Text Generation
Transformers
Safetensors
qwen2
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use PTTREP/asynchow-minutes-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PTTREP/asynchow-minutes-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PTTREP/asynchow-minutes-code") 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("PTTREP/asynchow-minutes-code") model = AutoModelForCausalLM.from_pretrained("PTTREP/asynchow-minutes-code", 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 PTTREP/asynchow-minutes-code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PTTREP/asynchow-minutes-code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PTTREP/asynchow-minutes-code", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PTTREP/asynchow-minutes-code
- SGLang
How to use PTTREP/asynchow-minutes-code 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 "PTTREP/asynchow-minutes-code" \ --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": "PTTREP/asynchow-minutes-code", "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 "PTTREP/asynchow-minutes-code" \ --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": "PTTREP/asynchow-minutes-code", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PTTREP/asynchow-minutes-code with Docker Model Runner:
docker model run hf.co/PTTREP/asynchow-minutes-code
Download trainer_log.jsonl from PTTREP/asynchow-minutes-code: direct link, hf CLI and curl.
- Browser
- Download file 6.1 kB
-
https://huggingface.co/PTTREP/asynchow-minutes-code/resolve/main/trainer_log.jsonl
- Command line
-
hf download hf://PTTREP/asynchow-minutes-code/trainer_log.jsonl
-
curl -L -o trainer_log.jsonl https://huggingface.co/PTTREP/asynchow-minutes-code/resolve/main/trainer_log.jsonl
6.1 kB
| {"current_steps": 10, "total_steps": 310, "loss": 0.4967, "lr": 5.625e-06, "epoch": 0.06477732793522267, "percentage": 3.23, "elapsed_time": "0:00:15", "remaining_time": "0:07:33"} | |
| {"current_steps": 20, "total_steps": 310, "loss": 0.383, "lr": 9.99743108100344e-06, "epoch": 0.12955465587044535, "percentage": 6.45, "elapsed_time": "0:00:25", "remaining_time": "0:06:15"} | |
| {"current_steps": 30, "total_steps": 310, "loss": 0.3309, "lr": 9.951834807474191e-06, "epoch": 0.19433198380566802, "percentage": 9.68, "elapsed_time": "0:00:36", "remaining_time": "0:05:41"} | |
| {"current_steps": 40, "total_steps": 310, "loss": 0.342, "lr": 9.849750306392085e-06, "epoch": 0.2591093117408907, "percentage": 12.9, "elapsed_time": "0:00:47", "remaining_time": "0:05:18"} | |
| {"current_steps": 50, "total_steps": 310, "loss": 0.2657, "lr": 9.692342110248802e-06, "epoch": 0.32388663967611336, "percentage": 16.13, "elapsed_time": "0:00:57", "remaining_time": "0:05:01"} | |
| {"current_steps": 60, "total_steps": 310, "loss": 0.3223, "lr": 9.481405858508935e-06, "epoch": 0.38866396761133604, "percentage": 19.35, "elapsed_time": "0:01:45", "remaining_time": "0:07:21"} | |
| {"current_steps": 70, "total_steps": 310, "loss": 0.2743, "lr": 9.219347813790416e-06, "epoch": 0.4534412955465587, "percentage": 22.58, "elapsed_time": "0:01:56", "remaining_time": "0:06:40"} | |
| {"current_steps": 80, "total_steps": 310, "loss": 0.2851, "lr": 8.90915741234015e-06, "epoch": 0.5182186234817814, "percentage": 25.81, "elapsed_time": "0:02:07", "remaining_time": "0:06:07"} | |
| {"current_steps": 90, "total_steps": 310, "loss": 0.2701, "lr": 8.554373161936176e-06, "epoch": 0.582995951417004, "percentage": 29.03, "elapsed_time": "0:02:18", "remaining_time": "0:05:38"} | |
| {"current_steps": 100, "total_steps": 310, "loss": 0.2502, "lr": 8.159042276237308e-06, "epoch": 0.6477732793522267, "percentage": 32.26, "elapsed_time": "0:02:29", "remaining_time": "0:05:13"} | |
| {"current_steps": 110, "total_steps": 310, "loss": 0.2626, "lr": 7.727674506052744e-06, "epoch": 0.7125506072874493, "percentage": 35.48, "elapsed_time": "0:03:16", "remaining_time": "0:05:57"} | |
