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Model: barath007183/qwen2.5-1.5b-yoda-speak
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+- lora
+- peft
+- yoda
+- style-transfer
+- qwen2.5
+license: apache-2.0
+language:
+- en
+---
+
+# Qwen2.5-1.5B Yoda-Speak Translator
+
+A LoRA fine-tune of Qwen2.5-1.5B-Instruct that translates ordinary English sentences into Yoda-style syntax (object/verb-first reordering, e.g. "Read this you must.").
+
+## Model Details
+
+### Model Description
+
+This model takes a plain English sentence and rewrites it in Yoda's speaking style — primarily through object-subject-verb reordering rather than vocabulary changes. It was trained as a hands-on learning project to understand LoRA fine-tuning mechanics end-to-end: data preparation, chat-template formatting, loss masking, hyperparameter tradeoffs, and — most importantly — why validation loss alone isn't sufficient for picking a checkpoint on a small dataset.
+
+- **Developed by:** Barath (independent project)
+- **Model type:** Causal language model, LoRA fine-tune (adapter merged into base weights)
+- **Language(s):** English
+- **License:** Apache 2.0 (inherited from base model)
+- **Finetuned from model:** [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
+
+## Uses
+
+### Direct Use
+
+Prompt the model with an instruction to translate a sentence into Yoda-speak. Works best on single, self-contained declarative sentences, questions, negations, and imperatives similar in length to the training data (roughly 25-60 characters). Style transfer only — no factual knowledge was added.
+
+### Out-of-Scope Use
+
+- Not intended for factual Q&A, general assistance, or any task beyond stylistic sentence reordering.
+- Degrades on longer multi-clause paragraphs and dialogue-heavy text (see Evaluation below) — treat output on such inputs as unreliable without manual review.
+- Not evaluated for languages other than English.
+
+## Bias, Risks, and Limitations
+
+- Trained on only 720 source examples (576 train / 72 val / 72 test) from the `dvgodoy/yoda_sentences` dataset — a narrow, templated dataset of short declarative sentences about mundane objects. Generalization to genuinely novel sentence structures (questions, multi-clause sentences, dialogue) is measurably weaker than in-distribution performance.
+- Occasionally under-transforms harder inputs (falls back toward plain English word order) rather than producing an incorrect reordering — this was the deciding factor in checkpoint selection, since it's a safer failure mode than the token-level corruption seen in more heavily-trained checkpoints.
+- No safety/toxicity-specific evaluation was performed; inherits the base model's general behavior and limitations.
+
+### Recommendations
+
+For inputs meaningfully different from short declarative English sentences (long paragraphs, dialogue, technical text), manually review output quality before relying on it.
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "barath007183/qwen2.5-1.5b-yoda-speak"  # replace with actual repo id
+model = AutoModelForCausalLM.from_pretrained(model_name, dtype=torch.bfloat16, device_map="cuda")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+messages = [
+    {"role": "system", "content": "You are a Yoda-speak translator."},
+    {"role": "user", "content": "Translate this into Yoda-speak: The cat sat on the mat."},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=60, do_sample=False)
+print(tokenizer.decode(output[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))
+# "Sat on the mat, the cat did."
+```
+
+## Training Details
+
+### Training Data
+
+[`dvgodoy/yoda_sentences`](https://huggingface.co/datasets/dvgodoy/yoda_sentences) — 720 paired examples of plain English sentences and their Yoda-syntax translations. Split 80/10/10 (576 train / 72 validation / 72 test), stratified by random shuffle (seed 42).
+
+Each row was formatted as a 3-turn chat example (system + user + assistant) using Qwen2.5's native ChatML template via `tokenizer.apply_chat_template()`. A fixed system prompt ("You are a Yoda-speak translator.") was paired with one of 4 randomly-rotated user instruction phrasings per row, to avoid the model overfitting to a single trigger phrase rather than the underlying task.
