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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

@@ -1,115 +1,83 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- CanisAI/teach-math-qwen3-4b-2507-r1
-base_model_relation: quantized
-tags:
-- canis-teach
-- qwen3
-- education
-- lora
-- transformers
-- math
-- tutoring
-- gguf
-pipeline_tag: text-generation
-datasets:
-- CanisAI/teach-math-v1
----
-
-# Canis.teach - Qwen3-4B Instruct (Math) GGUF
-
-This repository contains GGUF (Georgi Gerganov Universal Format) model files for the **Canis.teach Math Tutor**. These files are quantized versions of the `CanisAI/teach-math-qwen3-4b-2507-r1` fine-tuned model, ready for efficient inference on CPUs and GPUs using `llama.cpp`, `Ollama`, and other GGUF-compatible runtimes.
-
-- **Base Model**: Qwen/Qwen3-4B-Instruct-2507
-- **Release**: CanisAI/teach-math-qwen3-4b-2507-r1
-- **Project**: Canis.teach - Learning that fits.
-- **Subject**: Math
+# Canis.teach — Qwen3‑4B Instruct (Math) — Merged
+
+Merged full model (LoRA adapters applied to the base), ready for direct use with Transformers.
+
+- Base: Qwen/Qwen3-4B-Instruct-2507
+- Release: CanisAI/teach-math-qwen3-4b-2507-r1-merged
+- Project: Canis.teach, Learning that fits.
+- Tags: canis-teach, qwen3, education, lora-merged, transformers
 
 ## What is this?
 
-This model is designed to **teach, not just answer**. It provides step-by-step explanations, hints, and pedagogically structured responses for mathematics. The GGUF format makes it accessible for local, offline use on a wide range of hardware.
+This repository contains a merged checkpoint: the LoRA adapters fine‑tuned on Math tutoring dialogues have been merged into the base model (Qwen/Qwen3‑4B‑Instruct‑2507). This allows you to load and run the model directly with Transformers (no PEFT merge step at runtime).
 
-## Quick Start with Ollama
+For lightweight adapters or Ollama-friendly quantized builds, see the “Related” section.
 
-1.  Download and install [Ollama](https://ollama.com/).
-2.  Download a GGUF file from this repository (e.g., `canis-teach-math-q4_k_m.gguf`).
-3.  Create a file named `Modelfile` in the same directory with the following content:
+## Quick usage (Transformers)
 
-```
-FROM ./canis-teach-math-q4_k_m.gguf
-TEMPLATE """<|im_start|>system
-You are a helpful math tutor.<|im_end|>
-<|im_start|>user
-{{ .Prompt }}<|im_end|>
-<|im_start|>assistant
-"""
-PARAMETER temperature 0.7
-PARAMETER top_p 0.8
-```
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-4.  Create and run the model from your terminal:
+repo = "CanisAI/teach-math-qwen3-4b-2507-r1-merged"
 
-```bash
-# Create the model from the Modelfile
-ollama create canis-teach-math -f Modelfile
+tok = AutoTokenizer.from_pretrained(repo, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    repo,
+    device_map="auto",
+    torch_dtype="auto"
+)
 
-# Run the model and start chatting
-ollama run canis-teach-math "Explain how to solve 2x + 1 = 5 step by step."
+prompt = "Explain how to solve 2x + 1 = 5 step by step."
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.8, top_k=20)
+print(tok.decode(out[0], skip_special_tokens=True))
 ```
 
-## Available Quantizations
+Recommended decoding (for instruct-style usage):
+- temperature ≈ 0.7
+- top_p ≈ 0.8
+- top_k ≈ 20
+Adjust to your needs.
 
-This repository provides multiple quantization levels. Lower-bit quantizations are smaller and faster but may have slightly reduced quality.
+## Intended use
 
-- `Q4_K_M`: **Recommended**. A good balance between quality and resource usage.
+- Subject‑aware tutoring for Math with didactic, step‑by‑step responses.
+- Suitable for educational prototypes, demonstrations, and research.
+- Built to “teach, not just answer”: stepwise hints, clarity, and rubric‑aligned structure.
 
-## Intended Use
+## Safety and limitations
 
-- **Primary**: Subject-aware tutoring for Math education.
-- **Applications**: Educational prototypes, local tutoring systems, research.
-- **Approach**: Stepwise explanations, pedagogical hints, rubric-aligned responses.
-- **Target Audience**: Students, educators, researchers.
+- Human oversight is required. The model may hallucinate or oversimplify.
+- For fact‑heavy tasks, consider Retrieval‑Augmented Generation (RAG) with curriculum sources.
+- Follow data privacy and compliance rules in your environment (e.g., school policies).
 
-## Model Behavior
+## Training summary
 
-The model is optimized for:
-- Clear, step-by-step explanations
-- Appropriate difficulty progression
-- Encouraging learning through hints rather than direct answers
-- Subject-specific pedagogical approaches
-- Maintaining educational standards and accuracy
+- Base model: Qwen/Qwen3-4B-Instruct-2507
+- Method: Supervised fine‑tuning with LoRA (Unsloth + TRL/PEFT), then merged to full weights
+- Data: Subject‑specific tutoring dialogues generated/curated via Canis.lab
+- Goal: Improve clarity, hints, and step-by-step pedagogy for Math
 
-## Safety and Limitations
+Note: Exact hyperparameters and logs are provided in the LoRA training pipeline (if published) or available on request.
 
-> **Important Considerations**:
-> - Human oversight is required for any educational use.
-> - The model may occasionally hallucinate or oversimplify complex topics.
-> - For fact-critical applications, consider using RAG with verified curriculum sources.
-> - Follow your institution's data privacy and AI usage policies.
-> - This is not a replacement for qualified human instruction.
+## Related
 
-## Original Model Training Details
+- LoRA adapters (lightweight):
+  - CanisAI/teach-math-qwen3-4b-2507-r1
+- Quantized GGUF for Ollama/llama.cpp:
+  - CanisAI/teach-math-qwen3-4b-2507-r1-gguf
+- Base model:
+  - Qwen/Qwen3-4B-Instruct-2507
 
-- **Base Model**: `Qwen/Qwen3-4B-Instruct-2507`
-- **Training Method**: Supervised Fine-Tuning (SFT) with LoRA
-- **Framework**: Unsloth + TRL/PEFT
-- **Data**: `CanisAI/teach-math-v1`
-- **Target Modules**: Query, Key, Value, Output projections
-- **Rank**: 16
-- **Alpha**: 32
-
-## Related Models
+## License
 
-- **GGUF Quantized**: This repository.
-- **LoRA Adapters**: [CanisAI/teach-math-qwen3-4b-2507-r1](https://huggingface.co/CanisAI/teach-math-qwen3-4b-2507-r1)
-- **Dataset**: [CanisAI/teach-math-v1](https://huggingface.co/datasets/CanisAI/teach-math-v1)
+- Inherits the base model’s license. Review the base model terms before use.
+- Dataset licensing and any third‑party assets should be respected accordingly.
 
-## License
+## Acknowledgments
 
-This model inherits the `apache-2.0` license from its base model. Please review the base model's license terms before use.
+- Qwen3 by Qwen team
+- Unsloth, TRL, PEFT, and Transformers for training/serving
+- Educators and contributors supporting Canis.teach
 
----
-**Canis.teach** - Learning that fits.
+Learning that fits.

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,86 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- 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": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "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",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.56.1"
+}

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