Instructions to use Maximus200005/qwen35-0.8b-tldr-sft-ep1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Maximus200005/qwen35-0.8b-tldr-sft-ep1 with Transformers:

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

pipe = pipeline("text-generation", model="Maximus200005/qwen35-0.8b-tldr-sft-ep1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Maximus200005/qwen35-0.8b-tldr-sft-ep1")
model = AutoModelForCausalLM.from_pretrained("Maximus200005/qwen35-0.8b-tldr-sft-ep1", 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 Maximus200005/qwen35-0.8b-tldr-sft-ep1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Maximus200005/qwen35-0.8b-tldr-sft-ep1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Maximus200005/qwen35-0.8b-tldr-sft-ep1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Maximus200005/qwen35-0.8b-tldr-sft-ep1

SGLang

How to use Maximus200005/qwen35-0.8b-tldr-sft-ep1 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 "Maximus200005/qwen35-0.8b-tldr-sft-ep1" \
    --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": "Maximus200005/qwen35-0.8b-tldr-sft-ep1",
		"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 "Maximus200005/qwen35-0.8b-tldr-sft-ep1" \
        --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": "Maximus200005/qwen35-0.8b-tldr-sft-ep1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Maximus200005/qwen35-0.8b-tldr-sft-ep1 with Docker Model Runner:
```
docker model run hf.co/Maximus200005/qwen35-0.8b-tldr-sft-ep1
```

Qwen3.5-0.8B — SFT on Reddit TLDR

Supervised fine-tuned Qwen/Qwen3.5-0.8B for TL;DR summarization of Reddit posts from CarperAI/openai_summarize_tldr.

This checkpoint is intended as a warm-start policy for Active Preference Learning (AL) + DPO experiments.

Model summary

Item	Value
Base model	Qwen/Qwen3.5-0.8B
Task	Reddit post → short TL;DR-style summary
Training type	Causal LM SFT (completion-only cross-entropy)
Selected checkpoint	End of epoch 1 (global step 1292)
Validation `eval_loss` (epoch 1)	~1.868

Training data & filtering

Dataset: CarperAI/openai_summarize_tldr (train / valid)
Filters (aligned with AL_Uncertainty/src/data_utils.py):
- Prompt length 200–1000 characters (after rstrip)
- Exclude posts mentioning r/offmychest or r/tifu
Supervision: human label (reference TL;DR) as the assistant turn in a chat template.

After filtering, the training split size is ~20.6k examples.

Training procedure

Training was run with Hugging Face TRL

Key hyperparameters (sft/config/default.yaml):

Hyperparameter	Value
Epochs (planned)	3
Checkpoint released	Epoch 1 only
Per-device train batch size	2
Gradient accumulation	8 → effective batch 16
Learning rate	2e-5
Warmup ratio	0.05
Max sequence length	2048
Precision	bf16

How to use

Load model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Maximus200005/qwen35-0.8b-tldr-sft-ep1"

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

Downloads last month: 31

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for Maximus200005/qwen35-0.8b-tldr-sft-ep1

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

(401)

this model

Maximus200005
/

qwen35-0.8b-tldr-sft-ep1