Update README.md

README.md

@@ -1,30 +1,74 @@
-id: model_card
-name: Model Card
-type: markdown
-content: |-
-  ---
-  base_model: unsloth/Qwen2.5-Math-1.5B-Instruct-bnb-4bit
-  language:
-  - en
-  license: apache-2.0
-  tags:
-  - text-generation-inference
-  - transformers
-  - unsloth
-  - qwen2
-  - trl
-  ---
-
-  # Uploaded Model
-
-  - **Developed by:** thanhkt
-  - **License:** apache-2.0
-  - **Finetuned from model:** unsloth/Qwen2.5-Math-1.5B-Instruct-bnb-4bit
-
-  This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
-  [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
-
-  ## Dataset
+---
+base_model: unsloth/Qwen2.5-Math-1.5B-Instruct-bnb-4bit
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+---
+
+# Uploaded  model
+
+- **Developed by:** thanhkt
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/Qwen2.5-Math-1.5B-Instruct-bnb-4bit
+
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+## Dataset
 
   The model was trained on the Nvidia-mathinstuct dataset, which consists of 100,000 rows. This dataset was specifically chosen to enhance the model's mathematical reasoning and instruction-following capabilities.
+
+### 🤗 Hugging Face Transformers
+
+Qwen2.5-Math can be deployed and infered in the same way as [Qwen2.5](https://github.com/QwenLM/Qwen2.5). Here we show a code snippet to show you how to use the chat model with `transformers`:
+
+```python
+
+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "thanhkt/Qwen2.5-1.5B-MathInstruct", 
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+alpaca_prompt = """Below...
+
+### Instruct:
+{}
+
+### Input:
+{}
+
+### Output:
+{}"""
+
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        """A company wants to make a pipeline from a point A on shore to a point B on an island. The island is 6km from the coast. The price to build an onshore pipeline is $50,000 per kilometer, and $130,000 per kilometer to build
+
+underwater. B' is the point on the coast so that BB' is perpendicular to the coast. The distance from A to B' is 9km. Position C on section AB' so that when connecting pipes according to ACB, the amount is minimal. At that time, C is one paragraph away from A by:
+
+A. 6.5km B. 6km C. 0km D.9km""", # instruction
+        "", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 512)
+```