初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Poseidon-Reasoning-1.7B
Source: Original Platform

README.md

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+---
+datasets:
+- prithivMLmods/Poseidon-Reasoning-5M
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+library_name: transformers
+tags:
+- text-generation-inference
+- moe
+- code
+- science
+- biology
+- chemistry
+- thinking
+pipeline_tag: text-generation
+---
+
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vXEwxMVMiov1zhOFUt6AJ.png)
+
+# **Poseidon-Reasoning-1.7B**
+
+> **Poseidon-Reasoning-1.7B** is a general-purpose, high-efficiency reasoning model fine-tuned on **Qwen3-1.7B** using the **Poseidon-Reasoning-5M** dataset (first 70K entries). Designed for **mathematical, scientific, and code-related reasoning**, this model strikes a balance between structured logic and contextual fluency—ideal for domains demanding symbolic precision and algorithmic thought.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Poseidon-Reasoning-1.7B-GGUF](https://huggingface.co/prithivMLmods/Poseidon-Reasoning-1.7B-GGUF)
+
+## **Key Features**
+
+1. **Versatile Reasoning Model**
+   Finely tuned for multi-domain reasoning tasks, including mathematics, scientific computation, and code logic—capable of navigating structured problem-solving and analytic workflows.
+
+2. **Qwen3-1.7B Foundation**
+   Built upon **Qwen3-1.7B**, providing multilingual reasoning capability, efficient token handling, and strong alignment with instruction-following tasks.
+
+3. **Powered by Poseidon-Reasoning-5M (70K Sample Subset)**
+   Trained on a carefully selected subset of 70K entries from the **Poseidon-Reasoning-5M** dataset—focusing on tasks that emphasize **symbolic accuracy**, **step-by-step thinking**, and **STEM-relevant clarity**.
+
+4. **Balanced Thinking Mode**
+   Supports structured, guided thinking without excessive hallucination or unnecessary verbosity. Ideal for prompt-driven logic tasks with moderate complexity.
+
+5. **Rich Format Output**
+   Outputs include **Markdown**, **Python**, **LaTeX**, and tabular structures—helpful for notebooks, scientific documentation, and programmatic outputs.
+
+6. **1.7B Parameter Footprint**
+   Lightweight enough to run on **mid-tier GPUs or CPU-only environments**, while offering scalable reasoning power for research, teaching, and light automation.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Poseidon-Reasoning-1.7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve: What is the derivative of sin(x) * ln(x)?"
+
+messages = [
+    {"role": "system", "content": "You are a structured reasoning assistant for math, science, and code."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=256
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+## **Intended Use**
+
+* General-purpose symbolic reasoning
+* Math and science tutoring, theorem solving, and computational guidance
+* Structured coding under constraints or STEM-based tasks
+* Lightweight environments where interpretability and precision matter
+* Prompt-driven reasoning with deterministic steps
+
+## **Limitations**
+
+* Not designed for broad open-domain conversation
+* May underperform on creative writing or emotional expression
+* Best results occur with **clear problem statements and goal-directed prompts**
+* Less suitable for speculative or abstract reasoning without structure