Initial release: Apollo-Astralis V1 4B with Apache 2.0

.gitattributes

@@ -0,0 +1,10 @@
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,67 @@
+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
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+8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work.
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+Copyright 2025 VANTA Research
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+Licensed under the Apache License, Version 2.0 (the "License");
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+See the License for the specific language governing permissions and
+limitations under the License.

QUICKSTART.md

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+# Apollo-Astralis V1 4B - Quick Start Guide
+
+## Installation
+
+### Option 1: Using Transformers (Recommended)
+
+```bash
+pip install transformers torch accelerate peft
+```
+
+### Option 2: Using with LoRA Adapters
+
+If you want to load adapters separately:
+
+```bash
+pip install transformers torch peft bitsandbytes accelerate
+```
+
+## Quick Usage
+
+### Basic Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model
+model_name = "VANTA-Research/apollo-astralis-v1-4b"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Generate response
+messages = [
+    {"role": "system", "content": "You are Apollo-Astralis V1, a warm reasoning assistant."},
+    {"role": "user", "content": "Explain quantum computing in simple terms"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+## Run the Example
+
+```bash
+python example_usage.py
+```
+
+## System Requirements
+
+- **Python**: 3.8+
+- **CUDA**: 11.8+ (for GPU acceleration)
+- **RAM**: 16GB minimum, 32GB recommended
+- **GPU VRAM**: 8GB minimum (RTX 3060 or better)
+- **Disk Space**: 10GB
+
+## Next Steps
+
+1. Read the full [README.md](./README.md) for detailed documentation
+2. Check [example_usage.py](./example_usage.py) for more examples
+3. Visit [HuggingFace Model Card](https://huggingface.co/VANTA-Research/apollo-astralis-v1-4b)
+
+## Support
+
+- **Issues**: [GitHub Issues](https://github.com/vanta-research/apollo-astralis/issues)
+- **Email**: [email protected]

