaining_time": "0:05:16"} | |
| {"current_steps": 140, "total_steps": 155, "loss": 0.043, "lr": 6.467585824627886e-07, "epoch": 0.9061488673139159, "percentage": 90.32, "elapsed_time": "0:29:39", "remaining_time": "0:03:10"} | |
| {"current_steps": 150, "total_steps": 155, "loss": 0.0447, "lr": 9.180725568338045e-08, "epoch": 0.970873786407767, "percentage": 96.77, "elapsed_time": "0:31:52", "remaining_time": "0:01:03"} | |
| {"current_steps": 155, "total_steps": 155, "epoch": 1.0, "percentage": 100.0, "elapsed_time": "0:33:16", "remaining_time": "0:00:00"} | |