Instructions to use Batnini/knowledge-agent-sft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Batnini/knowledge-agent-sft-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "Batnini/knowledge-agent-sft-lora")

Transformers

How to use Batnini/knowledge-agent-sft-lora with Transformers:

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

pipe = pipeline("text-generation", model="Batnini/knowledge-agent-sft-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Batnini/knowledge-agent-sft-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Batnini/knowledge-agent-sft-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Batnini/knowledge-agent-sft-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Batnini/knowledge-agent-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Batnini/knowledge-agent-sft-lora

SGLang

How to use Batnini/knowledge-agent-sft-lora 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 "Batnini/knowledge-agent-sft-lora" \
    --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": "Batnini/knowledge-agent-sft-lora",
		"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 "Batnini/knowledge-agent-sft-lora" \
        --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": "Batnini/knowledge-agent-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Batnini/knowledge-agent-sft-lora with Docker Model Runner:
```
docker model run hf.co/Batnini/knowledge-agent-sft-lora
```

knowledge-agent-sft-lora / kaggle_output.json

Batnini

Upload kaggle_output.json with huggingface_hub

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{
  "status": "Auftrag/unverifiziert-lokal",
  "note": "Erzeugt auf Kaggle GPU. Adapter muss vom Menschen zurueckgegeben werden.",
  "base_model": "Qwen/Qwen2.5-0.5B-Instruct",
  "adapter_path": "/kaggle/working/adapter",
  "output_json": "/kaggle/working/kaggle_output.json",
  "loss": 0.5733484268188477,
  "sft_source": "local",
  "source_note": "M1 consolidation_sft.jsonl (lokal, §10.3)",
  "dataset": "consolidation_sft.jsonl",
  "dataset_path": "/kaggle/working/train_f6.jsonl",
  "dataset_hash_sha256": "90d467e96fbeca1dffbbd5dc2fdb51efd041fd67da1b5347c9977abcac88e00b",
  "dataset_split": null,
  "dataset_requested": null,
  "dataset_rows_scanned": 50000,
  "dataset_skipped_empty": null,
  "dataset_row_hash_count": 50000,
  "dataset_row_hash_aggregate_sha256": "253f64e2574138a2043d5b1cb2d1bf7d797f67f523b79699834dc1c1fb369816",
  "dataset_rows": 50000,
  "seed": 42,
  "num_train_epochs": 3.0,
  "max_length": 1024,
  "learning_rate": 0.0002,
  "lora_r": 16,
  "lora_alpha": 32,
  "compute_dtype": "float16",
  "amp": "disabled (fp16=False, bf16=False)",
  "compute_capability": "sm_75",
  "model_init_dtype": "float16",
  "gpu": "Tesla T4",
  "started_utc": "2026-10-10T12:33:09Z",
  "finished_utc": "2026-10-10T17:16:07Z",
  "library_versions": {
    "transformers": "5.16.1",
    "peft": "0.20.0",
    "trl": "1.15.0",
    "bitsandbytes": "0.50.2",
    "datasets": "4.8.5",
    "accelerate": "1.14.0",
    "torch": "2.11.0+cu128"
  },
  "python": "3.13.15",
  "source": "AGENTS.md §10.3 · arXiv:2608.22215 §3.4"
}

	{
	"status": "Auftrag/unverifiziert-lokal",
	"note": "Erzeugt auf Kaggle GPU. Adapter muss vom Menschen zurueckgegeben werden.",
	"base_model": "Qwen/Qwen2.5-0.5B-Instruct",
	"adapter_path": "/kaggle/working/adapter",
	"output_json": "/kaggle/working/kaggle_output.json",
	"loss": 0.5733484268188477,
	"sft_source": "local",
	"source_note": "M1 consolidation_sft.jsonl (lokal, §10.3)",
	"dataset": "consolidation_sft.jsonl",
	"dataset_path": "/kaggle/working/train_f6.jsonl",
	"dataset_hash_sha256": "90d467e96fbeca1dffbbd5dc2fdb51efd041fd67da1b5347c9977abcac88e00b",
	"dataset_split": null,
	"dataset_requested": null,
	"dataset_rows_scanned": 50000,
	"dataset_skipped_empty": null,
	"dataset_row_hash_count": 50000,
	"dataset_row_hash_aggregate_sha256": "253f64e2574138a2043d5b1cb2d1bf7d797f67f523b79699834dc1c1fb369816",
	"dataset_rows": 50000,
	"seed": 42,
	"num_train_epochs": 3.0,
	"max_length": 1024,
	"learning_rate": 0.0002,
	"lora_r": 16,
	"lora_alpha": 32,
	"compute_dtype": "float16",
	"amp": "disabled (fp16=False, bf16=False)",
	"compute_capability": "sm_75",
	"model_init_dtype": "float16",
	"gpu": "Tesla T4",
	"started_utc": "2026-10-10T12:33:09Z",
	"finished_utc": "2026-10-10T17:16:07Z",
	"library_versions": {
	"transformers": "5.16.1",
	"peft": "0.20.0",
	"trl": "1.15.0",
	"bitsandbytes": "0.50.2",
	"datasets": "4.8.5",
	"accelerate": "1.14.0",
	"torch": "2.11.0+cu128"
	},
	"python": "3.13.15",
	"source": "AGENTS.md §10.3 · arXiv:2608.22215 §3.4"
	}