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Model: jaygala24/Qwen2.5-1.5B-ReMax-math-reasoning
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
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+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B
+tags:
+- reinforcement-learning
+- remax
+- math-reasoning
+- pipelinerl
+datasets:
+- gsm8k_train
+- math_train
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-1.5B-ReMax-math-reasoning
+
+This model is a fine-tuned version of [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) using **ReMax without KL penalty** for mathematical reasoning.
+
+Trained with [PipelineRL](https://github.com/ServiceNow/PipelineRL).
+
+## Training Details
+
+### Datasets
+
+| Split | Datasets |
+|-------|----------|
+| Train | `gsm8k_train`, `math_train` |
+| Test  | `gsm8k_test`, `math_500` |
+
+### RL Algorithm
+
+| Parameter | Value |
+|-----------|-------|
+| Algorithm | ReMax |
+| Advantage Baseline | Greedy-decoded response reward |
+| Extra Inference | 1 deterministic rollout per prompt |
+| Group Structure | Not required |
+| Policy Loss | `ppo` |
+| KL Coefficient | `0.0` |
+| Epsilon (clip) | `0.2` |
+| Discount Factor (`gamma`) | `1.0` |
+| Divide Advantage by Std | `False` |
+| Filter Zero Advantage Groups | `False` |
+| Rollouts per Problem | `16` |
+
+ReMax uses a greedy-decoded response's reward as the baseline for advantages.
+
+### Training Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | `Qwen/Qwen2.5-1.5B` |
+| Learning Rate | `1e-06` |
+| LR Scheduler | `cosine` |
+| Warmup Steps | `25` |
+| Max Training Steps | `1500` |
+| Micro Batch Size | `4` |
+| Gradient Accumulation | `64` |
+| Effective Batch Size | `256` |
+| Sequence Length | `8192` |
+| Gradient Clipping | `0.3` |
+| Weight Decay | `0.01` |
+| Optimizer | `adamw_torch` |
+| Precision | `bf16` |
+| DeepSpeed | ZeRO Stage 3 |
+
+## Evaluation Results
+
+Pass@k on math reasoning benchmarks (N=32 samples per problem, temperature=1.0):
+
+| Dataset | pass@1 | pass@2 | pass@4 | pass@8 | pass@16 | pass@32 |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| GSM8K (test) | 76.71 | 84.58 | 89.79 | 93.03 | 94.93 | 96.29 |
+| MATH-500 | 57.79 | 66.88 | 74.21 | 80.47 | 85.57 | 89.20 |
+| **Overall** | **71.51** | **79.72** | **85.51** | **89.58** | **92.36** | **94.34** |
+
+*GSM8K test: 1319 problems · MATH-500: 500 problems · Overall: 1819 problems (overall weighted by problem count).*
+
+## Training Curves
+
+![Training Metrics](training_metrics.png)
+
+## W&B Run
+
+Full training logs: [https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_1.5b_remax_3a1f_4xh100_214754_finetune_4dda3b89](https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_1.5b_remax_3a1f_4xh100_214754_finetune_4dda3b89)
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("jaygala24/Qwen2.5-1.5B-ReMax-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
+tokenizer = AutoTokenizer.from_pretrained("jaygala24/Qwen2.5-1.5B-ReMax-math-reasoning", revision="step-0200")
+
+prompt = "Please reason step by step, and put your final answer within \\boxed{}.\n\nWhat is the sum of 123 and 456?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### vLLM
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="jaygala24/Qwen2.5-1.5B-ReMax-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
+sampling_params = SamplingParams(temperature=0.7, max_tokens=4096)
+
+prompt = "Please reason step by step, and put your final answer within \boxed{}.
+
+What is the sum of 123 and 456?"
+outputs = llm.generate([prompt], sampling_params)
+print(outputs[0].outputs[0].text)
+```
+
+## Framework
+
+- [PipelineRL](https://github.com/ServiceNow/PipelineRL)
+- [Transformers](https://github.com/huggingface/transformers)
+- [DeepSpeed](https://github.com/microsoft/DeepSpeed) (ZeRO Stage 3)

added_tokens.json

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- '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 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,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
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+  "intermediate_size": 8960,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.52.4"
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special_tokens_map.json

