Model save

README.md

@@ -1,199 +1,57 @@
 ---
 library_name: transformers
-tags: []
+model_name: Qwen2-VL-7B-Instruct-SFT
+tags:
+- generated_from_trainer
+- trl
+- sft
+licence: license
 ---
 
-# Model Card for Model ID
+# Model Card for Qwen2-VL-7B-Instruct-SFT
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is a fine-tuned version of [None](https://huggingface.co/None).
+It has been trained using [TRL](https://github.com/huggingface/trl).
 
+## Quick start
 
+```python
+from transformers import pipeline
 
-## Model Details
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="yichenmeilixinshijie/Qwen2-VL-7B-Instruct-SFT", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
 
-### Model Description
+## Training procedure
 
-<!-- Provide a longer summary of what this model is. -->
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/skywork/cot_sft_0226/runs/rnkx2vc4) 
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+This model was trained with SFT.
 
-### Model Sources [optional]
+### Framework versions
 
-<!-- Provide the basic links for the model. -->
+- TRL: 0.14.0
+- Transformers: 4.49.0.dev0
+- Pytorch: 2.5.1
+- Datasets: 3.3.0
+- Tokenizers: 0.21.0
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Citations
 
-## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

all_results.json

@@ -0,0 +1,9 @@
+{
+    "epoch": 0.9777777777777777,
+    "total_flos": 8.982325273768755e+16,
+    "train_loss": 1.056926799542976,
+    "train_runtime": 683.8851,
+    "train_samples": 2145,
+    "train_samples_per_second": 3.136,
+    "train_steps_per_second": 0.048
+}

generation_config.json

@@ -0,0 +1,16 @@
+{
+  "attn_implementation": null,
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.1,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.49.0.dev0",
+  "use_cache": false
+}

tokenizer_config.json

@@ -138,7 +138,6 @@
   "model_max_length": 32768,
   "pad_token": "<|endoftext|>",
   "padding_side": "left",
-  "processor_class": "Qwen2VLProcessor",
   "split_special_tokens": false,
   "tokenizer_class": "Qwen2Tokenizer",
   "unk_token": null

train_results.json

@@ -0,0 +1,9 @@
+{
+    "epoch": 0.9777777777777777,
+    "total_flos": 8.982325273768755e+16,
+    "train_loss": 1.056926799542976,
+    "train_runtime": 683.8851,
+    "train_samples": 2145,
+    "train_samples_per_second": 3.136,
+    "train_steps_per_second": 0.048
+}

trainer_state.json

@@ -0,0 +1,84 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.9777777777777777,
+  "eval_steps": 500,
+  "global_step": 33,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
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+      "grad_norm": 8.899718284606934,
+      "learning_rate": 1.9941379571543597e-05,
+      "loss": 1.6146,
+      "step": 5
+    },
+    {
+      "epoch": 0.2962962962962963,
+      "grad_norm": 16.135953903198242,
+      "learning_rate": 1.796093065705644e-05,
+      "loss": 1.0589,
+      "step": 10
+    },
+    {
+      "epoch": 0.4444444444444444,
+      "grad_norm": 2.037100315093994,
+      "learning_rate": 1.3701381553399147e-05,
+      "loss": 0.9919,
+      "step": 15
+    },
+    {
+      "epoch": 0.5925925925925926,
+      "grad_norm": 2.2769477367401123,
+      "learning_rate": 8.382180034472353e-06,
+      "loss": 0.9477,
+      "step": 20
+    },
+    {
+      "epoch": 0.7407407407407407,
+      "grad_norm": 1.6512153148651123,
+      "learning_rate": 3.5261371521817247e-06,
+      "loss": 0.9219,
+      "step": 25
+    },
+    {
+      "epoch": 0.8888888888888888,
+      "grad_norm": 1.4710832834243774,
+      "learning_rate": 5.234682881719766e-07,
+      "loss": 0.9062,
+      "step": 30
+    },
+    {
+      "epoch": 0.9777777777777777,
+      "step": 33,
+      "total_flos": 8.982325273768755e+16,
+      "train_loss": 1.056926799542976,
+      "train_runtime": 683.8851,
+      "train_samples_per_second": 3.136,
+      "train_steps_per_second": 0.048
+    }
+  ],
+  "logging_steps": 5,
+  "max_steps": 33,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 1,
+  "save_steps": 100,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 8.982325273768755e+16,
+  "train_batch_size": 2,
+  "trial_name": null,
+  "trial_params": null
+}