micaebe
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Qwen2.5-1.5B-Instruct-QwQ

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 ---
-library_name: transformers
 tags:
 - trl
 - sft
----
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 ---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-1.5B-Instruct
 tags:
+- chat
 - trl
 - sft
+- math
+library_name: transformers
+model-index:
+  - name: Qwen2.5-1.5B-Instruct-QwQ
+    results:
+      - task:
+          type: text-generation
+        dataset:
+          name: GSM8k
+          type: gsm8k
+        metrics:
+          - name: pass@4
+            type: pass@4
+            value: 85.15
+            verified: false
+---
+# Qwen2.5-1.5B-Instruct-QwQ
+## Introduction
+Qwen2.5-QwQ is a fine-tuned model based on Qwen2.5-1.5B-Instruct. It was fine-tuned on roughly 20k samples from QwQ-32B-Preview. Compared to Qwen2.5-1.5B-Instruct, this fine-tuned model seems more performant in mathematics contexts and general reasoning. Also it shows some capabilities of self-correction, altough it seems a bit limited because of the size (bigger models seem to learn self-correction more easily, e.g. the 3B & 7B version show much better self-correction abilities).
+**This repo contains the instruction-tuned 1.5B Qwen2.5 model fine-tuned on QwQ reasoning chains**, which has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
+- Number of Parameters: 1.54B
+- Number of Paramaters (Non-Embedding): 1.31B
+- Number of Layers: 28
+- Number of Attention Heads (GQA): 12 for Q and 2 for KV
+- Context Length: Full 32,768 tokens and generation 8192 tokens
+## Quickstart
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "micaebe/Qwen2.5-1.5B-Instruct-QwQ"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"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=512
+)
+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]
+```
+Disclaimer: GSM scores are currently only fro the first 20% of the dataset. Will run the tests on all samples and adjust the score.