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Model: ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1
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---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- 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
base_model: Qwen/Qwen3-4B-Thinking-2507
datasets:
- sequelbox/Titanium3-DeepSeek-V3.1-Terminus
- sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus
- sequelbox/Tachibana3-Part2-DeepSeek-V3.2
- sequelbox/Mitakihara-DeepSeek-R1-0528
license: apache-2.0
---
**[Support our open-source dataset and model releases!](https://huggingface.co/spaces/sequelbox/SupportOpenSource)**
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/qdicXwrO_XOKRTjOu2yBF.jpeg)
Esper 3.1: [Ministral-3-3B-Reasoning-2512](https://huggingface.co/ValiantLabs/Ministral-3-3B-Reasoning-2512-Esper3.1), [Qwen3-4B-Thinking-2507](https://huggingface.co/ValiantLabs/Qwen3-4B-Thinking-2507-Esper3.1), [Ministral-3-8B-Reasoning-2512](https://huggingface.co/ValiantLabs/Ministral-3-8B-Reasoning-2512-Esper3.1), [Ministral-3-14B-Reasoning-2512](https://huggingface.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-Esper3.1), [gpt-oss-20b](https://huggingface.co/ValiantLabs/gpt-oss-20b-Esper3.1), [Qwen3.5-27B](https://huggingface.co/ValiantLabs/Qwen3.5-27B-Esper3.1), [Qwen3.6-27B](https://huggingface.co/ValiantLabs/Qwen3.6-27B-Esper3.1), [Qwen3.6-35B-A3B](https://huggingface.co/ValiantLabs/Qwen3.6-35B-A3B-Esper3.1)
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](https://huggingface.co/datasets/sequelbox/Titanium3-DeepSeek-V3.1-Terminus) generated with DeepSeek-V3.1-Terminus!
- Improved coding performance: challenging code-reasoning datasets stretch [DeepSeek-V3.1-Terminus](https://huggingface.co/datasets/sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus) and [DeepSeek-V3.2](https://huggingface.co/datasets/sequelbox/Tachibana3-Part2-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](https://huggingface.co/datasets/sequelbox/Mitakihara-DeepSeek-R1-0528) 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](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) prompt format.
Example inference script to get started:
```python
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)
```
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/VCJ8Fmefd8cdVhXSSxJiD.jpeg)
Esper 3.1 is created by [Valiant Labs.](http://valiantlabs.ca/)
[Check out our HuggingFace page to see all of our models!](https://huggingface.co/ValiantLabs)
We care about open source. For everyone to use.

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"</think>": 151668,
"</tool_call>": 151658,
"</tool_response>": 151666,
"<think>": 151667,
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n<think>\n' }}
{%- endif %}

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],
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"max_window_layers": 36,
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"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
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"rope_theta": 5000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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"do_sample": true,
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],
"pad_token_id": 151643,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
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