From 7f05ccefd1ba3aabec568fa086aaf5ee64494450 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 20 Sep 2026 17:04:16 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: barath007183/qwen2.5-1.5b-yoda-speak Source: Original Platform --- .gitattributes | 36 ++++++++++ README.md | 146 +++++++++++++++++++++++++++++++++++++++++ chat_template.jinja | 54 +++++++++++++++ config.json | 61 +++++++++++++++++ generation_config.json | 14 ++++ model.safetensors | 3 + tokenizer.json | 3 + tokenizer_config.json | 30 +++++++++ 8 files changed, 347 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..935f5fc --- /dev/null +++ b/README.md @@ -0,0 +1,146 @@ +--- +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 \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..bdf7919 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,54 @@ +{%- 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 XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|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\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- tool_call.arguments | tojson }} + {{- '}\n' }} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- message.content }} + {{- '\n' }} + {%- 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 %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..c58cba4 --- /dev/null +++ b/config.json @@ -0,0 +1,61 @@ +{ + "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 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..a472a0f --- /dev/null +++ b/generation_config.json @@ -0,0 +1,14 @@ +{ + "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" +} diff --git a/model.safetensors b/model.safetensors new file mode 100644 index 0000000..49bc6d5 --- /dev/null +++ b/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51a52204340cfbd56f76abc12d21578fc1e7a43971fa0075ebfbba6bc2ff52bc +size 3087467144 diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..34510ff --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8 +size 11421892 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..770e41d --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,30 @@ +{ + "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 +}