Instructions to use ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b with Transformers:

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

pipe = pipeline("text-generation", model="ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b")
model = AutoModelForCausalLM.from_pretrained("ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b

SGLang

How to use ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b 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 "ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b with Docker Model Runner:
```
docker model run hf.co/ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b
```

agenttune-apigen-SFT-qwen3-0.6b

Built using AgentTune — agentic workflows, then train / evaluate / distill / self-heal through one trajectory schema.


Finetuned from	Qwen/Qwen3-0.6B
Algorithm	sft
Backend	trl
Artifact	adapter
Published	2026-09-11 12:44 UTC

Usage

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained("ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b")
tokenizer = AutoTokenizer.from_pretrained("ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b")

This repo is a LoRA adapter. Load it on top of Qwen/Qwen3-0.6B (PEFT does that from adapter_config.json).

Community: Lexsi Discord

Downloads last month: 19

Safetensors

Model size

0.6B params

Tensor type

F32

BF16

Model tree for ram-lexsi/agenttune-apigen-SFT-qwen3-0.6b

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