Text Generation
Transformers
Safetensors
qwen3
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use DCAgent2/nl2bash-bugs-over5050 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DCAgent2/nl2bash-bugs-over5050 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DCAgent2/nl2bash-bugs-over5050") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DCAgent2/nl2bash-bugs-over5050") model = AutoModelForCausalLM.from_pretrained("DCAgent2/nl2bash-bugs-over5050") 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 DCAgent2/nl2bash-bugs-over5050 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DCAgent2/nl2bash-bugs-over5050" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DCAgent2/nl2bash-bugs-over5050", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DCAgent2/nl2bash-bugs-over5050
- SGLang
How to use DCAgent2/nl2bash-bugs-over5050 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 "DCAgent2/nl2bash-bugs-over5050" \ --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": "DCAgent2/nl2bash-bugs-over5050", "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 "DCAgent2/nl2bash-bugs-over5050" \ --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": "DCAgent2/nl2bash-bugs-over5050", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DCAgent2/nl2bash-bugs-over5050 with Docker Model Runner:
docker model run hf.co/DCAgent2/nl2bash-bugs-over5050
Training in progress, step 6400
Browse files
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| 1258 |
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{"current_steps": 6290, "total_steps": 7140, "loss": 0.071, "lr": 1.7060978971298791e-06, "epoch": 6.166666666666667, "percentage": 88.1, "elapsed_time": "8:21:58", "remaining_time": "1:07:50"}
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| 1259 |
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{"current_steps": 6295, "total_steps": 7140, "loss": 0.0662, "lr": 1.6863944377197162e-06, "epoch": 6.171568627450981, "percentage": 88.17, "elapsed_time": "8:22:17", "remaining_time": "1:07:25"}
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| 1260 |
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{"current_steps": 6300, "total_steps": 7140, "loss": 0.0831, "lr": 1.666800407188862e-06, "epoch": 6.176470588235294, "percentage": 88.24, "elapsed_time": "8:22:39", "remaining_time": "1:07:01"}
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| 1261 |
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{"current_steps": 6305, "total_steps": 7140, "loss": 0.0739, "lr": 1.6473159226170898e-06, "epoch": 6.181372549019608, "percentage": 88.31, "elapsed_time": "8:23:02", "remaining_time": "1:06:37"}
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| 1262 |
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{"current_steps": 6310, "total_steps": 7140, "loss": 0.0741, "lr": 1.6279411004296042e-06, "epoch": 6.186274509803922, "percentage": 88.38, "elapsed_time": "8:23:21", "remaining_time": "1:06:12"}
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| 1263 |
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{"current_steps": 6315, "total_steps": 7140, "loss": 0.0726, "lr": 1.6086760563963478e-06, "epoch": 6.1911764705882355, "percentage": 88.45, "elapsed_time": "8:23:42", "remaining_time": "1:05:48"}
|
| 1264 |
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{"current_steps": 6320, "total_steps": 7140, "loss": 0.068, "lr": 1.5895209056313166e-06, "epoch": 6.196078431372549, "percentage": 88.52, "elapsed_time": "8:24:07", "remaining_time": "1:05:24"}
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| 1265 |
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{"current_steps": 6325, "total_steps": 7140, "loss": 0.0691, "lr": 1.5704757625918454e-06, "epoch": 6.200980392156863, "percentage": 88.59, "elapsed_time": "8:24:24", "remaining_time": "1:04:59"}
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| 1266 |
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{"current_steps": 6330, "total_steps": 7140, "loss": 0.0717, "lr": 1.5515407410779636e-06, "epoch": 6.205882352941177, "percentage": 88.66, "elapsed_time": "8:24:48", "remaining_time": "1:04:35"}
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| 1267 |
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{"current_steps": 6335, "total_steps": 7140, "loss": 0.0705, "lr": 1.5327159542316784e-06, "epoch": 6.21078431372549, "percentage": 88.73, "elapsed_time": "8:25:06", "remaining_time": "1:04:11"}
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| 1268 |
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{"current_steps": 6340, "total_steps": 7140, "loss": 0.0822, "lr": 1.5140015145363163e-06, "epoch": 6.215686274509804, "percentage": 88.8, "elapsed_time": "8:25:32", "remaining_time": "1:03:47"}
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| 1269 |