| {"current_steps": 120, "total_steps": 310, "loss": 0.2661, "lr": 7.265190694203086e-06, "epoch": 0.7773279352226721, "percentage": 38.71, "elapsed_time": "0:03:27", "remaining_time": "0:05:28"} | |
| {"current_steps": 130, "total_steps": 310, "loss": 0.2313, "lr": 6.7768666408349445e-06, "epoch": 0.8421052631578947, "percentage": 41.94, "elapsed_time": "0:03:39", "remaining_time": "0:05:03"} | |
| {"current_steps": 140, "total_steps": 310, "loss": 0.2383, "lr": 6.268272919547537e-06, "epoch": 0.9068825910931174, "percentage": 45.16, "elapsed_time": "0:03:51", "remaining_time": "0:04:41"} | |
| {"current_steps": 150, "total_steps": 310, "loss": 0.2315, "lr": 5.745211330880872e-06, "epoch": 0.97165991902834, "percentage": 48.39, "elapsed_time": "0:04:03", "remaining_time": "0:04:19"} | |
| {"current_steps": 160, "total_steps": 310, "loss": 0.2245, "lr": 5.213648718074584e-06, "epoch": 1.0323886639676114, "percentage": 51.61, "elapsed_time": "0:04:54", "remaining_time": "0:04:35"} | |
| {"current_steps": 170, "total_steps": 310, "loss": 0.1497, "lr": 4.679648900096436e-06, "epoch": 1.097165991902834, "percentage": 54.84, "elapsed_time": "0:05:06", "remaining_time": "0:04:12"} | |
| {"current_steps": 180, "total_steps": 310, "loss": 0.189, "lr": 4.149303498416838e-06, "epoch": 1.1619433198380567, "percentage": 58.06, "elapsed_time": "0:05:18", "remaining_time": "0:03:50"} | |
| {"current_steps": 190, "total_steps": 310, "loss": 0.1771, "lr": 3.6286624466253496e-06, "epoch": 1.2267206477732793, "percentage": 61.29, "elapsed_time": "0:05:29", "remaining_time": "0:03:28"} | |
| {"current_steps": 200, "total_steps": 310, "loss": 0.2094, "lr": 3.12366497560313e-06, "epoch": 1.291497975708502, "percentage": 64.52, "elapsed_time": "0:05:40", "remaining_time": "0:03:07"} | |
| {"current_steps": 210, "total_steps": 310, "loss": 0.1753, "lr": 2.6400718615403852e-06, "epoch": 1.3562753036437247, "percentage": 67.74, "elapsed_time": "0:06:32", "remaining_time": "0:03:06"} | |
| {"current_steps": 220, "total_steps": 310, "loss": 0.1369, "lr": 2.1833997096818897e-06, "epoch": 1.4210526315789473, "percentage": 70.97, "elapsed_time": "0:06:44", "remaining_time": "0:02:45"} | |
| {"current_steps": 230, "total_steps": 310, "loss": 0.169, "lr": 1.7588580234610592e-06, "epoch": 1.48582995951417, "percentage": 74.19, "elapsed_time": "0:06:56", "remaining_time": "0:02:24"} | |
| {"current_steps": 240, "total_steps": 310, "loss": 0.1607, "lr": 1.3712897769085903e-06, "epoch": 1.5506072874493926, "percentage": 77.42, "elapsed_time": "0:07:08", "remaining_time": "0:02:04"} | |
| {"current_steps": 250, "total_steps": 310, "loss": 0.1159, "lr": 1.0251161682580125e-06, "epoch": 1.6153846153846154, "percentage": 80.65, "elapsed_time": "0:07:20", "remaining_time": "0:01:45"} | |
| {"current_steps": 260, "total_steps": 310, "loss": 0.1523, "lr": 7.242861849732696e-07, "epoch": 1.680161943319838, "percentage": 83.87, "elapsed_time": "0:08:10", "remaining_time": "0:01:34"} | |
| {"current_steps": 270, "total_steps": 310, "loss": 0.1415, "lr": 4.722315555370793e-07, "epoch": 1.7449392712550607, "percentage": 87.1, "elapsed_time": "0:08:22", "remaining_time": "0:01:14"} | |
| {"current_steps": 280, "total_steps": 310, "loss": 0.1441, "lr": 2.71827601889067e-07, "epoch": 1.8097165991902835, "percentage": 90.32, "elapsed_time": "0:08:34", "remaining_time": "0:00:55"} | |
| {"current_steps": 290, "total_steps": 310, "loss": 0.1436, "lr": 1.253604390908819e-07, "epoch": 1.874493927125506, "percentage": 93.55, "elapsed_time": "0:08:46", "remaining_time": "0:00:36"} | |
| {"current_steps": 300, "total_steps": 310, "loss": 0.1504, "lr": 3.450089638922738e-08, "epoch": 1.9392712550607287, "percentage": 96.77, "elapsed_time": "0:08:58", "remaining_time": "0:00:17"} | |
| {"current_steps": 310, "total_steps": 310, "loss": 0.1196, "lr": 2.854571731947253e-10, "epoch": 2.0, "percentage": 100.0, "elapsed_time": "0:09:49", "remaining_time": "0:00:00"} | |
| {"current_steps": 310, "total_steps": 310, "epoch": 2.0, "percentage": 100.0, "elapsed_time": "0:10:30", "remaining_time": "0:00:00"} | |