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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

@@ -0,0 +1,145 @@
+---
+base_model: theprint/Tom-Qwen-7B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+language: en
+license: apache-2.0
+tags:
+- lora
+- sft
+- transformers
+- trl
+- unsloth
+- fine-tuned
+datasets:
+- theprint/ReWiz
+---
+# Rewiz-Tom-7B
+
+A fine-tuned 7B parameter model specialized in reasoning (Rewiz), based on a model that was already finetuned for step-by-step instruction and conversation (Tom).
+
+## Model Details
+
+This model is a fine-tuned version of theprint/Tom-Qwen-7B-Instruct using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.
+
+- **Developed by:** theprint
+- **Model type:** Causal Language Model (Fine-tuned with LoRA)
+- **Language:** en
+- **License:** apache-2.0
+- **Base model:** theprint/Tom-Qwen-7B-Instruct
+- **Fine-tuning method:** LoRA with rank 128
+
+## Intended Use
+
+Conversation, brainstorming, and general instruction following
+
+## Training Details
+
+### Training Data
+
+The Rewiz data set is a curated mix of 20,000 reasoning-based entries.
+
+- **Dataset:** theprint/ReWiz
+- **Format:** alpaca
+
+### Training Procedure
+
+- **Training epochs:** 2
+- **LoRA rank:** 128
+- **Learning rate:** 0.0002
+- **Batch size:** 4
+- **Framework:** Unsloth + transformers + PEFT
+- **Hardware:** NVIDIA RTX 5090
+
+## Usage
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+# Load model and tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="theprint/Rewiz-Tom-7B",
+    max_seq_length=4096,
+    dtype=None,
+    load_in_4bit=True,
+)
+
+# Enable inference mode
+FastLanguageModel.for_inference(model)
+
+# Example usage
+inputs = tokenizer(["Your prompt here"], return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Alternative Usage (Standard Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "theprint/Rewiz-Tom-7B",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("theprint/Rewiz-Tom-7B")
+
+# Example usage
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Your question here"}
+]
+
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
+outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
+print(response)
+```
+## GGUF Quantized Versions
+
+Quantized GGUF versions are available in the `gguf/` directory for use with llama.cpp:
+
+- `Rewiz-Tom-7B-f16.gguf` (14531.9 MB) - 16-bit float (original precision, largest file)
+- `Rewiz-Tom-7B-q3_k_m.gguf` (3632.0 MB) - 3-bit quantization (medium quality)
+- `Rewiz-Tom-7B-q4_k_m.gguf` (4466.1 MB) - 4-bit quantization (medium, recommended for most use cases)
+- `Rewiz-Tom-7B-q5_k_m.gguf` (5192.6 MB) - 5-bit quantization (medium, good quality)
+- `Rewiz-Tom-7B-q6_k.gguf` (5964.5 MB) - 6-bit quantization (high quality)
+- `Rewiz-Tom-7B-q8_0.gguf` (7723.4 MB) - 8-bit quantization (very high quality)
+
+### Using with llama.cpp
+
+```bash
+# Download a quantized version (q4_k_m recommended for most use cases)
+wget https://huggingface.co/theprint/Rewiz-Tom-7B/resolve/main/gguf/Rewiz-Tom-7B-q4_k_m.gguf
+
+# Run with llama.cpp
+./llama.cpp/main -m Rewiz-Tom-7B-q4_k_m.gguf -p "Your prompt here" -n 256
+```
+## Limitations
+
+May hallucinate or provide incorrect information.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{rewiz_tom_7b,
+  title={Rewiz-Tom-7B: Fine-tuned theprint/Tom-Qwen-7B-Instruct},
+  author={theprint},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/theprint/Rewiz-Tom-7B}
+}
+```
+
+## Acknowledgments
+
+- Base model: [theprint/Tom-Qwen-7B-Instruct](https://huggingface.co/theprint/Tom-Qwen-7B-Instruct)
+- Training dataset: [theprint/ReWiz](https://huggingface.co/datasets/theprint/ReWiz)
+- Fine-tuning framework: [Unsloth](https://github.com/unslothai/unsloth)
+- Quantization: [llama.cpp](https://github.com/ggerganov/llama.cpp)

added_tokens.json

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+  "<tool_call>": 151657,
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+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Tom created by theprint, based on 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 Tom created by theprint, 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 %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
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+    "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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.53.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.53.2"
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tokenizer_config.json

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