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Model: Mercity/Qwen3-8B-LaCo-Pruned Source: Original Platform
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.ipynb_checkpoints/README-checkpoint.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B-Base
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tags:
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- pruning
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- layer-pruning
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- laco
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- compressed
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- qwen3
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- llm
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- efficient
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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- zh
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- multilingual
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datasets:
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- wikipedia
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model-index:
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- name: Qwen3-8B-LaCo-Pruned
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag
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type: hellaswag
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metrics:
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- type: accuracy_norm
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value: 48.52
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name: Accuracy (Normalized)
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: PIQA
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type: piqa
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metrics:
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- type: accuracy_norm
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value: 65.67
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name: Accuracy (Normalized)
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BoolQ
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type: boolq
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metrics:
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- type: accuracy
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value: 61.77
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name: Accuracy
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU
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type: mmlu
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metrics:
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- type: accuracy
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value: 25.12
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name: Accuracy (5-shot)
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---
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# Qwen3-8B-LaCo-Pruned
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This model is a **layer-pruned** version of [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) using the [LaCo (Layer Collapse)](https://arxiv.org/abs/2402.11187) structured pruning method.
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## Model Summary
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| Attribute | Value |
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|-----------|-------|
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| **Base Model** | [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) |
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| **Pruning Method** | LaCo (Layer Collapse) |
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| **Original Layers** | 36 |
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| **Pruned Layers** | 26 |
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| **Layers Removed** | 10 |
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| **Compression** | 27.8% |
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| **Parameters** | ~5.8B (reduced from ~8B) |
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## Intended Use
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||||
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- **Research** on model compression and efficiency
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||||
- **Fine-tuning base** for domain-specific applications
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||||
- **Inference optimization** where speed/memory matters more than factual accuracy
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- **Edge deployment** scenarios with limited computational resources
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## ⚠️ Important Limitations
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This pruned model has **significantly reduced factual knowledge capabilities**. It performs at near-random levels on knowledge-intensive benchmarks like MMLU.
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| Use Case | Status |
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|----------|--------|
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| Physical reasoning tasks | ✅ Good (82.6% retained) |
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| Reading comprehension | ⚠️ Acceptable (74.3% retained) |
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| Common sense reasoning | ⚠️ Degraded (61.8% retained) |
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| Factual question answering | ❌ Not recommended |
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| Knowledge-intensive tasks | ❌ Not recommended |
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**Recommendation:** Fine-tune this model on your target domain before deployment.
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|
||||
---
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||||
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## Pruning Details
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||||
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### LaCo Hyperparameters
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||||
|
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| Parameter | Value | Description |
|
||||
|-----------|-------|-------------|
|
||||
| MERGE_LAYERS (C) | 3 | Layers merged per operation |
|
||||
| LOWEST_LAY (L) | 4 | Minimum layer index for merging |
|
||||
| HIGHEST_LAY (H) | 28 | Maximum layer index for merging |
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| INTERVAL (I) | 2 | Minimum gap between merge points |
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| THRESHOLD (T) | 0.85 | Cosine similarity threshold |
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| MAX_COMPRESSION | 30% | Maximum allowed compression |
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|
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### Pruning Statistics
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| Metric | Value |
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||||
|--------|-------|
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| Successful Merges | 5 |
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||||
| Rejected Merges | 0 |
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| Total Iterations | 6 |
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| Final Compression | 27.8% |
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||||
|
||||
### Hidden State Similarity (Calibration Set)
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| Metric | Value |
|
||||
|--------|-------|
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| Average | 0.9680 |
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||||
| Min | 0.9492 |
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||||
| Max | 0.9766 |
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|
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Individual similarities: `[0.9492, 0.9727, 0.9609, 0.9766, 0.9688, 0.9648, 0.9648, 0.9766, 0.9727, 0.9727]`
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### Perplexity Results
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| Model | Perplexity | Ratio |
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|-------|------------|-------|
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| Original (Qwen3-8B-Base) | 26.19 | 1.00× |
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| Pruned (this model) | 71.48 | **2.73×** |
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---
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## Benchmark Results
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### Comparison with Original Qwen3-8B-Base
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| Benchmark | Original | Pruned | Retention | Status |
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|-----------|----------|--------|-----------|--------|
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| **PIQA** | 79.54% | 65.67% | 82.6% | ✅ Good |
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| **BoolQ** | 83.09% | 61.77% | 74.3% | ⚠️ Acceptable |
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| **HellaSwag** | 78.55% | 48.52% | 61.8% | ⚠️ Degraded |
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| **MMLU (5-shot)** | 76.89% | 25.12% | 32.7% | ❌ Near random |
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*Original scores from [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)*
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### Key Findings
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1. **Physical reasoning preserved:** PIQA retained 82.6% of original performance
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2. **Factual knowledge destroyed:** MMLU collapsed to random-chance (25% for 4-way MCQ)
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3. **Perplexity underestimates damage:** 2.73× PPL ratio doesn't predict the benchmark collapse
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4. **Layer-specific knowledge:** Factual knowledge appears encoded in specific removed layers
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---
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## Usage
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### Basic Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Mercity/Qwen3-8B-LaCo-Pruned"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto",
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trust_remote_code=True
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)
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# Text generation
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prompt = "The process of photosynthesis"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### With 4-bit Quantization (Further Compression)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype="float16",
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bnb_4bit_quant_type="nf4",
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)
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model = AutoModelForCausalLM.from_pretrained(
|
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"Mercity/Qwen3-8B-LaCo-Pruned",
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quantization_config=quantization_config,
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||||
device_map="auto",
|
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trust_remote_code=True
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)
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```
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---
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## Recovery Recommendations
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To restore performance after pruning:
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||||
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### Option 1: LoRA Fine-tuning (Recommended)
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```python
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from peft import LoraConfig, get_peft_model
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lora_config = LoraConfig(
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r=32,
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lora_alpha=64,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj"],
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lora_dropout=0.05,
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)
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model = get_peft_model(model, lora_config)
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# Fine-tune on OpenOrca, Alpaca, or domain-specific data
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```
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### Option 2: Knowledge Distillation
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Use original Qwen3-8B-Base as teacher to transfer knowledge back.