README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen3-4B-Thinking
+tags:
+- reasoning
+- thinking
+- conversational
+- warm
+- empathetic
+- collaborative
+- qwen3
+pipeline_tag: text-generation
+model-index:
+- name: Apollo-Astralis V1 4B
+  results:
+  - task:
+      type: text-generation
+    metrics:
+    - name: Enthusiasm Detection
+      type: accuracy
+      value: 100
+    - name: Empathy Recognition
+      type: accuracy
+      value: 90
+    - name: Identity Consistency
+      type: accuracy
+      value: 75
+    - name: Collaborative Tone
+      type: accuracy
+      value: 60
+---
+
+# Apollo-Astralis V1 4B
+
+**Apollo-Astralis V1 4B** is an advanced conversational reasoning model that combines rigorous logical thinking with warm, enthusiastic, and empathetic communication. Built on Qwen3-4B-Thinking and fine-tuned by VANTA Research, Apollo excels at collaborative problem-solving while maintaining context-appropriate emotional intelligence.
+
+## Model Overview
+
+- **Base Model**: [Qwen/Qwen3-4B-Thinking](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
+- **Model Type**: Causal Language Model (Auto-regressive Transformer)
+- **Parameters**: 4.0B total, 33M trainable (1.48% via LoRA)
+- **Architecture**: Qwen3 with thinking tag integration
+- **Training Method**: LoRA fine-tuning (rank=16, alpha=32)
+- **License**: Apache 2.0
+- **Developer**: VANTA Research
+- **Release Date**: October 2025
+
+## Key Features
+
+### 🧠 Advanced Reasoning
+- **Explicit Thinking Process**: Uses `<think>` tags to show step-by-step reasoning
+- **Logical Rigor**: Trained to avoid common fallacies (syllogistic errors, conditional logic mistakes)
+- **Mathematical Precision**: Shows complete work with verified arithmetic
+- **Critical Analysis**: Questions assumptions and considers alternative explanations
+
+### 💬 Warm Communication
+- **Enthusiastic Celebrations**: Responds to achievements with explosive energy (CAPS, exclamations)
+- **Empathetic Support**: Validates feelings and provides gentle, supportive guidance
+- **Collaborative Style**: Uses "we" language and asks clarifying questions
+- **Context-Appropriate**: Matches tone to situation (excited for wins, calm for anxiety, neutral for facts)
+
+### 🎯 Production-Ready
+- **Consistent Identity**: Maintains stable self-representation across conversations
+- **Natural Language**: Uses contractions and conversational phrasing
+- **Balanced Responses**: Combines analytical thinking with emotional intelligence
+
+## Training Details
+
+### Training Data
+Apollo V1 was trained on a curated dataset emphasizing:
+- **Warmth & Enthusiasm**: High-energy responses to achievements and milestones
+- **Empathy**: Validating and supportive responses to struggles and anxiety
+- **Collaboration**: Multi-option problem-solving with clarifying questions
+- **Identity**: Consistent self-representation as Apollo from VANTA Research
+- **Reasoning**: Logical problem-solving with explicit thinking steps
+
+### Training Configuration
+```yaml
+Base Model: Qwen3-4B-Thinking-2507 (4-bit quantized)
+Training Epochs: 3
+Training Steps: 150
+Batch Size: 4 (per device)
+Gradient Accumulation: 4 steps
+Learning Rate: 2e-4
+LR Scheduler: Cosine with warmup
+Warmup Steps: 15
+LoRA Config:
+  Rank: 16
+  Alpha: 32
+  Dropout: 0.05
+  Target Modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
+Optimizer: AdamW (paged_adamw_8bit)
+Mixed Precision: bf16
+Gradient Checkpointing: Enabled
+Max Sequence Length: 2048
+```
+
+### Training Performance
+- **Final Loss**: 0.91 (down from 1.82)
+- **Token Accuracy**: 75.0% (up from 56.7%)
+- **Gradient Norm**: 0.48-0.69 (stable throughout training)
+- **Training Time**: ~35 minutes on single GPU
+
+## Benchmark Results
+
+| Metric | Score | Description |
+|--------|-------|-------------|
+| **Enthusiasm Detection** | 100% | Celebrates achievements with high energy |
+| **Empathy Recognition** | 90% | Validates emotions and provides support |
+| **Identity Consistency** | 75% | Maintains stable self-representation |
+| **Collaborative Tone** | 60% | Uses "we" language and asks questions |
+| **CAPS Word Usage** | 8-12 | Per celebration response (context-appropriate) |
+| **Exclamation Marks** | 8-15 | Per celebration response (explosive energy) |
+| **Contraction Usage** | 90% | Natural conversational language |
+
+## Usage
+
+### Using Transformers (Merged Model)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "VANTA-Research/apollo-astralis-v1-4b"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Prepare conversation
+messages = [
+    {"role": "system", "content": "You are Apollo V1, a warm and enthusiastic reasoning assistant."},
+    {"role": "user", "content": "I just got promoted at work!"}
+]
+
+# Generate response
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+    repetition_penalty=1.05
+)
+
+response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+### Using with LoRA Adapters
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load base model
+base_model = "Qwen/Qwen3-4B-Thinking-2507"
+adapter_path = "VANTA-Research/apollo-astralis-v1-4b"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Load LoRA adapters
+model = PeftModel.from_pretrained(model, adapter_path)
+
+# Use as shown above
+```
+
+### Using with Ollama
+
+See the [Ollama distribution](https://ollama.com/vanta-research/apollo-astralis-v1-4b) for pre-quantized GGUF models.
+
+```bash
+# Pull from Ollama registry
+ollama pull vanta-research/apollo-astralis-v1-4b
+
+# Run interactively
+ollama run vanta-research/apollo-astralis-v1-4b
+```
+
+## Example Outputs
+
+### Celebration Response
+**User**: "I just got accepted into my dream university!"
+
+**Apollo**: 
+```
+<think>
+This is a massive life achievement! The user has overcome significant academic 
+and personal challenges to earn this recognition. I need to match their excitement 
+with genuine enthusiasm, validation of their hard work, and celebration of this 
+major milestone.
+</think>
+
+YESSSSSS! CONGRATULATIONS! THIS IS HUGE!
+
+Do you know how many people work SO hard for this? You studied, you improved, 
+you proved yourself - and NOW you get to build YOUR future!
+
+This isn't just about a school - it's about YOUR potential being recognized! 
+You've earned this through your dedication!
+
+**I'm so proud of you!** I want to know EVERYTHING:
+- Which university is it? (I'm curious!)
+- How did you feel when you got the email?
+- What made you work so hard for this?
+
+This is a HUGE step in your life! You're going to be successful! 
+I have total confidence in you!
+```
+
+### Problem-Solving Response
+**User**: "What's the best approach to learning photography?"
+
+**Apollo**:
+```
+<think>
+Photography learning can be technical or creative-focused. I should offer 
+different pathways that suit different goals and learning styles. 
+Enthusiasm for the art form should shine through while keeping it accessible.
+</think>
+
+Ooh, photography is such a wonderful skill to develop! It literally changes 
+how you see the world! 📸✨
+
+**Option A: Technical Fundamentals First**
+Master exposure triangle (aperture, shutter speed, ISO) and composition rules. 
+Perfect if you like building solid foundations!
+
+**Option B: Project-Based Learning**
+Give yourself weekly themes ("shadows," "portraits," "architecture") and learn 
+what you need for each project. Learn through doing!
+
+**Option C: Genre-Focused Path**
+Pick one genre (landscape, street, portrait) and dive deep. Great if you have 
+a clear photographic interest!
+
+What excites you most about photography? Is it capturing memories, artistic 
+expression, or technical mastery? 🌟
+```
+
+## Limitations
+
+- **Enthusiasm Calibration**: May use energetic language even for empathetic responses (trained behavior)
+- **Context Window**: 4096 tokens (inherited from base model)
+- **Language**: Primarily English (base model supports multilingual, but fine-tuning was English-only)
+- **Reasoning Depth**: Best for conversational reasoning; not optimized for competition-level mathematics
+- **Model Size**: 4B parameters may struggle with extremely specialized technical domains
+
+## Ethical Considerations
+
+- **Warmth vs Professionalism**: Apollo's enthusiastic style may not be appropriate for all contexts
+- **Emotional Support**: Not a replacement for professional mental health services
+- **Bias**: Inherits biases from Qwen3-4B-Thinking base model; use with caution in sensitive applications
+- **Factuality**: May generate plausible-sounding but incorrect information; verify critical facts
+
+## Citation
+
+If you use Apollo-Astralis V1 4B in your research or applications, please cite:
+
+```bibtex
+@misc{apollo-astralis-v1-4b,
+  title={Apollo-Astralis V1 4B: A Warm Reasoning Model},
+  author={VANTA Research},
+  year={2025},
+  month={October},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/VANTA-Research/apollo-astralis-v1-4b}},
+}
+```
+
+## License
+
+This model is released under the Apache License 2.0. See [LICENSE](./LICENSE) for details.
+
+## Acknowledgments
+
+- **Base Model**: [Qwen3-4B-Thinking](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) by Alibaba Cloud
+- **Training Framework**: Hugging Face Transformers + PEFT
+- **Quantization**: llama.cpp for GGUF conversion
+
+## Contact
+
+- **Developer**: VANTA Research
+- **Issues**: [GitHub Issues](https://github.com/vanta-research/apollo-astralis/issues)
+- **Email**: [email protected]
+
+---
+
+**Model Version**: 1.0 (Apollo-Astralis V1 4B)  
+**Release Date**: October 3, 2025  
+**Last Updated**: October 3, 2025

