Instructions to use da1ch812/advanced-comp-model-20260228233156 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use da1ch812/advanced-comp-model-20260228233156 with Transformers:

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

pipe = pipeline("text-generation", model="da1ch812/advanced-comp-model-20260228233156")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("da1ch812/advanced-comp-model-20260228233156")
model = AutoModelForCausalLM.from_pretrained("da1ch812/advanced-comp-model-20260228233156")
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 da1ch812/advanced-comp-model-20260228233156 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "da1ch812/advanced-comp-model-20260228233156"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "da1ch812/advanced-comp-model-20260228233156",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/da1ch812/advanced-comp-model-20260228233156

SGLang

How to use da1ch812/advanced-comp-model-20260228233156 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 "da1ch812/advanced-comp-model-20260228233156" \
    --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": "da1ch812/advanced-comp-model-20260228233156",
		"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 "da1ch812/advanced-comp-model-20260228233156" \
        --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": "da1ch812/advanced-comp-model-20260228233156",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use da1ch812/advanced-comp-model-20260228233156 with Docker Model Runner:
```
docker model run hf.co/da1ch812/advanced-comp-model-20260228233156
```

＜qwen3-4b-agent-trajectory-lora＞

This repository provides a merged model that includes both the base model unsloth/Qwen3-4B-Instruct-2507 and the LoRA adapter. No separate LoRA loading is required.

Training Objective

This adapter is trained to improve multi-turn agent task performance on ALFWorld (household tasks) and DBBench (database operations).

Loss is applied to all assistant turns in the multi-turn trajectory, enabling the model to learn environment observation, action selection, tool use, and recovery from errors.

Training Configuration

Base model: unsloth/Qwen3-4B-Instruct-2507
Method: LoRA
- dtype: torch.bfloat16
- load_in_4bit: False
Max sequence length: 1024
Epochs: 30.0
Learning rate: 1e-06
LoRA: r=64, alpha=128

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "da1ch812/advanced-comp-model-20260228233156"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

Sources & Terms (IMPORTANT)

Training data:

u-10bei/sft_alfworld_trajectory_dataset_v2
u-10bei/sft_alfworld_trajectory_dataset_v3
u-10bei/sft_alfworld_trajectory_dataset_v4
u-10bei/sft_alfworld_trajectory_dataset_v5
u-10bei/dbbench_sft_dataset_react
u-10bei/dbbench_sft_dataset_react_v2
u-10bei/dbbench_sft_dataset_react_v3
u-10bei/dbbench_sft_dataset_react_v4

Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License. Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.

Downloads last month: 4

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for da1ch812/advanced-comp-model-20260228233156

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

unsloth/Qwen3-4B-Instruct-2507

Adapter

(405)

this model

da1ch812
/

advanced-comp-model-20260228233156