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

Libraries

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

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

pipe = pipeline("text-generation", model="da1ch812/advanced-comp-model")
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")
model = AutoModelForCausalLM.from_pretrained("da1ch812/advanced-comp-model", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

advanced-comp-model / README.md

da1ch812

Upload merged unsloth/Qwen3-4B-Instruct-2507 model (auto-generated README)

bcadd0d verified 6 months ago

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	---
	base_model: unsloth/Qwen3-4B-Instruct-2507
	datasets:
	- 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
	language:
	- en
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- lora
	- agent
	- tool-use
	- alfworld
	- dbbench
	---

	# ＜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: 1
	- Learning rate: 2e-06
	- LoRA: r=64, alpha=128

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "da1ch812/advanced-comp-model"

	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.