初始化项目，由ModelHub XC社区提供模型

Model: ValiantLabs/Qwen3-4B-Esper3
Source: Original Platform

README.md

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+---
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- esper
+- esper-3
+- valiant
+- valiant-labs
+- qwen
+- qwen-3
+- qwen-3-4b
+- 4b
+- reasoning
+- code
+- code-instruct
+- python
+- javascript
+- dev-ops
+- jenkins
+- terraform
+- scripting
+- powershell
+- azure
+- aws
+- gcp
+- cloud
+- problem-solving
+- architect
+- engineer
+- developer
+- creative
+- analytical
+- expert
+- rationality
+- conversational
+- chat
+- instruct
+base_model: Qwen/Qwen3-4B
+datasets:
+- sequelbox/Titanium2.1-DeepSeek-R1
+- sequelbox/Tachibana2-DeepSeek-R1
+- sequelbox/Raiden-DeepSeek-R1
+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: [Qwen3-4B](https://huggingface.co/ValiantLabs/Qwen3-4B-Esper3), [Qwen3-8B](https://huggingface.co/ValiantLabs/Qwen3-8B-Esper3), [Qwen3-14B](https://huggingface.co/ValiantLabs/Qwen3-14B-Esper3)
+
+
+Esper 3 is a coding, architecture, and DevOps reasoning specialist built on Qwen 3.
+- Finetuned on our [DevOps and architecture reasoning](https://huggingface.co/datasets/sequelbox/Titanium2.1-DeepSeek-R1) and [code reasoning](https://huggingface.co/datasets/sequelbox/Tachibana2-DeepSeek-R1) data generated with Deepseek R1!
+- Improved [general and creative reasoning](https://huggingface.co/datasets/sequelbox/Raiden-DeepSeek-R1) to supplement problem-solving and general chat performance.
+- Small model sizes allow running on local desktop and mobile, plus super-fast server inference!
+
+
+## Prompting Guide
+Esper 3 uses the [Qwen 3](https://huggingface.co/Qwen/Qwen3-4B) prompt format.
+
+Esper 3 is a reasoning finetune; we recommend enable_thinking=True for all chats.
+
+Example inference script to get started:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "ValiantLabs/Qwen3-4B-Esper3"
+
+# 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 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.
+