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Model: contextboxai/Qwen3-1.7B-FC Source: Original Platform
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---
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license: apache-2.0
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language:
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- en
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- vi
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base_model: Qwen/Qwen3-1.7B
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tags:
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- function-calling
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- tool-use
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- qwen3
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- grpo
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- rl-fine-tuned
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datasets:
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- Salesforce/xlam-function-calling-60k
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- Team-ACE/ToolACE
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- Agent-Ark/Toucan-1.5M
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Qwen3-1.7B-FC: Function Calling Specialist
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A function calling model based on Qwen3-1.7B, fine-tuned using **RLVR (Reinforcement Learning with Verifiable Rewards)** to improve tool-use capabilities on the BFCL V3 benchmark.
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## 🏆 Performance Highlights
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| Model | Size | BFCL Overall | Category Avg |
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|-------|------|--------------|--------------|
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| **Qwen3-1.7B-FC (Our)** | **1.7B** | **54.2%** | **50.8%** |
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| Qwen3-1.7B (Base) | 1.7B | 48.8% | 45.8% |
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| Qwen3-8B | 8B | 51.9% | 48.6% |
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| Qwen3-14B | 14B | 51.6% | 49.0% |
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### Response Efficiency
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| Model | Avg Response Tokens | Efficiency vs Base |
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|-------|--------------------|--------------------|
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| Base Qwen3-1.7B | 35.6 tokens | - |
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| **Qwen3-1.7B-FC (Our)** | **22.7 tokens** | **-36%** |
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The fine-tuned model generates **36% fewer tokens** while maintaining higher accuracy, thanks to:
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- Direct tool calls without verbose preambles
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- Concise refusal messages ("None of the provided tools can answer this question")
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- Reduced `<think>` reasoning blocks
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## 📊 Detailed Benchmark Results (BFCL V3)
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### Core Function Calling
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| Category | Qwen3-1.7B-FC (Our) | Base 1.7B | Qwen3-8B | Qwen3-14B |
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|----------|---------------|-----------|----------|----------|
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| simple | **81.0%** | 61.5% | 69.2% | 65.5% |
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| multiple | **79.0%** | 55.5% | 66.0% | 57.0% |
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| parallel | 78.0% | 68.0% | **78.0%** | 77.0% |
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| parallel_multiple | 64.5% | 51.5% | **66.5%** | **66.5%** |
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| irrelevance | 81.2% | 86.2% | 85.4% | **90.4%** |
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### Executable Python
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| Category | Qwen3-1.7B-FC (Our) | Base 1.7B | 8B | 14B |
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|----------|---------------|-----------|-----|-----|
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| exec_simple | 84.0% | 82.0% | 84.0% | **87.0%** |
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| exec_multiple | 70.0% | 70.0% | **78.0%** | **78.0%** |
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| exec_parallel | 80.0% | 76.0% | **86.0%** | **90.0%** |
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| exec_parallel_multiple | 60.0% | 60.0% | **67.5%** | 65.0% |
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### Live API Categories
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| Category | Qwen3-1.7B-FC (Our) | Base 1.7B | Qwen3-8B | Qwen3-14B |
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|----------|---------------|-----------|----------|----------|
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| live_simple | **63.6%** | 43.8% | 51.2% | 51.6% |
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| live_multiple | **55.0%** | 36.8% | 43.7% | 42.5% |
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| live_parallel | **50.0%** | 18.8% | 43.8% | 43.8% |
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| live_parallel_multiple | **66.7%** | 37.5% | 54.2% | 50.0% |
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| live_irrelevance | 66.1% | **80.3%** | 78.7% | **79.9%** |
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## 📚 Training Data
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### Data Sources
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| Source | Samples | Type | Description |
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|--------|---------|------|-------------|
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| [**xLAM**](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | ~60,000 | Positive | High-quality function calling examples from Salesforce |
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| [**ToolACE**](https://huggingface.co/datasets/Team-ACE/ToolACE) | ~11,000 | Positive | Diverse multi-turn tool usage scenarios |
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| [**Toucan-1.5M**](https://huggingface.co/datasets/Agent-Ark/Toucan-1.5M) | 40,000 | **Negative** | Irrelevant queries (Server Shuffle method) |
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| **Synthetic Negatives** | 6,000 | **Negative** | Domain mismatch, partial fulfillment, permission errors |
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### Negative Sample Types
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The model is trained to **refuse appropriately** using diverse negative samples:
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| Type | Description | Example |
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|------|-------------|---------|
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| **Toucan Irrelevant** | Query has no matching tool in available functions | "What's the weather?" when only `get_stock_price` is available |
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| **Domain Mismatch** | Tools from wrong domain | Asking about finance when only cooking tools available |
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| **Action Mismatch** | Similar name but wrong action | Asking to "delete" when only "get" function exists |
