Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,9 @@ saved_model/**/* 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
+results/reward_model_clean/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+results/reward_model_qwen_single/checkpoint-140/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+results/reward_model_qwen_single/checkpoint-210/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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+results/reward_model_qwen_single/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+wandb/run-20250821_170958-9d5sa76z/run-9d5sa76z.wandb filter=lfs diff=lfs merge=lfs -text

CLAUDE.md

@@ -0,0 +1,31 @@
+## PPO training for multipref datasets
+
+### Data
+#### Original on huggingface
+allenai/multipref
+#### Transformed
+so this is just a 90/10 split on the train set with seed 2025. The aggregate label is in the agg_label column. The transformed data uses a [-2, 2] scale where labels < 0 indicate preference for A and labels > 0 indicate preference for B. You can load them using e.g. d = datasets.load_from_disk("MultiPref_PPO/data/multipref_test.hf")
+
+### Training
+create an environment named trl, will all necessary packages installed
+Using trl
+
+#### Train a reward model
+- MultiPref_PPO/train_reward_model_official.py
+  - log: MultiPref_PPO/reward_model_qwen_bs16_training.log 
+  eval_accuracy: 0.9354 (93.55%)
+  checkpoint: MultiPref_PPO/results/reward_model_qwen_single
+
+
+#### Models
+  - RM:meta-llama/Meta-Llama-3-8B-Instruct
+- PPO policy: allenai/OLMo-2-0425-1B-SFT
+
+
+
+
+
+
+
+
+

accelerate_config.yaml

@@ -0,0 +1,15 @@
+compute_environment: LOCAL_MACHINE
+distributed_type: MULTI_GPU
+downcast_bf16: 'no'
+gpu_ids: 0,1,2
+machine_rank: 0
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 3
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

config.yaml

@@ -0,0 +1,54 @@
+# Configuration for MultiPref PPO Training
+
+# Model Configuration
+models:
+  base_model: "meta-llama/Meta-Llama-3-8B-Instruct"
+  reward_model_output: "./results/reward_model"
+  ppo_model_output: "./results/ppo_model"
+
+# Data Configuration
+data:
+  train_dataset: "data/multipref_train.hf"
+  test_dataset: "data/multipref_test.hf"
+  max_length: 1024
+  max_new_tokens: 256
+
+# Reward Model Training Configuration
+reward_training:
+  per_device_train_batch_size: 4
+  per_device_eval_batch_size: 8
+  gradient_accumulation_steps: 2
+  learning_rate: 1.41e-5
+  num_train_epochs: 3
+  warmup_steps: 100
+  logging_steps: 10
+  save_strategy: "epoch"
+  evaluation_strategy: "epoch"
+  load_best_model_at_end: true
+  metric_for_best_model: "eval_loss"
+
+# PPO Training Configuration
+ppo_training:
+  batch_size: 32
+  mini_batch_size: 4
+  gradient_accumulation_steps: 1
+  learning_rate: 1.41e-5
+  max_ppo_epochs: 4
+  cliprange: 0.2
+  cliprange_value: 0.2
+  vf_coef: 0.1
+  steps: 1000
+  save_freq: 100
+  log_freq: 10
+  
+# Generation Configuration
+generation:
+  temperature: 0.7
+  top_p: 0.95
+  do_sample: true
+
+# Logging Configuration
+logging:
+  use_wandb: true
+  reward_project: "multipref-reward-model"
+  ppo_project: "multipref-ppo"

