Qwen3-4B-Thinking-2507-Esper3.1/README.md

language, library_name, pipeline_tag, tags, base_model, datasets, license

language	library_name	pipeline_tag	tags	base_model	datasets	license
en	transformers	text-generation	esper esper-3.1 esper-3 valiant valiant-labs qwen qwen-3 qwen-3-4b qwen3-4b-thinking-2507 4b reasoning code code-instruct python javascript dev-ops jenkins terraform ansible docker jenkins kubernetes helm grafana prometheus shell bash azure aws gcp cloud scripting powershell problem-solving architect engineer developer creative analytical expert rationality conversational chat instruct	Qwen/Qwen3-4B-Thinking-2507	sequelbox/Titanium3-DeepSeek-V3.1-Terminus sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus sequelbox/Tachibana3-Part2-DeepSeek-V3.2 sequelbox/Mitakihara-DeepSeek-R1-0528	apache-2.0

Support our open-source dataset and model releases!

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.

Check out our HuggingFace page to see all of our models!

We care about open source. For everyone to use.