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tokenizer_config.json

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+}

training_config.yaml

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+finetune:
+  data: null
+  model_class: causal-language-modeling
+  config_name: ${..model_path}
+  optim: adamw_torch
+  load_as_bf16: true
+  fp32_lm_head: ${..fp32_lm_head}
+  fp32_layer_prefix: ${..fp32_layer_prefix}
+  use_flash_attention: true
+  attn_implementation: flash_attention_2
+  auto_device_map: false
+  lora:
+    enabled: false
+    task_type: CAUSAL_LM
+    base_model_8bit: false
+    base_model_4bit: false
+    r: 16
+    alpha: 16
+    dropout: 0.05
+    bias: none
+    target_modules: []
+  force_restart: ${..force_restart}
+  resume_dataloader: false
+  train_batch_size: 4
+  valid_batch_size: 4
+  weight_decay: 0.01
+  learning_rate: 1.0e-06
+  gradient_clipping_threshold: 0.3
+  lr_scheduler_type: cosine
+  num_warmup_steps: 25
+  gradient_accumulation_passes: 64
+  gradient_checkpointing: true
+  reentrant_checkpointing: false
+  max_train_steps: 1500
+  interrupt_train_steps: -1
+  max_eval_steps: -1
+  seq_length: 8192
+  seq_packing: true
+  output_dir: ${..output_dir}/finetune
+  seed: ${..seed}
+  save_checkpoint_steps: 100
+  keep_intermediate_checkpoints: true
+  trust_remote_code: false
+  cuda_empty_cache: true
+  sft_config_name: null
+  n_examples: 0
+  log_each_n_steps: 1
+  also_save_steps: []
+  use_safetensors: true
+  save_final_training_state: true
+  seq_parallel: 1
+  objective: rl
+  input: training_data
+  send_weight_updates: true
+  queue_size: 32
+  max_lag: null
+  weight_update_interval: 1
+  pop_old_data: ${..pop_old_data}
+  attempts: 8
+  eval_callback:
+    _target_: pipelinerl.finetune.utils.dummy_eval_callback
+    config_name: ''
+  rl:
+    policy_loss: ppo
+    divide_advantage_by_std: false
+    kl_coef: 0.0
+    final_kl_coef: 0.0
+    entropy_bonus: 0.0
+    reward_minus_kl_coef: 0.0
+    epsilon_low: 0.2
+    epsilon_high: 0.2
+    use_advantages: true
+    relu_log_p_weights: false
+    clamp_log_ratio_ref_new_value: 5
+    temperature: ${...llm.parameters.temperature}
+    aggregate_loss: sum
+    overlong_filtering: false
+    adv_estimator: remax
+    gamma: 1.0
+    filter_zero_advantage_groups: false
+rewards:
+  correct_answer_finished: 1.0
+  correct_answer_not_finished: 1.0
+  wrong_answer_finished: 0
+  wrong_answer_not_finished: 0
+  no_answer_finished: 0
+  no_answer_not_finished: 0
+  unparsable_finished: 0
+  unparsable_not_finished: 0
+streams:
+  backend: files
+seed: 42
+fp32_lm_head: false
+fp32_layer_prefix: lm_head
+actor:
+  log_each_n_secs: 0
+  llm_max_rollouts: 256
+  rollout_workers: 1
+  discount_factor: 1
+  problem_queue_size: 256
+  result_queue_size: 256
+  throughput_window_size: 50
+  shared_memory_entry_size: 10000000
+  rollout_policy: pipelinerl.domains.math.generate_math_rollout
+  system_prompt: Please reason step by step, and put your final answer within \boxed{}.
+  task_template: '{task}'
+  task_prompt: ''
+environment: null
+preprocess:
+  input: actor
+  output: training_data
+  n_workers: 8
+  chunk_n_groups: 2
+  raw_queue_size: 8
+  input_queue_size: 32
+  output_queue_size: 32
+  dataset_buffer_size: 0
+  ring_buffer_size: 128
+  max_ready_samples_per_lead: 64
+  pop_old_data: ${..pop_old_data}
+  shared_memory_entry_size: 100000000
+  log_every_n_samples: 128