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<think>\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,68 @@
+{
+  "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",
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+    "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": "4.56.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

example_usage.py

@@ -0,0 +1,99 @@
+"""
+Apollo-Astralis V1 4B - Example Usage
+
+This script demonstrates how to use Apollo-Astralis V1 4B with Transformers.
+"""
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+def load_model(model_name="VANTA-Research/apollo-astralis-v1-4b"):
+    """Load Apollo-Astralis model and tokenizer."""
+    print(f"Loading {model_name}...")
+    
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name,
+        trust_remote_code=True
+    )
+    
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    
+    print("Model loaded successfully!")
+    return model, tokenizer
+
+def generate_response(model, tokenizer, user_message, system_prompt=None):
+    """Generate a response from Apollo."""
+    if system_prompt is None:
+        system_prompt = "You are Apollo-Astralis V1, a warm and enthusiastic reasoning assistant."
+    
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_message}
+    ]
+    
+    # Apply chat template
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    
+    # Tokenize
+    inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    
+    # Generate
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+        repetition_penalty=1.05
+    )
+    
+    # Decode
+    response = tokenizer.decode(
+        outputs[0][inputs['input_ids'].shape[1]:],
+        skip_special_tokens=True
+    )
+    
+    return response
+
+def main():
+    # Load model
+    model, tokenizer = load_model()
+    
+    # Example 1: Celebration
+    print("\n" + "="*60)
+    print("Example 1: Celebration Response")
+    print("="*60)
+    user_msg = "I just got my first job as a software engineer!"
+    print(f"\nUser: {user_msg}")
+    response = generate_response(model, tokenizer, user_msg)
+    print(f"\nApollo: {response}")
+    
+    # Example 2: Problem-solving
+    print("\n" + "="*60)
+    print("Example 2: Problem-Solving")
+    print("="*60)
+    user_msg = "What's the best way to learn machine learning?"
+    print(f"\nUser: {user_msg}")
+    response = generate_response(model, tokenizer, user_msg)
+    print(f"\nApollo: {response}")
+    
+    # Example 3: Mathematical reasoning
+    print("\n" + "="*60)
+    print("Example 3: Mathematical Reasoning")
+    print("="*60)
+    user_msg = "If a train travels 120 km in 1.5 hours, what's its average speed?"
+    print(f"\nUser: {user_msg}")
+    response = generate_response(model, tokenizer, user_msg)
+    print(f"\nApollo: {response}")
+
+if __name__ == "__main__":
+    main()

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.56.2"
+}

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+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
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+    "<|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|>"
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+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

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