Update README.md

README.md

@@ -1,49 +1,49 @@
----
-language:
-- en
-license: apache-2.0
-tags:
-- math
-- reasoning
-- mathematics
-- causal-lm
-- text-generation
-library_name: transformers
-pipeline_tag: text-generation
-model_name: Math-A1-2B
----
-
-# 🐟 Math-A1-2B
-
-**Math-A1-2B** is a 2B-parameter language model by **Kitefish**, focused on mathematical reasoning, symbolic understanding, and structured problem solving.
-
-This is an early release and part of our ongoing effort to build strong, efficient models for reasoning-heavy tasks.
-
----
-
-## ✨ What this model is good at
-
-- Basic to intermediate **math problem solving**
-- **Step-by-step reasoning** for equations and word problems
-- Understanding **mathematical symbols and structure**
-- Educational and experimentation use cases
-
----
-
-## 🚀 Quick start
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("kitefish/math-a1-2B")
-model = AutoModelForCausalLM.from_pretrained(
-    "kitefish/math-a1-2B",
-    torch_dtype="auto",
-    device_map="auto"
-)
-
-prompt = "Solve: 2x + 5 = 13"
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-outputs = model.generate(**inputs, max_new_tokens=100)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+---
+language:
+- en
+license: apache-2.0
+tags:
+- math
+- reasoning
+- mathematics
+- causal-lm
+- text-generation
+library_name: transformers
+pipeline_tag: text-generation
+model_name: Minnow-Math-2B
+---
+
+# 🐟 Minnow-Math-2B
+
+**Minnow-Math-2B** is a 2B-parameter language model by **Kitefish**, focused on mathematical reasoning, symbolic understanding, and structured problem solving.
+
+This is an early release and part of our ongoing effort to build strong, efficient models for reasoning-heavy tasks.
+
+---
+
+## ✨ What this model is good at
+
+- Basic to intermediate **math problem solving**
+- **Step-by-step reasoning** for equations and word problems
+- Understanding **mathematical symbols and structure**
+- Educational and experimentation use cases
+
+---
+
+## 🚀 Quick start
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("kitefish/Minnow-Math-2B")
+model = AutoModelForCausalLM.from_pretrained(
+    "kitefish/Minnow-Math-2B",
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+prompt = "Solve: 2x + 5 = 13"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))