Instructions to use DCAgent2/bugs-nl2bashseq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DCAgent2/bugs-nl2bashseq with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DCAgent2/bugs-nl2bashseq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DCAgent2/bugs-nl2bashseq")
model = AutoModelForCausalLM.from_pretrained("DCAgent2/bugs-nl2bashseq")
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 DCAgent2/bugs-nl2bashseq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DCAgent2/bugs-nl2bashseq"
# 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/bugs-nl2bashseq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DCAgent2/bugs-nl2bashseq

SGLang

How to use DCAgent2/bugs-nl2bashseq 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/bugs-nl2bashseq" \
    --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/bugs-nl2bashseq",
		"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/bugs-nl2bashseq" \
        --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/bugs-nl2bashseq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DCAgent2/bugs-nl2bashseq with Docker Model Runner:
```
docker model run hf.co/DCAgent2/bugs-nl2bashseq
```

bugs-nl2bashseq / trainer_log.jsonl

Commit History

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Commit History

Training in progress, step 6657 7449e30 verified

Training in progress, step 6600 49f1264 verified

Reset repository without checkpoints directories d94db65

Training in progress, step 6657

7449e30
verified

Training in progress, step 6600

49f1264
verified

Reset repository without checkpoints directories

d94db65