Instructions to use uchkw/qwen2.5-7b-instruct-sft-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uchkw/qwen2.5-7b-instruct-sft-v6 with Transformers:

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

pipe = pipeline("text-generation", model="uchkw/qwen2.5-7b-instruct-sft-v6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("uchkw/qwen2.5-7b-instruct-sft-v6")
model = AutoModelForCausalLM.from_pretrained("uchkw/qwen2.5-7b-instruct-sft-v6")
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 uchkw/qwen2.5-7b-instruct-sft-v6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "uchkw/qwen2.5-7b-instruct-sft-v6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uchkw/qwen2.5-7b-instruct-sft-v6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/uchkw/qwen2.5-7b-instruct-sft-v6

SGLang

How to use uchkw/qwen2.5-7b-instruct-sft-v6 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 "uchkw/qwen2.5-7b-instruct-sft-v6" \
    --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": "uchkw/qwen2.5-7b-instruct-sft-v6",
		"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 "uchkw/qwen2.5-7b-instruct-sft-v6" \
        --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": "uchkw/qwen2.5-7b-instruct-sft-v6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use uchkw/qwen2.5-7b-instruct-sft-v6 with Docker Model Runner:
```
docker model run hf.co/uchkw/qwen2.5-7b-instruct-sft-v6
```

qwen2.5-7b-instruct-sft-v6

This repository provides a merged full model produced by supervised fine-tuning for AgentBench-oriented ALFWorld/DBBench robustness.

Training Objective

Improve strict action selection reliability for ALFWorld prompts and strengthen SQL error-recovery robustness for DBBench prompts, while keeping balanced mixed-task behavior.

Training Configuration

Method: SFT (Unsloth LoRA) + merge to full model
Base model ID (upstream): Qwen/Qwen2.5-7B-Instruct
Initialization model for this stage: prior merged checkpoint from the previous advanced retraining stage
LoRA: r=16, alpha=32, dropout=0.0
LoRA target modules: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
Output learn mode: from_marker (ACTION: and Action: markers)
Max sequence length: 4096
Max steps: 400
Epochs: 1
Learning rate: 2.0e-6
Per-device train batch size: 1
Per-device eval batch size: 2
Gradient accumulation steps: 32
Effective global batch size: 32
Warmup ratio: 0.03
Weight decay: 0.01
Eval/Save steps: 50 / 25

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "uchkw/qwen2.5-7b-instruct-sft-v6"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

Training Data / Sources & License (IMPORTANT)

Primary source datasets:
- u-10bei/sft_alfworld_trajectory_dataset_v5
- u-10bei/dbbench_sft_dataset_react_v4
- u-10bei/dbbench_sft_dataset_react_v3
Data construction policy (concise):
- ALFWorld samples were converted into strict one-line action supervision (ACTION: ...) with exact matching against AVAILABLE ACTIONS.
- Added hard-copy style ALF augmentation to reinforce exact action copying and reduce formatting drift.
- Mixed DBBench supervision and recovery-oriented examples for Unknown column style failures.
- Mixed train ratio was controlled at approximately ALF:DB = 55:45.
Dataset scale (fix8 stage2):
- Train samples: 138496
- Validation samples: 7289
- Train ALF rows: 76173
- Train DB rows: 62323
- ALF strict-match in training set: 1.0
- ALF completion-verb ratio: 0.4726
- ALF toggle rows: 1772
Evaluation snapshot (checkpoint-350, official_v02 setting):
- DB overall_cat_accuracy: 0.4993505979
- ALF success_rate: 0.60
- ALF invalid_action_rate: 0.10
- ALF task_limit_rate: 0.30
Compliance:
- Follow each source dataset card and license terms.
- Follow base model terms of use.

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Safetensors

Model size

8B params

Tensor type

BF16

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qwen2.5-7b-instruct-sft-v6