Create README.md

README.md

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+---
+license: mit
+datasets:
+- gretelai/synthetic_text_to_sql
+base_model:
+- eagle0504/openai-gsm8k-codealpaca-20k-enhanced-deepseek-r1-distill-qwen-1.5b
+library_name: transformers
+---
+
+
+# 🧠 eagle0504/qwen-distilled-scout-1.5b-instruct-gen1
+
+This model is a fine-tuned version of [`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B), enhanced with instruction-tuned chain-of-thought (CoT) reasoning across three problem domains: **math**, **text-to-SQL**, and **Python programming**.
+
+Fine-tuning was conducted using DeepSpeed on a multi-A100 GPU setup via RunPod for efficient training in memory-constrained environments. The training dataset includes CoT-formatted tasks with natural language questions and structured reasoning paths.
+
+Inference notebook is publicly available [here](https://colab.research.google.com/drive/10CJqyIAOd9QnEp0W8NN_SxdiOrFsBz0-?usp=sharing).
+
+---
+
+## 📎 Model Details
+
+* **Base Model:** [`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
+* **Language:** English
+* **Architecture:** Causal Language Model (Decoder-only)
+* **Tokenizer:** AutoTokenizer from base model
+* **Parameter Count:** 1.5 Billion
+* **Training Framework:** 🧢 Transformers + DeepSpeed
+* **Compute Environment:** RunPod (6x A100 SXM, 192 vCPU, 1.5TB RAM)
+
+---
+
+## 🧪 Training Dataset
+
+**Datasets Used:**
+
+* [`gretelai/synthetic_text_to_sql`](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql)
+* [`eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1`](https://huggingface.co/datasets/eagle0504/openai-gsm8k-enhanced-using-together-ai-deepseek-train8k-test1k-v1)
+* [`eagle0504/augmented_codealpaca-20k-using-together-ai-deepseek-v1`](https://huggingface.co/datasets/eagle0504/augmented_codealpaca-20k-using-together-ai-deepseek-v1)
+
+Each example in the dataset follows the structure:
+
+```xml
+<instruction>This is a [math/SQL/Python] problem.</instruction>
+<question>...</question>
+<think>...</think>
+<response>...</response>
+```
+
+This instruction format ensures that the model understands the task type explicitly and applies step-by-step reasoning across all domains.
+
+---
+
+## 📊 Fine-Tuning Summary
+
+The base model [`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) was fine-tuned on three different datasets using DeepSpeed across various RunPod infrastructure setups. Below is a consolidated summary of the training configurations and results:
+
+| Model ID                                                                        | Dataset Description             | GPUs          | vCPUs | RAM (GB) | Disk per GPU | Container Image                                            | Duration | Cost    | DeepSpeed Stage | Precision | Mean Token Accuracy |
+| ------------------------------------------------------------------------------- | ------------------------------- | ------------- | ----- | -------- | ------------ | ---------------------------------------------------------- | -------- | ------- | --------------- | --------- | ------------------- |
+| `eagle0504/finetuned-deepseek-r1-distill-qwen-1.5b-by-openai-gsm8k-enhanced-v2` | OpenAI GSM8K Enhanced v2        | 6 × H100 PCIe | 144   | 1132     | 20 GB        | `runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04` | 2 hrs    | \~\$28  | Stage 1         | FP16      | 98%                 |
+| `eagle0504/openai-gsm8k-codealpaca-20k-enhanced-deepseek-r1-distill-qwen-1.5b`  | GSM8K + CodeAlpaca-20K Enhanced | 4 × A100 SXM  | 146   | 1144     | 20 GB        | `runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04` | 2 hrs    | \~\$14+ | Stage 1         | FP16      | 97%                 |
+| `eagle0504/qwen-distilled-scout-1.5b`                                           | Custom CoT + SQL-Reasoning      | 6 × A100 SXM  | 192   | 1536     | 20 GB        | `runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04` | 1.5 hrs  | \~\$21  | Stage 2         | FP16      | 97%                 |
+
+---
+
+## 🏗️ Training Configuration
+
+Training was performed with the following configuration:
+
+* **Batch Size:** 2 (with gradient accumulation steps = 4)
+* **Epochs:** 15
+* **Max Length:** 1024 tokens
+* **Optimizer:** AdamW
+* **Learning Rate:** 5e-5 (with warmup + linear decay)
+* **Precision:** FP16
+* **DeepSpeed Config:**
+
+  * Zero Redundancy Optimizer Stage 2
+  * Gradient Clipping: 1.0
+  * AllGather + ReduceScatter optimization
+* **Checkpoint Saving:** Disabled to minimize disk usage
+
+---
+
+## 🧶 Evaluation Metric
+
+The model is evaluated with a custom token-level accuracy metric:
+
+* **Metric:** Mean token-level accuracy
+* **Definition:** Accuracy over all non-masked tokens (`labels != -100`)
+* **Implementation:** NumPy-based vectorized comparison between predicted tokens and ground truth
+
+---
+
+## 🚀 Use Case
+
+This model is tuned for **instruction-driven chain-of-thought generation**, and is especially useful in:
+
+* Educational tools for logical reasoning and coding
+* Auto SQL and code generation for tabular or structured systems
+* Teaching agents in math, database, and programming domains
+* Conversational agents requiring task-specific structured outputs
+
+---
+
+## 📦 How to Use
+
+```python
+from transformers import StoppingCriteria, StoppingCriteriaList
+import torch
+
+class StopOnTokens(StoppingCriteria):
+    def __init__(self, stop_token_ids: list):
+        super().__init__()
+        self.stop_token_ids = stop_token_ids
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        return any(input_ids[0, -len(token):].tolist() == token for token in self.stop_token_ids)
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("eagle0504/qwen-distilled-scout-1.5b-instruct-gen1")
+tokenizer = AutoTokenizer.from_pretrained("eagle0504/qwen-distilled-scout-1.5b-instruct-gen1")
+
+stop_sequence = "</response>"
+stop_ids = tokenizer.encode(stop_sequence, add_special_tokens=False)
+stopping_criteria = StoppingCriteriaList([StopOnTokens([stop_ids])])
+
+inputs = tokenizer(
+    "<instruction>This is a math problem.</instruction><question>Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether?</question>",
+    return_tensors="pt"
+)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=230,
+    stopping_criteria=stopping_criteria
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+## 📊 Limitations
+
+* The model is specialized for instruction-following tasks in math, SQL, and Python reasoning. It may require further fine-tuning to generalize to open-domain dialogue or creative generation.
+* Input length is capped at 1024 tokens, beyond which content will be truncated.
+
+---
+
+## 🧑‍💻 Author
+
+* **Name:** Yiqiao Yin
+* **Hugging Face:** [eagle0504](https://huggingface.co/eagle0504)
+* **Organization:** \[WYN AI / Independent AI Researcher]
+
+---
+
+## 📝 Citation
+
+```bibtex
+@misc{yin2025instructgen1,
+  title={Instruction-Tuned Qwen 1.5B Fine-tuned on Math + SQL + Python CoT Tasks},
+  author={Yiqiao Yin},
+  year={2025},
+  howpublished={\url{https://huggingface.co/eagle0504/qwen-distilled-scout-1.5b-instruct-gen1}},
+}
+```