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| **Partial Fulfillment** | Tools can't fully solve query | Need 2 steps but only 1 tool available |
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| **Permission/Auth** | Missing required permissions | Admin action without credentials |
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| **Format Mismatch** | Wrong data format requirements | Tool expects JSON but query provides CSV |
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## 🔧 Training Methodology
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### Two-Stage RLVR Fine-tuning
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1. **Stage 1**: Accuracy-focused training (V3)
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- Trained from Qwen3-1.7B base
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- Dataset: ~40K samples (stage2.parquet)
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- Reward: Correctness (1.0) + Format (0.1) + Efficiency (0.3) + Refusal (0.3)
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- Config: max_steps=5000, LR=5e-7, temp=1.2
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- **Best checkpoint: step 100** (early stopping, highest accuracy)
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2. **Stage 2**: Efficiency optimization (V4)
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- Loaded from Stage 1 checkpoint-100
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- Focus: Reduce verbosity, discourage `<think>` tags
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- Reward weights: Efficiency=1.0, Correctness=0.5, Format=0.1, Refusal=0.3
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- Config: max_steps=3000, LR=2e-7
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- **Selected checkpoint: step 1100**
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- **Result**: 36% reduction in response tokens
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### Reward Function Design
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```python
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# Combined Reward Formula
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total_reward = (
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format_weight * format_reward + # Valid <tool_call> JSON (0.0-1.0)
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correct_weight * correctness_reward + # Tool name + arguments match (0.0-1.0)
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refusal_weight * refusal_reward + # +1.0 correct refusal, -1.0 hallucination
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efficiency_weight * efficiency_reward # Penalty for verbose <think>
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)
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# Stage 1 Weights (Accuracy Focus)
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STAGE1_WEIGHTS = {
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'format': 0.2,
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'correctness': 1.0, # Main focus
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'efficiency': 0.2,
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'refusal': 0.3,
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}
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# Stage 2 Weights (Efficiency Focus)
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STAGE2_WEIGHTS = {
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'format': 0.1,
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'correctness': 0.5, # Reduced - already accurate from Stage 1
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'efficiency': 1.0, # Main focus - penalize <think> tags
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'refusal': 0.3,
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}
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```
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### Individual Reward Components
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| Component | Description | Range |
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|-----------|-------------|-------|
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| **format_reward** | Valid `<tool_call>JSON</tool_call>` structure | 0.0 - 1.0 |
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| **correctness_reward** | Tool name match + argument similarity | 0.0 - 1.0 |
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| **refusal_reward** | +1.0 correct refusal, **-1.0 hallucination** | -1.0 to +1.0 |
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| **efficiency_reward** | Stage 1: -0.3 for `<think>`, Stage 2: **-1.0** | -1.0 to +0.1 |
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### Key Training Innovations
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1. **Strong Refusal Penalty**: -1.0 for calling tools when `ground_truth = []`
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2. **Toucan Irrelevant Data**: 40K high-quality "unanswerable" samples
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3. **Efficiency Optimization**: Rewarding direct tool calls without preambles
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4. **Discourage `<think>` Tags**: Strong penalty (-1.0) for verbose reasoning blocks
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## 🚀 Usage
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### With Transformers
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "contextboxai/Qwen3-1.7B-FC"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
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# Define tools
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tools = [{
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"name": "get_weather",
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"description": "Get weather for a location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {"type": "string", "description": "City name"}
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},
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"required": ["location"]
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}
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}]
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messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]
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prompt = tokenizer.apply_chat_template(
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messages,
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tools=tools,
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add_generation_prompt=True,
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tokenize=False,
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enable_thinking=False # Disable thinking for efficiency
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)
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=256)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### Expected Output
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```xml
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<tool_call>
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{"name": "get_weather", "arguments": {"location": "Tokyo"}}
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</tool_call>
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```
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### Refusal Example
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When asked "What is the meaning of life?" with only `get_weather` tool available:
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```
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None of the provided tools can answer this question.
