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@@ -33,3 +33,4 @@ 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+# PiCo 1B
+
+> A 1B-parameter dense language model optimized for reasoning and knowledge tasks.
+
+---
+
+## 📌 Model Overview
+
+**PiCo 1B** is a compact, high-performance language model with ~1.46 billion parameters. Despite its small size, it achieves competitive performance across reasoning, knowledge, and coding benchmarks, particularly excelling in science reasoning tasks.
+
+---
+
+## 📋 Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Model Size** | ~1.46B parameters |
+| **Architecture** | Dense transformer (decoder-only) |
+| **Context Length** | 2048 tokens |
+| **Training Data** | Wikitext + curated corpora |
+| **Precision** | FP32 / FP16 / Safetensors |
+| **License** | Open-source |
+
+---
+
+## 📊 Benchmark Results
+
+PiCo 1B is evaluated against **31 open-source models** in the 1B–2B parameter range across 7 standard benchmarks.
+
+### MMLU (Massive Multitask Language Understanding)
+
+Measures general knowledge across 57 subjects including STEM, humanities, and social sciences.
+
+![MMLU Benchmark](https://aka.doubaocdn.com/s/AGt01wd7E8)
+
+**PiCo 1B Score: 53.93%** — Rank: Top 3 among 1B–2B models
+
+---
+
+### GSM8K (Grade School Math)
+
+Measures mathematical reasoning with grade-school level word problems.
+
+![GSM8K Benchmark](https://aka.doubaocdn.com/s/7as81wd7E8)
+
+**PiCo 1B Score: 29.33%** — Rank: Top 10 among 1B–2B models
+
+---
+
+### ARC-Challenge (AI2 Reasoning Challenge)
+
+Measures science reasoning with grade-level science questions (harder subset).
+
+![ARC-Challenge Benchmark](https://aka.doubaocdn.com/s/PAwz1wd7E8)
+
+**PiCo 1B Score: 69.2%** — 🥇 **Rank #1** among 1B–2B models
+
+---
+
+### ARC-Easy (AI2 Reasoning Challenge)
+
+Measures basic science reasoning with grade-level science questions (easier subset).
+
+![ARC-Easy Benchmark](https://aka.doubaocdn.com/s/qprH1wd7E8)
+
+**PiCo 1B Score: 85.56%** — 🥇 **Rank #1** among 1B–2B models
+
+---
+
+### HellaSwag (Commonsense Reasoning)
+
+Measures commonsense natural language inference with everyday scenarios.
+
+![HellaSwag Benchmark](https://aka.doubaocdn.com/s/TnLS1wd7E8)
+
+**PiCo 1B Score: 49.4%** — An area for improvement
+
+---
+
+### HumanEval (Code Generation)
+
+Measures functional correctness of code generation across 164 programming problems.
+
+![HumanEval Benchmark](https://aka.doubaocdn.com/s/NaZ91wd7E8)
+
+**PiCo 1B Score: 39.63%** — Rank: Top 4 among 1B–2B models
+
+---
+
+### TruthfulQA (Truthfulness)
+
+Measures whether the model generates truthful answers rather than mimicking common misconceptions.
+
+![TruthfulQA Benchmark](https://aka.doubaocdn.com/s/s3lo1wd7E8)
+
+**PiCo 1B Score: 39.3%** — Rank: Top 5 among 1B–2B models
+
+---
+
+## 🏆 Performance Highlights
+
+### ✅ Strengths
+
+- **Science Reasoning**: Best-in-class performance on ARC-Easy and ARC-Challenge
+- **General Knowledge**: Top 3 on MMLU, outperforming many larger 1.5B–2B models
+- **Coding Ability**: Strong HumanEval performance, competitive with models 2x its size
+- **Truthfulness**: Top 5 on TruthfulQA, demonstrating reliable factual output
+
+### 📈 Areas for Improvement
+
+- **Commonsense Reasoning**: HellaSwag score lags behind modern 1.5B+ models
+- **Mathematical Reasoning**: GSM8K performance is solid but not top-tier
+- **Scale**: Further training on larger, more diverse datasets could boost all benchmarks
+
+---
+
+## 🚀 Usage
+
+### Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "pico-1b"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "Explain the theory of relativity in simple terms."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Model Formats
+
+- **Safetensors** (recommended): Secure and fast loading
+- **PyTorch (FP16)**: Standard format
+- **GGUF**: For local inference with llama.cpp
+
+---
+
+## 🏋️ Training Details
+
+| Aspect | Description |
+|--------|-------------|
+| **Architecture** | Dense decoder-only transformer |
+| **Training Data** | Wikitext + curated multi-domain corpora |
+| **Optimizer** | AdamW |
+| **Learning Rate** | Cosine schedule with warmup |
+| **Batch Size** | Configurable per GPU setup |
+| **Training Framework** | PyTorch + Hugging Face Transformers |
+
+---
+
+## ⚠️ Limitations
+
+- **Small Model Size**: As a 1B-parameter model, it has inherent limitations compared to larger models (7B+) on complex reasoning tasks
+- **Training Data**: Primarily trained on English text; performance on non-English languages may be limited
+- **Hallucinations**: Like all LLMs, it may generate factually incorrect information
+- **Context Window**: Limited to 2048 tokens by default
+
+---
+
+## 📝 Citation
+
+If you use PiCo 1B in your research or projects, please cite:
+
+```bibtex
+@misc{pico1b,
+  title={PiCo 1B: A Compact Language Model Optimized for Reasoning},
+  author={PiCo Team},
+  year={2026},
+  howpublished={\url{https://github.com/pico-llm/pico-1b}},
+}
+```
+
+---
+
+## 📄 License
+
+This model is released under an open-source license. Please see the LICENSE file for details.
+
+---
+
+*Last updated: June 2026*

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

@@ -0,0 +1,62 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "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": 131072,
+  "max_window_layers": 28,
+  "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": "5.12.1",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "5.12.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bcf18ee39c3575a4c8df94713dbde01a8ee5bc5a0404d254944f66c6d90a1b33
+size 3087467144

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "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
+}