Instructions to use zoeeyys/qwen0.8-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zoeeyys/qwen0.8-v0 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B")
model = PeftModel.from_pretrained(base_model, "zoeeyys/qwen0.8-v0")

Transformers

How to use zoeeyys/qwen0.8-v0 with Transformers:

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

pipe = pipeline("text-generation", model="zoeeyys/qwen0.8-v0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zoeeyys/qwen0.8-v0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zoeeyys/qwen0.8-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zoeeyys/qwen0.8-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zoeeyys/qwen0.8-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zoeeyys/qwen0.8-v0

SGLang

How to use zoeeyys/qwen0.8-v0 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 "zoeeyys/qwen0.8-v0" \
    --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": "zoeeyys/qwen0.8-v0",
		"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 "zoeeyys/qwen0.8-v0" \
        --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": "zoeeyys/qwen0.8-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zoeeyys/qwen0.8-v0 with Docker Model Runner:
```
docker model run hf.co/zoeeyys/qwen0.8-v0
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Usage

This model was fine-tuned using PEFT/LoRA, with the linear layers updated

! pip install \
transformers==5.3.0 \
datasets==4.0.0 \
peft==0.18.1 \
trl==0.29.0 \
bitsandbytes==0.49.2 \
accelerate==1.13.0

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

b_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
fmodel = PeftModel.from_pretrained(
    b_model,
    "zoeeyys/qwen0.8-v0"
)
gtokenizer = AutoTokenizer.from_pretrained( "Qwen/Qwen3.5-0.8B",padding_side="left")

messages = [

    {"role": "user", "content": "hi,i want to career shift into software"}
]
inputs = gtokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(fmodel.device)

outputs = fmodel.generate(**inputs, max_new_tokens=2048,eos_token_id=gtokenizer.eos_token_id,
    pad_token_id=gtokenizer.eos_token_id)
print(gtokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:],skip_special_tokens=True))

Downloads last month: -

Model tree for zoeeyys/qwen0.8-v0

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Adapter

(241)

this model