Instructions to use ShogoMu/qwen25_7b_lora_agentbench_v23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ShogoMu/qwen25_7b_lora_agentbench_v23 with Transformers:

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

pipe = pipeline("text-generation", model="ShogoMu/qwen25_7b_lora_agentbench_v23")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ShogoMu/qwen25_7b_lora_agentbench_v23")
model = AutoModelForCausalLM.from_pretrained("ShogoMu/qwen25_7b_lora_agentbench_v23", 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 ShogoMu/qwen25_7b_lora_agentbench_v23 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ShogoMu/qwen25_7b_lora_agentbench_v23"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ShogoMu/qwen25_7b_lora_agentbench_v23",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ShogoMu/qwen25_7b_lora_agentbench_v23

SGLang

How to use ShogoMu/qwen25_7b_lora_agentbench_v23 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 "ShogoMu/qwen25_7b_lora_agentbench_v23" \
    --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": "ShogoMu/qwen25_7b_lora_agentbench_v23",
		"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 "ShogoMu/qwen25_7b_lora_agentbench_v23" \
        --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": "ShogoMu/qwen25_7b_lora_agentbench_v23",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ShogoMu/qwen25_7b_lora_agentbench_v23 with Docker Model Runner:
```
docker model run hf.co/ShogoMu/qwen25_7b_lora_agentbench_v23
```

qwen25_7b_lora_agentbench_v23

This repository provides a merged model fine-tuned from ShogoMu/qwen25_7b_lora_agentbench_v11. The fine-tuning was performed using LoRA + Unsloth and the resulting adapter has been merged back into the base model weights.

This repository contains full model weights, making it ready for inference without the need to load a separate adapter.

Training Objective

This model is optimized for multi-turn agent tasks, specifically for ALFWorld (household navigation/interaction) and DBBench (database operations).

The training process applied loss to all assistant turns in the multi-turn trajectories, allowing the model to learn not just final answers, but also intermediate reasoning (Thought), environment observation processing, action selection, and error recovery.

Training Configuration

Base model: ShogoMu/qwen25_7b_lora_agentbench_v11
Method: LoRA (merged post-training)
Max sequence length: 2048
Epochs: 4
Learning rate: 2e-06
LoRA Parameters: r=64, alpha=128

Usage

This model can be loaded using the standard transformers library or deployed with vLLM (recommended for evaluation).

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your_hf_id/your_repo_name"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for ShogoMu/qwen25_7b_lora_agentbench_v23

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

ShogoMu/qwen25_7b_lora_agentbench_v11

Finetuned

(5)

this model

ShogoMu
/

qwen25_7b_lora_agentbench_v23

qwen25_7b_lora_agentbench_v23

Training Objective

Training Configuration

Usage

Transformers

Model tree for ShogoMu/qwen25_7b_lora_agentbench_v23

Datasets used to train ShogoMu/qwen25_7b_lora_agentbench_v23