+
+### Training Procedure
+
+- **Method:** LoRA (not QLoRA — base model loaded in bf16, no quantization; unnecessary at this model size on a 24GB GPU)
+- **LoRA config:** r=8, alpha=16, dropout=0.05, target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj` (attention only — MLP modules were not targeted, since this task is primarily syntactic/relational rather than knowledge-based)
+- **Trainable parameters:** 2,179,072 / 1,545,893,376 total (0.141%)
+- **Loss masking:** `assistant_only_loss=True` (trl 1.8.0) — loss computed only on assistant-response tokens, not system/user tokens
+
+#### Training Hyperparameters
+
+- **Training regime:** bf16
+- **Epochs:** 3 (selected after comparing checkpoints at epochs 2, 3, and 8 — see Evaluation)
+- **Batch size:** 8 (train and eval)
+- **Learning rate:** 2e-4
+- **Optimizer:** AdamW (trl/transformers default)
+
+#### Speeds, Sizes, Times
+
+- **Hardware:** 1x RTX 4090 (24GB), rented via RunPod
+- **Training time:** ~26 seconds for 3 epochs (576 examples, 216 steps)
+- **Checkpoint size:** LoRA adapter ~9MB; merged model ~3GB (bf16)
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+Evaluation was done in three stages, deliberately going beyond validation loss alone:
+
+1. **Held-out test set** (72 examples, same distribution as training data)
+2. **Out-of-distribution set** (27 hand-written sentences spanning longer multi-clause sentences, questions, negation, first-person, modern/tech vocabulary, cricket-specific sentences, imperatives, and conditionals — deliberately unlike the training distribution)
+3. **Paragraph stress test** (a short original fantasy-narrative paragraph with dialogue, proper nouns, and a 3-clause compound sentence)
+
+Three checkpoints (epoch 2, 3, and 8) were compared at each stage, alongside the un-fine-tuned base model as a control.
+
+### Results
+
+#### Summary
+
+- **Base model control:** confirmed fine-tuning was necessary and effective — the base model, even when explicitly instructed, produced correct Yoda-syntax reordering on only ~5/72 test sentences, frequently substituting generic "old-timey" phrasing, hallucinating unrelated content, or leaving sentences unchanged.
+- **Validation loss was a misleading tie-breaker at this data scale.** Epoch 2 had marginally lower validation loss (0.2385) than epoch 3 (0.2405) — a difference within noise at 72 validation examples — but epoch 3 produced clearly better outputs on both the in-distribution and out-of-distribution test sets (5/9 flagged failure cases from epoch 2 were fully fixed by epoch 3).
+- **Epoch 8 showed clear overfitting**, though not in the way a naive expectation (worse everywhere) would predict: validation loss more than doubled (0.521 vs. 0.240) and training loss approached zero, with entropy collapsing (0.190 → 0.042), indicating the model became overconfident on training-distribution patterns. In practice this showed up as *inconsistent* behavior on out-of-distribution input — fixing some failure cases epoch 3 couldn't, while introducing new token-level corruption and grammatical regressions elsewhere (e.g., producing a malformed fused token on a 3-clause compound sentence).
+- **Epoch 3 was selected as the final checkpoint**: across all three evaluation stages, it never produced outright broken/garbled output, only occasional "under-transformation" (falling back toward plain English on the hardest inputs) — judged the safer failure mode compared to epoch 8's occasional token-level corruption.
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+Qwen2.5-1.5B-Instruct architecture (transformer decoder, SwiGLU MLP blocks), causal language modeling objective, fine-tuned via supervised fine-tuning (SFT) with LoRA adapters merged into the base weights post-training.
+
+### Compute Infrastructure
+
+#### Hardware
+
+1x NVIDIA RTX 4090 (24GB VRAM), rented via RunPod (PyTorch template, CUDA 12.8 driver)
+
+#### Software
+
+- `transformers`
+- `trl` 1.8.0
+- `peft` 0.19.1
+- `torch` 2.x (cu124 build)
+- `datasets`
+
+## Model Card Authors
+
+Barath C

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.13.1"
+}

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tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}