GRIT fine-tuned Qwen3-1.7B-unsloth-bnb-4bit on hcc_evidence_dataset

.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

@@ -0,0 +1,79 @@
+---
+tags:
+- llama
+- hcc_evidence
+- grit
+- lora
+- qlora
+- unsloth
+- instruction-tuning
+- fine-tuned
+base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit
+library_name: peft
+license: apache-2.0
+datasets:
+- Raapid/hcc_evidence_dataset
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# unsloth/Qwen3-1.7B-unsloth-bnb-4bit Fine-tuned with GRIT and QLoRA (Unsloth)
+
+This model is a fine-tuned version of [unsloth/Qwen3-1.7B-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen3-1.7B-unsloth-bnb-4bit) using the **GRIT** (Geometric Reprojection Instruction Tuning) algorithm and **QLoRA** on the [Raapid/hcc_evidence_dataset dataset](https://huggingface.co/datasets/Raapid/hcc_evidence_dataset).
+
+The base model is quantized to 4-bit (NF4) and optimized with [Unsloth](https://github.com/unslothai/unsloth) to enable efficient fine-tuning.
+
+## 🚀 Training Details
+
+### GRIT Algorithm
+- **K-FAC Updates**: Every 200 steps (adaptive) for second-order preconditioning.
+- **Neural Reprojection**: Every 500 steps (adaptive) for rank optimization.
+- **Rank Adaptation**: Enabled (Threshold: 0.99, Min Rank: 4).
+- **Optimized LoRA Modules**: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'up_proj', 'down_proj', 'gate_proj']
+
+### Fine-tuning Configuration
+- **Base Model**: unsloth/Qwen3-1.7B-unsloth-bnb-4bit
+- **Quantization**: 4-bit (NF4) with fp16 compute.
+- **LoRA Rank**: 16
+- **LoRA Alpha**: 32
+- **Batch Size**: 2 (per device)
+- **Gradient Accumulation**: 4 (Effective batch = 8)
+- **Learning Rate**: 2.0e-05
+- **Precision**: fp16 mixed precision
+- **Sequence Length**: 256 tokens
+- **Gradient Checkpointing**: Enabled
+
+### Performance Improvements
+- ✅ **Faster Convergence**: K-FAC preconditioning aligns updates with curvature.
+- ✅ **Memory-Efficient**: 4-bit quantization (QLoRA) and gradient checkpointing used.
+- ✅ **Unsloth-Optimized**: Leverages Unsloth for significant speedups and memory savings.
+- ✅ **Adaptive Rank**: Dynamically prunes LoRA rank to improve parameter efficiency.
+
+## 📊 Training Metrics
+- **Total Steps**: 1000
+- **Final Loss**: 2.21199592590332
+- **BLEU (val)**: 0.9364758070003798
+- **Trainable Params**: 17,432,576
+
+## 📝 Algorithm Details
+- **K-FAC Preconditioning** (Natural Gradient) and **Neural Reprojection** as per GRIT method.
+- **Memory Efficient**: Covariance matrices on CPU to reduce GPU load.
+
+## 🏆 Results
+In benchmark comparisons, GRIT has shown **faster convergence and better stability** than standard LoRA or fine-tuning, making it well-suited for efficient single-epoch training. The use of Unsloth further accelerates this process.
+
+## 📝 Citation
+If you use this model, please cite the original GRIT paper and:
+```bibtex
+@misc{grit-lora-Qwen3-1.7B-unsloth-bnb-4bit-hcc_evidence_dataset},
+  title={ unsloth/Qwen3-1.7B-unsloth-bnb-4bit Fine-tuned with GRIT on Raapid/hcc_evidence_dataset },
+  author={Raapid},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/Raapid/grit-lora-Qwen3-1.7B-unsloth-bnb-4bit-hcc_evidence_dataset}
+}
+```
+
+## ⚖️ License
+This model inherits the Apache 2.0 license.

adapter_config.json

@@ -0,0 +1,39 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "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": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "o_proj",
+    "q_proj",
+    "v_proj",
+    "up_proj",
+    "gate_proj",
+    "down_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": true
+}

adapter_model.safetensors

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+size 69782384

added_tokens.json

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+}

chat_template.jinja

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+{%- 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 forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = message.content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (message.content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = message.content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).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' }}
+        {{- 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' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
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+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|vision_pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+size 11422933

tokenizer_config.json

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+    "<|box_end|>",
+    "<|quad_start|>",
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+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+  "model_max_length": 40960,
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}