+llm:
+  parameters:
+    max_tokens: 4096
+    temperature: 1.0
+test_llm:
+  parameters:
+    max_tokens: 4096
+    temperature: 1.0
+    top_p: 0.95
+    top_k: 50
+vllm_config:
+  use_v1: false
+  quantization: null
+  vllm_kwargs:
+    dtype: bfloat16
+    gpu-memory-utilization: 0.92
+    max-num-seqs: 64
+    max-num-batched-tokens: 16384
+    enable-chunked-prefill: ''
+    return-tokens-as-token-ids: ''
+    tensor-parallel-size: 1
+    pipeline-parallel-size: 1
+    generation-config: vllm
+    max_model_len: 8192
+    num-scheduler-steps: 8
+    disable-log-requests: ''
+    disable-frontend-multiprocessing: ''
+world:
+  replicas: 1
+  actor_fraction: 3
+  preprocessor_fraction: 0
+  finetune_fraction: 1
+  env_replicas: 1
+  actor_group_port: 9000
+  environment_start_port: 7777
+jobs:
+- kind: actor_llm
+  idx: 0
+  replica_idx: 0
+  local_idx: 0
+  node_rank: 0
+  hostname: localhost
+  port: 8080
+  gpus:
+  - 0
+  url: http://localhost:8080
+  environment_key: null
+  environment_index: null
+- kind: actor_llm
+  idx: 1
+  replica_idx: 1
+  local_idx: 1
+  node_rank: 0
+  hostname: localhost
+  port: 8081
+  gpus:
+  - 1
+  url: http://localhost:8081
+  environment_key: null
+  environment_index: null
+- kind: actor_llm
+  idx: 2
+  replica_idx: 2
+  local_idx: 2
+  node_rank: 0
+  hostname: localhost
+  port: 8082
+  gpus:
+  - 2
+  url: http://localhost:8082
+  environment_key: null
+  environment_index: null
+- kind: actor
+  idx: 3
+  replica_idx: 0
+  local_idx: 0
+  node_rank: 0
+  hostname: localhost
+  port: null
+  gpus: []
+  url: ''
+  environment_key: null
+  environment_index: null
+- kind: preprocessor
+  idx: 4
+  replica_idx: 0
+  local_idx: 0
+  node_rank: 0
+  hostname: localhost
+  port: null
+  gpus: []
+  url: ''
+  environment_key: null
+  environment_index: null
+- kind: environment
+  idx: 5
+  replica_idx: 0
+  local_idx: 0
+  node_rank: 0
+  hostname: localhost
+  port: 7777
+  gpus: []
+  url: ''
+  environment_key: math
+  environment_index: 0
+- kind: finetune
+  idx: 6
+  replica_idx: 0
+  local_idx: 0
+  node_rank: 0
+  hostname: localhost
+  port: null
+  gpus:
+  - 3
+  url: ''
+  environment_key: null
+  environment_index: null
+eval_every_n_versions: 78000
+model_path: Qwen/Qwen2.5-1.5B
+accelerate_config: null
+use_deepspeed: true
+deepspeed_config: deepspeed_stage3_bf16
+use_fsdp: false
+fsdp:
+  param_dtype: fp32
+  reduce_dtype: fp32
+  buffer_dtype: fp32
+output_dir: results/qwen2.5_1.5b_remax_3a1f_4xh100_214754
+force_restart: false
+pop_old_data: true
+max_lag: null
+attempts: 16
+train_subset: null
+debug:
+  mode: ''
+  streams_from: null
+  place_inference_workers: true
+  use_existing_llms: false
+me:
+  job_idx: null
+wandb:
+  use_wandb: true
+  fail_on_init_error: false
+  init_timeout: 120
+  wandb_id: null
+  wandb_name: null
+  wandb_entity_name: jaygala24-team
+  wandb_project_name: rl-post-training
+  wandb_resume: always
+  wandb_use_basename: true
+  wandb_workspace_root: results
+  wandb_group: qwen2.5_1.5b_remax_3a1f_4xh100_214754
+  wandb_dir: null
+  tags: []
+environments:
+- key: math
+  mode: remote
+  _target_: pipelinerl.domains.math.MathEnvironment
+environment_key: math
+dataset_loader: pipelinerl.domains.math.load_datasets
+train_dataset_names:
+- gsm8k_train
+- math_train
+test_dataset_names:
+- gsm8k_test
+- math_500

training_metrics.png

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