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")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

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
```

da1ch812 commited on Feb 20

Commit

d41768c

verified ·

1 Parent(s): d434e64

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

Browse files

Files changed (4) hide show

README.md +18 -4
config.json +1 -1
model-00001-of-00002.safetensors +2 -2
model-00002-of-00002.safetensors +2 -2

README.md CHANGED Viewed

@@ -1,7 +1,14 @@
 ---
 base_model: unsloth/Qwen3-4B-Instruct-2507
 datasets:
-- u-10bei/sft_alfworld_trajectory_dataset
 language:
 - en
 license: apache-2.0
@@ -33,8 +40,8 @@ tool use, and recovery from errors.
 - Base model: unsloth/Qwen3-4B-Instruct-2507
 - Method: LoRA
-  - dtype: torch.float16
-  - load_in_4bit: True
 - Max sequence length: 2048
 - Epochs: 2
 - Learning rate: 2e-06
@@ -59,7 +66,14 @@ model = AutoModelForCausalLM.from_pretrained(
 ## Sources & Terms (IMPORTANT)
 Training data:
- - u-10bei/sft_alfworld_trajectory_dataset
 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)

 ---
 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
 - Base model: unsloth/Qwen3-4B-Instruct-2507
 - Method: LoRA
+  - dtype: torch.bfloat16
+  - load_in_4bit: False
 - Max sequence length: 2048
 - Epochs: 2
 - Learning rate: 2e-06
 ## 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)

config.json CHANGED Viewed

@@ -4,7 +4,7 @@
   ],
   "attention_bias": false,
   "attention_dropout": 0.0,
-  "dtype": "float16",
   "eos_token_id": 151645,
   "head_dim": 128,
   "hidden_act": "silu",

   ],
   "attention_bias": false,
   "attention_dropout": 0.0,
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   "eos_token_id": 151645,
   "head_dim": 128,
   "hidden_act": "silu",

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