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{"current_steps": 6345, "total_steps": 7140, "loss": 0.0678, "lr": 1.4953975338158545e-06, "epoch": 6.220588235294118, "percentage": 88.87, "elapsed_time": "8:25:53", "remaining_time": "1:03:23"}
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| 1270 |
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{"current_steps": 6350, "total_steps": 7140, "loss": 0.0795, "lr": 1.4769041232342464e-06, "epoch": 6.2254901960784315, "percentage": 88.94, "elapsed_time": "8:26:25", "remaining_time": "1:03:00"}
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| 1271 |
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{"current_steps": 6355, "total_steps": 7140, "loss": 0.0704, "lr": 1.458521393294754e-06, "epoch": 6.230392156862745, "percentage": 89.01, "elapsed_time": "8:26:49", "remaining_time": "1:02:36"}
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| 1272 |
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{"current_steps": 6360, "total_steps": 7140, "loss": 0.0736, "lr": 1.4402494538392975e-06, "epoch": 6.235294117647059, "percentage": 89.08, "elapsed_time": "8:27:11", "remaining_time": "1:02:12"}
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| 1273 |
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{"current_steps": 6365, "total_steps": 7140, "loss": 0.0663, "lr": 1.4220884140477841e-06, "epoch": 6.240196078431373, "percentage": 89.15, "elapsed_time": "8:27:26", "remaining_time": "1:01:47"}
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| 1274 |
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{"current_steps": 6370, "total_steps": 7140, "loss": 0.0734, "lr": 1.4040383824374738e-06, "epoch": 6.245098039215686, "percentage": 89.22, "elapsed_time": "8:27:42", "remaining_time": "1:01:22"}
|
| 1275 |
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{"current_steps": 6375, "total_steps": 7140, "loss": 0.0792, "lr": 1.386099466862314e-06, "epoch": 6.25, "percentage": 89.29, "elapsed_time": "8:28:06", "remaining_time": "1:00:58"}
|
| 1276 |
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{"current_steps": 6380, "total_steps": 7140, "loss": 0.0733, "lr": 1.3682717745123154e-06, "epoch": 6.254901960784314, "percentage": 89.36, "elapsed_time": "8:28:25", "remaining_time": "1:00:33"}
|
| 1277 |
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{"current_steps": 6385, "total_steps": 7140, "loss": 0.071, "lr": 1.3505554119128861e-06, "epoch": 6.259803921568627, "percentage": 89.43, "elapsed_time": "8:28:52", "remaining_time": "1:00:10"}
|
| 1278 |
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{"current_steps": 6390, "total_steps": 7140, "loss": 0.0679, "lr": 1.3329504849242114e-06, "epoch": 6.264705882352941, "percentage": 89.5, "elapsed_time": "8:29:13", "remaining_time": "0:59:46"}
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| 1279 |
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{"current_steps": 6395, "total_steps": 7140, "loss": 0.0777, "lr": 1.3154570987406202e-06, "epoch": 6.269607843137255, "percentage": 89.57, "elapsed_time": "8:29:39", "remaining_time": "0:59:22"}
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| 1280 |
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{"current_steps": 6400, "total_steps": 7140, "loss": 0.0733, "lr": 1.2980753578899563e-06, "epoch": 6.2745098039215685, "percentage": 89.64, "elapsed_time": "8:29:58", "remaining_time": "0:58:57"}
|
| 1281 |
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{"current_steps": 6405, "total_steps": 7140, "loss": 0.0693, "lr": 1.2808053662329422e-06, "epoch": 6.279411764705882, "percentage": 89.71, "elapsed_time": "8:31:12", "remaining_time": "0:58:39"}
|
| 1282 |
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|
| 1283 |
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{"current_steps": 6415, "total_steps": 7140, "loss": 0.0794, "lr": 1.2466010426035214e-06, "epoch": 6.28921568627451, "percentage": 89.85, "elapsed_time": "8:31:53", "remaining_time": "0:57:51"}
|
| 1284 |
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{"current_steps": 6420, "total_steps": 7140, "loss": 0.0781, "lr": 1.2296669150114381e-06, "epoch": 6.294117647058823, "percentage": 89.92, "elapsed_time": "8:32:10", "remaining_time": "0:57:26"}
|
| 1285 |
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{"current_steps": 6425, "total_steps": 7140, "loss": 0.0738, "lr": 1.2128449453724556e-06, "epoch": 6.299019607843137, "percentage": 89.99, "elapsed_time": "8:32:33", "remaining_time": "0:57:02"}
|
| 1286 |
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{"current_steps": 6430, "total_steps": 7140, "loss": 0.066, "lr": 1.1961352342025135e-06, "epoch": 6.303921568627451, "percentage": 90.06, "elapsed_time": "8:32:50", "remaining_time": "0:56:37"}
|
| 1287 |
+
{"current_steps": 6435, "total_steps": 7140, "loss": 0.075, "lr": 1.1795378813467727e-06, "epoch": 6.3088235294117645, "percentage": 90.13, "elapsed_time": "8:33:12", "remaining_time": "0:56:13"}
|