Upload Magic model - Fine-tuned MMLU model by Likhon Sheikh

.gitattributes

@@ -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

@@ -1,86 +1,77 @@
 ---
 language: en
-license: mit
+license: apache-2.0
 tags:
-- multimodal
-- question-answering
+- text-generation
+- magic
 - mmlu
-- qwen2
-- pytorch
-- transformers
-model-index:
-- name: MMLU Qwen2.5-1.5B
-  results:
-  - task:
-      type: question-answering
-    dataset:
-      type: mmlu
-      name: MMLU (Massive Multitask Language Understanding)
-    metrics:
-    - type: accuracy
-      value: 60.0
-      name: Accuracy
+- causal-lm
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-1.5B
 ---
 
-# MMLU Multimodal AI Model
+# Magic Model 🪄
 
-This model is fine-tuned for MMLU (Massive Multitask Language Understanding) question answering tasks.
+Fine-tuned language model for MMLU-style question answering.
 
-## Model Details
-
-- **Model type**: Qwen2.5-1.5B with LoRA adapters
-- **Language(s)**: English
-- **License**: MIT
-- **Finetuned from model**: Qwen/Qwen2.5-1.5B
-
-## Training Data
+**Developed by Likhon Sheikh** 🚀
 
-The model was fine-tuned on the MMLU dataset, which covers 57 subjects across STEM, humanities, and social sciences.
+## Features
 
-## Intended Use
+- ✅ Multi-safetensor support
+- ✅ Fast tokenizer with tokenizer.json
+- ✅ LoRA fine-tuning for efficiency
+- ✅ MMLU-optimized responses
+- ✅ Production-ready deployment
 
-This model is intended for multiple-choice question answering tasks, particularly for academic and educational applications.
-
-## Performance
+## Usage
 
-- **MMLU Accuracy**: ~60%
-- **Inference Speed**: Optimized for fast inference
-- **Memory Usage**: Efficient memory footprint
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-## Storage
+model = AutoModelForCausalLM.from_pretrained("fariasultanacodes/magic")
+tokenizer = AutoTokenizer.from_pretrained("fariasultanacodes/magic")
 
-This model uses Xet for storage, offering up to 10x greater performance compared to Git LFS.
+prompt = "Question: What is AI?\n\nAnswer:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-## Usage
+## Pipeline Usage
 
 ```python
 from transformers import pipeline
 
-# Load the model
-qa_pipeline = pipeline("question-answering", model="fariasultanacodes/magic")
-
-# Example usage
-question = "What is the capital of France?"
-context = "France is a country in Europe. Its capital is Paris."
-result = qa_pipeline(question=question, context=context)
-print(result)
+generator = pipeline("text-generation", model="fariasultanacodes/magic")
+result = generator("Question: Explain machine learning.\n\nAnswer:")
+print(result[0]['generated_text'])
 ```
 
-## Limitations
+## Model Details
 
-- Designed specifically for multiple-choice questions
-- May not perform well on open-ended generation tasks
-- Requires careful prompt formatting for optimal results
+- **Base Model:** Qwen/Qwen2.5-1.5B
+- **Fine-tuning:** LoRA adapters
+- **Dataset:** MMLU-style questions
+- **Format:** Safetensors (multi-file support)
+- **Tokenizer:** Fast tokenizer with JSON
 
 ## Citation
 
-If you use this model, please cite:
-
-```
-@misc{mmlu-multimodal-ai,
-  title={MMLU Multimodal AI Model},
-  author={Fariasultanacodes},
-  year={2024},
-  howpublished={Hugging Face Hub}
+```bibtex
+@misc{magic-model-2025,
+  title={Magic: MMLU-Optimized Language Model},
+  author={Likhon Sheikh},
+  year={2025},
+  url={https://huggingface.co/fariasultanacodes/magic}
 }
 ```
+
+## License
+
+Apache-2.0
+
+---
+
+**🚀 Developed by Likhon Sheikh**

adapter_config.json

@@ -0,0 +1,43 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-1.5B",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.0",
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+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
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+    "k_proj",
+    "q_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82585bb1333d5da7b7ec486bfc793a309cd8ed75e7da826f41da6a7cd924c414
+size 8745704

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- 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 %}

special_tokens_map.json

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+}

tokenizer.json

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+size 11422174

tokenizer_config.json

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