Instructions to use laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B with Transformers:

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

pipe = pipeline("text-generation", model="laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B")
model = AutoModelForCausalLM.from_pretrained("laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B", 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 laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B"
# 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/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B

SGLang

How to use laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B 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/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B" \
    --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/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B",
		"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/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B" \
        --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/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B with Docker Model Runner:
```
docker model run hf.co/laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B
```

nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B

This model is a fine-tuned version of Qwen/Qwen3-32B on the /e/data1/datasets/playground/ot-baf/hf_hub/datasets--laion--nemotron-terminal-scientific_computing/snapshots/610c7db0b8510b87e3c99b3bd49660bc56821866_thinking_preprocessed, the /e/data1/datasets/playground/ot-baf/hf_hub/datasets--DCAgent--exp_tas_repetition_penalty_1.05_traces/snapshots/b4f5500e00651d5ffc7f8701f8a055d9b2b68a0a_thinking_preprocessed, the /e/data1/datasets/playground/ot-baf/hf_hub/datasets--DCAgent--a1_multifile_composition/snapshots/a19e5e467f3e83605b4de72bb5b7923e5e55efa9_thinking_preprocessed, the /e/data1/datasets/playground/ot-baf/hf_hub/datasets--DCAgent--exp_tas_max_episodes_512_traces/snapshots/236c1dc9aa6d24cf77ce281b5342d93bae685832_thinking_preprocessed, the /e/data1/datasets/playground/ot-baf/hf_hub/datasets--DCAgent--dev_set_part1_10k_glm_4.7_traces_jupiter/snapshots/f1871d1c1446b3b43cbfe2737d0df56cecf3f420_thinking_preprocessed, the /e/data1/datasets/playground/ot-baf/hf_hub/datasets--DCAgent--swesmith-sandboxes-with_tests-gpt-5-mini-passed_glm_4.7_traces/snapshots/b9b0e0d113e9c37dd035f03644315478acc04487_thinking_preprocessed and the /e/data1/datasets/playground/ot-baf/hf_hub/datasets--penfever--Kimi-2.5-r2egym_sandboxes-maxeps-32k/snapshots/4d777f61eacde52705d17f4ec7388bc01c0d95b6_thinking_preprocessed datasets.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 4e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 96
total_train_batch_size: 96
total_eval_batch_size: 768
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 7.0

Training results

Framework versions

Transformers 4.57.6
Pytorch 2.9.1+cu130
Datasets 4.7.0
Tokenizers 0.22.2

Downloads last month: 3

Safetensors

Model size

33B params

Tensor type

F32

Model tree for laion/nemosci-tasrep-a1mfc-dev1-maxeps-swes-r2eg-32b__Qwen3-32B

Base model

Qwen/Qwen3-32B

Finetuned

(524)

this model