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Esper 3.1: Ministral-3-3B-Reasoning-2512, Qwen3-4B-Thinking-2507, Ministral-3-8B-Reasoning-2512, Ministral-3-14B-Reasoning-2512, gpt-oss-20b, Qwen3.5-27B, Qwen3.6-27B, Qwen3.6-35B-A3B
Esper 3.1 is a coding, architecture, and DevOps reasoning specialist built on Qwen 3.
- Your dedicated DevOps expert: Esper 3.1 maximizes DevOps and architecture helpfulness, powered by high-difficulty DevOps and architecture data generated with DeepSeek-V3.1-Terminus!
- Improved coding performance: challenging code-reasoning datasets stretch DeepSeek-V3.1-Terminus and DeepSeek-V3.2 to the limits, allowing Esper 3.1 to tackle harder coding tasks!
- AI to build AI: our high-difficulty AI expertise data boosts Esper 3.1's MLOps, AI architecture, AI research, and general reasoning skills.
- Small model sizes allow running on local desktop and mobile, plus super-fast server inference!
Prompting Guide
Esper 3.1 uses the Qwen3-4B-Thinking-2507 prompt format.
Example inference script to get started:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Write a Terraform configuration that uses the `aws_ami` data source to find the latest Amazon Linux 2 AMI. Then, provision an EC2 instance using this dynamically determined AMI ID."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
Esper 3.1 is created by Valiant Labs.
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