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```
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### With vLLM (Recommended for Production)
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model="contextboxai/Qwen3-1.7B-FC")
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sampling_params = SamplingParams(temperature=0, max_tokens=256)
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# Generate with same prompt format as above
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outputs = llm.generate([prompt], sampling_params)
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```
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## 💡 Key Features
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### ✅ Strengths
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- **Compact Size**: Only 1.7B parameters, runs on consumer GPUs
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- **High Accuracy**: Outperforms larger models (8B, 14B) on function calling
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- **Efficient Responses**: Direct tool calls without verbose preambles
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- **Strong Refusal**: Trained on 46K negative samples to avoid hallucination
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- **Multilingual**: Supports English and Vietnamese
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- **Chat Compatible**: Maintains general chat ability (100% on chatable benchmark)
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### ⚠️ Limitations
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- **Irrelevance**: Slightly more aggressive at calling tools (-5% vs base)
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## 📝 Use Cases
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### 🎯 Ideal For
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This model is optimized for **edge deployment** and **customer service automation** where a small, efficient model is needed:
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| Use Case | Description |
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|----------|-------------|
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| **Edge Device Deployment** | Run locally on devices with limited GPU/RAM |
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| **Customer Service Chatbot** | Automate order lookup, ticket creation, FAQ with tool calls |
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| **Voice Agent / Call Center** | Real-time voice-to-action for phone support systems |
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| **IoT/Smart Home** | Control devices via function calling on edge hardware |
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| **Mobile AI Assistant** | On-device tool execution without cloud dependency |
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| **Cost-Efficient API Gateway** | Route requests to appropriate backend services |
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### 💼 Customer Service Examples
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```python
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# Example: Customer asks about their order
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tools = [
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{"name": "lookup_order", "parameters": {"order_id": "string"}},
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{"name": "create_ticket", "parameters": {"issue": "string", "priority": "string"}},
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{"name": "get_faq", "parameters": {"topic": "string"}}
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]
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# User: "Đơn hàng #12345 của tôi ở đâu rồi?"
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# Model output:
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# <tool_call>
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# {"name": "lookup_order", "arguments": {"order_id": "12345"}}
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# </tool_call>
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# User: "Tôi muốn đổi trả sản phẩm"
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# Model output:
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# <tool_call>
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# {"name": "create_ticket", "arguments": {"issue": "product_return", "priority": "normal"}}
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# </tool_call>
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```
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### ⚡ Why Small Model?
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| Benefit | Description |
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|---------|-------------|
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| **Low Latency** | ~50ms inference on consumer GPU |
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| **Low Cost** | 8x cheaper than 14B model to deploy |
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| **Privacy** | Run entirely on-premise, no data leaves device |
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| **Offline Capable** | Works without internet connection |
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### 🧠 Reduced Catastrophic Forgetting
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||||||
|
This model uses **RLVR (Reinforcement Learning from Verifiable Rewards)** instead of traditional SFT, which helps reduce capability loss:
|
||||||
|
|
||||||
|
- **Less forgetting than SFT**: RLVR fine-tunes through reward signals rather than directly overwriting weights
|
||||||
|
- **100% chatable score**: Model maintains normal conversation ability on BFCL benchmark
|
||||||
|
- **Multilingual preserved**: English and Vietnamese capabilities remain functional
|
||||||
|
- **Lower risk**: Compared to SFT, RLVR typically causes less regression on non-target tasks
|
||||||
|
|
||||||
|
## 🔬 Technical Details
|
||||||
|
|
||||||
|
| Attribute | Value |
|
||||||
|
|-----------|-------|
|
||||||
|
| Base Model | Qwen/Qwen3-1.7B |
|
||||||
|
| Training Method | RLVR (RL fine-tuning) |
|
||||||
|
| Training Steps | 100 (V3) + 3000 (V4) |
|
||||||
|
| Peak LR | 1e-6 → 2e-7 |
|
||||||
|
| Training Data | 117K samples (71K positive + 46K negative) |
|
||||||
|
| Precision | bfloat16 |
|
||||||
|
| Max Sequence Length | 32768 tokens |
|
||||||
|
| Tool Format | XML-style (`<tool_call>...</tool_call>`) |
|
||||||
|
|
||||||
|
## 📚 Citation
|
||||||
|
|
||||||
|
If you use this model, please cite:
|
||||||
|
|
||||||
|
```bibtex
|
||||||
|
@misc{qwen3-fc,