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ppo_training.log

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+/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/torch/cuda/__init__.py:829: UserWarning: Can't initialize NVML
+  warnings.warn("Can't initialize NVML")
+/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/torch/cuda/__init__.py:1036: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:109.)
+  r = torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count
+/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:109.)
+  return torch._C._cuda_getDeviceCount() > 0
+/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:109.)
+  return torch._C._cuda_getDeviceCount() > 0
+Traceback (most recent call last):
+Traceback (most recent call last):
+  File "/data/home/yliu/sct-rlhf/MultiPref_PPO/train_ppo.py", line 106, in <module>
+  File "/data/home/yliu/sct-rlhf/MultiPref_PPO/train_ppo.py", line 106, in <module>
+    script_args, training_args, model_args = parser.parse_args_into_dataclasses()
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/transformers/hf_argparser.py", line 358, in parse_args_into_dataclasses
+    script_args, training_args, model_args = parser.parse_args_into_dataclasses()
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/transformers/hf_argparser.py", line 358, in parse_args_into_dataclasses
+    obj = dtype(**inputs)
+  File "<string>", line 165, in __init__
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/trl/trainer/utils.py", line 873, in __post_init__
+    obj = dtype(**inputs)
+  File "<string>", line 165, in __init__
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/trl/trainer/utils.py", line 873, in __post_init__
+    super().__post_init__()
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/transformers/training_args.py", line 1729, in __post_init__
+    super().__post_init__()
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/transformers/training_args.py", line 1729, in __post_init__
+    raise ValueError(error_message)
+ValueError: Your setup doesn't support bf16/gpu.
+    raise ValueError(error_message)
+ValueError: Your setup doesn't support bf16/gpu.
+/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:109.)
+  return torch._C._cuda_getDeviceCount() > 0
+Traceback (most recent call last):
+  File "/data/home/yliu/sct-rlhf/MultiPref_PPO/train_ppo.py", line 106, in <module>
+    script_args, training_args, model_args = parser.parse_args_into_dataclasses()
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/transformers/hf_argparser.py", line 358, in parse_args_into_dataclasses
+    obj = dtype(**inputs)
+  File "<string>", line 165, in __init__
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/trl/trainer/utils.py", line 873, in __post_init__
+    super().__post_init__()
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/transformers/training_args.py", line 1729, in __post_init__
+    raise ValueError(error_message)
+ValueError: Your setup doesn't support bf16/gpu.
+E0826 22:40:47.705000 3041180 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 3041233) of binary: /home/yliu/miniconda3/envs/trl/bin/python3.10
+Traceback (most recent call last):
+  File "/home/yliu/miniconda3/envs/trl/bin/accelerate", line 8, in <module>
+    sys.exit(main())
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1226, in launch_command
+    multi_gpu_launcher(args)
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/accelerate/commands/launch.py", line 853, in multi_gpu_launcher
+    distrib_run.run(args)
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/torch/distributed/run.py", line 892, in run
+    elastic_launch(
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 143, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/home/yliu/miniconda3/envs/trl/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 277, in launch_agent
+    raise ChildFailedError(
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+train_ppo.py FAILED
+------------------------------------------------------------
+Failures:
+[1]:
+  time      : 2025-08-26_22:40:47
+  host      : tree
+  rank      : 1 (local_rank: 1)
+  exitcode  : 1 (pid: 3041234)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[2]:
+  time      : 2025-08-26_22:40:47
+  host      : tree
+  rank      : 2 (local_rank: 2)
+  exitcode  : 1 (pid: 3041235)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2025-08-26_22:40:47
+  host      : tree
+  rank      : 0 (local_rank: 0)
+  exitcode  : 1 (pid: 3041233)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================

requirements.txt

@@ -0,0 +1,11 @@
+torch>=2.0.0
+transformers>=4.55.0
+datasets>=4.0.0
+trl>=0.21.0
+accelerate>=1.10.0
+peft>=0.17.0
+bitsandbytes>=0.47.0
+wandb>=0.21.0
+numpy>=1.17
+pyyaml>=5.1
+safetensors>=0.4.3

results/reward_model_clean/README.md

@@ -0,0 +1,58 @@
+---
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+model_name: reward_model_qwen_single
+tags:
+- generated_from_trainer
+- trl
+- reward-trainer
+licence: license
+---
+
+# Model Card for reward_model_qwen_single
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+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="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<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/photon/huggingface/runs/9d5sa76z) 
+
+
+This model was trained with Reward.
+
+### Framework versions
+
+- TRL: 0.21.0
+- Transformers: 4.55.3
+- Pytorch: 2.8.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## Citations
+
+
+
+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 Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

results/reward_model_clean/added_tokens.json

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+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
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+  "<|fim_middle|>": 151660,
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+  "<|object_ref_end|>": 151647,
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+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

results/reward_model_clean/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 %}

results/reward_model_clean/config.json

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+  "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": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151665
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+    }
+  },
+  "additional_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|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+}

results/reward_model_qwen_single/README.md

@@ -0,0 +1,58 @@
+---
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+model_name: reward_model_qwen_single
+tags:
+- generated_from_trainer
+- trl
+- reward-trainer
+licence: license
+---
+
+# Model Card for reward_model_qwen_single
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+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="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<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/photon/huggingface/runs/9d5sa76z) 
+
+
+This model was trained with Reward.
+
+### Framework versions
+
+- TRL: 0.21.0
+- Transformers: 4.55.3
+- Pytorch: 2.8.0
+- Datasets: 4.0.0
+- Tokenizers: 0.21.4
+
+## Citations
+
+
+
+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 Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

results/reward_model_qwen_single/added_tokens.json

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