初始化项目，由ModelHub XC社区提供模型
Model: barath007183/qwen2.5-1.5b-yoda-speak Source: Original Platform
This commit is contained in:
ModelHub XC
36
.gitattributes
vendored
Normal file
36
.gitattributes
vendored
Normal file
@@ -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
|
||||
146
README.md
Normal file
146
README.md
Normal file
@@ -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
|
||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -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 <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 %}
|
||||
61
config.json
Normal file
61
config.json
Normal file
@@ -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
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -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"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:51a52204340cfbd56f76abc12d21578fc1e7a43971fa0075ebfbba6bc2ff52bc
|
||||
size 3087467144
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
|
||||
size 11421892
|
||||
30
tokenizer_config.json
Normal file
30
tokenizer_config.json
Normal file
@@ -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
|
||||
}
|
||||
Reference in New Issue
Block a user