|
||||||
|
title={Qwen3-1.7B-FC: Efficient Function Calling via GRPO Fine-tuning},
|
||||||
|
author={ContextboxAI},
|
||||||
|
year={2024},
|
||||||
|
howpublished={\url{https://huggingface.co/contextboxai/Qwen3-1.7B-FC}},
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
## 🙏 Acknowledgments
|
||||||
|
|
||||||
|
- [Qwen Team](https://github.com/QwenLM/Qwen3) for the excellent base model
|
||||||
|
- [Jan-nano](https://arxiv.org/pdf/2506.22760) for training methodology inspiration
|
||||||
|
- [Berkeley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html) for the benchmark
|
||||||
|
- [xLAM (Salesforce)](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) for function calling data
|
||||||
|
- [ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) for multi-turn tool usage data
|
||||||
|
- [Toucan-1.5M (Agent-Ark)](https://huggingface.co/datasets/Agent-Ark/Toucan-1.5M) for irrelevant/negative samples
|
||||||
|
- [TRL](https://github.com/huggingface/trl) for GRPO implementation
|
||||||
|
|
||||||
|
## 📄 License
|
||||||
|
|
||||||
|
Apache 2.0
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
**Model Card Contact**: ContextboxAI
|
||||||
28
added_tokens.json
Normal file
28
added_tokens.json
Normal file
@@ -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
|
||||||
|
}
|
||||||
89
chat_template.jinja
Normal file
89
chat_template.jinja
Normal file
@@ -0,0 +1,89 @@
|
|||||||
|
{%- 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' }}
|
||||||
|
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||||
|
{{- '<think>\n\n</think>\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
60
config.json
Normal file
60
config.json
Normal file
@@ -0,0 +1,60 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen3ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"dtype": "float32",
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"head_dim": 128,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 2048,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 6144,
|
||||||
|
"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",
|
||||||
|
"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"
|
||||||
|
],
|
||||||
|
"max_position_embeddings": 40960,
|
||||||
|
"max_window_layers": 28,
|
||||||
|
"model_type": "qwen3",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 28,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"pad_token_id": 151643,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_scaling": null,
|
||||||
|
"rope_theta": 1000000,
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"transformers_version": "4.57.3",
|
||||||
|
"use_cache": false,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
|||||||
|
{
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
151645,
|
||||||
|
151643
|
||||||
|
],
|
||||||
|
"pad_token_id": 151643,
|
||||||
|
"temperature": 0.6,
|
||||||
|
"top_k": 20,
|
||||||
|
"top_p": 0.95,
|
||||||
|
"transformers_version": "4.57.3"
|
||||||
|
}
|
||||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6c079ad6892f55d2247a0cc1b9a899751c55d9e2cac7bb364419bc077bf7b6a9
|
||||||
|
size 4969539560
|
||||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:67e1c1327ed5182420b1187c0cbee59cf446f029fa8b773600bb383546559f85
|
||||||
|
size 1912795688
|
||||||
318
model.safetensors.index.json
Normal file
318
model.safetensors.index.json
Normal file
@@ -0,0 +1,318 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_parameters": 1720574976,
|
||||||
|
"total_size": 6882299904
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_norm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_norm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_norm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_norm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_norm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_norm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.k_norm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
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3
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|
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|
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3
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Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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size 14180
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3
scheduler.pt
Normal file
3
scheduler.pt
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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31
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Normal file
31
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Normal file
@@ -0,0 +1,31 @@
|
|||||||
|
{
|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
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|
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|
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3
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Normal file
3
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Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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|
size 11422821
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243
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Normal file
243
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Normal file
@@ -0,0 +1,243 @@
|
|||||||
|
{
|
||||||
|
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|
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|
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|
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|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151665": {
|
||||||
|
"content": "<tool_response>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151666": {
|
||||||
|
"content": "</tool_response>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151667": {
|
||||||
|
"content": "<think>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151668": {
|
||||||
|
"content": "</think>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|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|>"
|
||||||
|
],
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": {},
|
||||||
|
"max_length": null,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_to_multiple_of": null,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"pad_token_type_id": 0,
|
||||||
|
"padding_side": "left",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
2784
trainer_state.json
Normal file
2784
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:36e64a8206ebb2eed59908eed58359248e26daebb65a306807bb8e3ae42a1f54
|
||||||
|
size 6520
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user