Instructions to use laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8 with Transformers:

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

pipe = pipeline("text-generation", model="laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8")
model = AutoModelForCausalLM.from_pretrained("laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8")
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 laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8

SGLang

How to use laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8 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 "laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8" \
    --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": "laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8",
		"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 "laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8" \
        --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": "laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8 with Docker Model Runner:
```
docker model run hf.co/laion/sera-subset-mixed-3160-axolotl__Qwen3-8B-v8
```

sera-subset-mixed-3160-axolotl__Qwen3-8B-v8

SFT of Qwen/Qwen3-8B on a 3160-row random mixed subset of ethanlshen/sera-subset (stage1 unresolved + stage2 resolved), trained with axolotl following the upstream SERA recipe.

See baselines/sera/README.md in the open-thoughts/OpenThoughts-Agent repo for the full reproduction details, hyperparameters, and iteration history (this is iteration i9, version v8).

Hyperparameters

learning_rate: 1e-5
batch_size: 32 (global; micro=1, grad_accum=1, dp=32)
num_epochs: 3
warmup_steps: 48
adam_beta1: 0.9, adam_beta2: 0.95
weight_decay: 0.01
sequence_len: 32768
chat_template: chatml
bf16, deepspeed zero3 (no CPU offload)

Downloads last month: 138

Safetensors

Model size

8B params

Tensor type

BF16

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