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Model: littlelearner/littlelearner-0.6b-grpo-math-expert Source: Original Platform
2026-09-26 10:20:26 +08:00
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--- a/README.md
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+---
+license: other
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen3
+- text-generation
+- littlelearner
+- bounded
+- instruct
+- reinforcement-learning
+---
+
+# littlelearner-0.6b-grpo-math-expert
+
+0.617B K-5-bounded chat model post-trained with GRPO on top of SFT.
+
+Part of the [**LittleLearner**](https://arxiv.org/abs/2608.13545) scale-up study (*pedagogically-controlled knowledge exposure*): Qwen3 dense LMs trained on a corpus filtered to U.S. K-5 material (**bounded**) vs an unfiltered FineWeb-Edu corpus (**unbounded**), to measure what an interpretable knowledge boundary costs and grants.
+
+Note: This checkpoint was post-trained with GRPO on mathematical reasoning tasks to probe achievable performance on MathCAMPS. As a result, its behavior is specialized toward mathematical reasoning and may not preserve general-purpose chat capabilities; responses may also exhibit a tendency toward math-oriented reasoning or output.
+
+## Model
+- **Architecture:** Qwen3 dense (`Qwen3ForCausalLM`).
+- **Size:** 0.617B params, hidden 1536, 20 layers, 12 query / 6 KV heads, FFN 4096. **Context:** 4096.
+- **Tokenizer:** custom 64k byte-level BPE with per-digit splitting (ChatML special tokens).
+- **Pretraining:** 88B tokens on K-5 **LittleCurriculum** (FineWeb-Edu filtered to U.S. grades K-5). WSD schedule, sharded Muon, MXFP8, Megatron-Core on 8xB200.
+- **SFT:** supervised fine-tuned on K-5 chat data (lr 1e-5, 3 epochs) from a cooloff-SFT-primed base.
+- **RL (GRPO):** segmented policy re-banding on a strictly K-5 verifiable-answer pool (Gemini-generated K-5 word problems + K-5-filtered GSM8K): 3 segments at rollout/training temperature 1.0, then 2 more at temperature 1.5 (the bounded-corpus, 0.6B-scale unlock temperature).
+
+## Evaluation
+MathCAMPS:
+- K-5 pass@64 **61.1** / pass@1 **36.8**
+- beyond-K-5 pass@64 **18.5** / pass@1 **6.4**
+
+## Usage
+```python
+# transformers (chat)
+from transformers import AutoModelForCausalLM, AutoTokenizer
+repo = "manueldeprada/littlelearner-0.6b-bounded-grpo"
+tok = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="cuda")
+msgs = [{"role": "user", "content": "Liam has 3 apples and buys 4 more. How many apples does he have?"}]
+ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(ids)
+print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))
+```
+
+```python
+# vLLM
+from vllm import LLM
+repo = "manueldeprada/littlelearner-0.6b-bounded-grpo"
+llm = LLM(repo)
+msgs = [{"role": "user", "content": "Liam has 3 apples and buys 4 more. How many apples does he have?"}]
+print(llm.chat(msgs)[0].outputs[0].text)
+```
--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{%- for message in messages -%}{{- '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>\n' -}}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<|im_start|>assistant\n' -}}{%- endif -%}
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "float32",
+  "eos_token_id": [
+    3,
+    0
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "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"
+  ],
+  "max_position_embeddings": 4096,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 20,
+  "num_key_value_heads": 6,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.2.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 64000,
+  "rope_theta": 1000000.0
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "eos_token_id": [
+    3,
+    0
+  ],
+  "max_new_tokens": 512,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "transformers_version": "5.2.0",
+  "use_cache": true
+}
--- a/model.safetensors
+++ b/model.safetensors
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+version https://git-lfs.github.com/spec/v1
+oid sha256:edb97c6962c1a8d93bf2f66479362b912c99d2428d10ae4577800d100d6c4abb
+size 1234859616
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
+  "add_prefix_space": null,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "eos_token": "<|im_end|>",
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "chat_template": "{%- for message in messages -%}\n{{- '<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>\\n' -}}\n{%- endfor -%}\n{%- if add_generation_prompt -%}{{- '<|im_start|>assistant\\n' -}}{%- endif -%}\n"
+}
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				`{%- for message in messages -%}{{- '<\|im_start\|>' + message['role'] + '\n' + message['content'] + '<\|im_end\|>\n' -}}{%- endfor -%}{%- if add_generation_prompt -%}{{- '<\|im_start\|>assistant\n' -}}{%- endif -%}`