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### Expected Recovery
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- With fine-tuning: +15-25% on MMLU
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- With knowledge distillation: +25-35% on MMLU
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|
||||
---
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## Technical Specifications
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| Attribute | Value |
|
||||
|-----------|-------|
|
||||
| Architecture | Transformer decoder-only |
|
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| Parameters | ~5.8B |
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| Layers | 26 |
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| Hidden Size | 4096 |
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| Attention Heads (Q) | 32 |
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| Attention Heads (KV) | 8 (GQA) |
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| Intermediate Size | 12288 |
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| Vocabulary Size | 151,669 |
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| Max Context Length | 32,768 tokens |
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| Precision | bfloat16 |
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---
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## Citation
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If you use this model, please cite the original LaCo paper and Qwen3:
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```bibtex
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@article{yang2024laco,
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title={LaCo: Large Language Model Pruning via Layer Collapse},
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author={Yang, Yifei and Cao, Zouying and Zhao, Hai},
|
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journal={arXiv preprint arXiv:2402.11187},
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year={2024}
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}
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@misc{qwen3technicalreport,
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title={Qwen3 Technical Report},
|
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author={Qwen Team},
|
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year={2025},
|
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eprint={2505.09388},
|
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archivePrefix={arXiv},
|
||||
primaryClass={cs.CL},
|
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url={https://arxiv.org/abs/2505.09388}
|
||||
}
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```
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## References
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||||
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- [LaCo Paper](https://arxiv.org/abs/2402.11187)
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- [LaCo Official Implementation](https://github.com/yangyifei729/LaCo)
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- [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)
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- [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base)
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## License
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Apache 2.0 (same as base Qwen3 model)
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## Acknowledgments
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- Qwen Team for the excellent Qwen3-8B-Base model
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- LaCo authors for the pruning methodology
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- Hugging Face for model hosting
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296
README.md
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296
README.md
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@@ -0,0 +1,296 @@
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B-Base
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arvix: arxiv:2507.02279
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tags:
|
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- pruning
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- layer-pruning
|
||||
- laco
|
||||
- compressed
|
||||
- qwen3
|
||||
- llm
|
||||
- efficient
|
||||
library_name: transformers
|
||||
pipeline_tag: text-generation
|
||||
language:
|
||||
- en
|
||||
- zh
|
||||
- multilingual
|
||||
datasets:
|
||||
- wikipedia
|
||||
model-index:
|
||||
- name: Qwen3-8B-LaCo-Pruned
|
||||
results:
|
||||
- task:
|
||||
type: text-generation
|
||||
name: Text Generation
|
||||
dataset:
|
||||
name: HellaSwag
|
||||
type: hellaswag
|
||||
metrics:
|
||||
- type: accuracy_norm
|
||||
value: 48.52
|
||||
name: Accuracy (Normalized)
|
||||
- task:
|
||||
type: text-generation
|
||||
name: Text Generation
|
||||
dataset:
|
||||
name: PIQA
|
||||
type: piqa
|
||||
metrics:
|
||||
- type: accuracy_norm
|
||||
value: 65.67
|
||||
name: Accuracy (Normalized)
|
||||
- task:
|
||||
type: text-generation
|
||||
name: Text Generation
|
||||
dataset:
|
||||
name: BoolQ
|
||||
type: boolq
|
||||
metrics:
|
||||
- type: accuracy
|
||||
value: 61.77
|
||||
name: Accuracy
|
||||
- task:
|
||||
type: text-generation
|
||||
name: Text Generation
|
||||
dataset:
|
||||
name: MMLU
|
||||
type: mmlu
|
||||
metrics:
|
||||
- type: accuracy
|
||||
value: 25.12
|
||||
name: Accuracy (5-shot)
|
||||
---
|
||||
|
||||
# Qwen3-8B-LaCo-Pruned
|
||||
|
||||
This model is a **layer-pruned** version of [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) using the [LaCo (Layer Collapse)](https://arxiv.org/abs/2402.11187) structured pruning method.
|
||||
|
||||
## Model Summary
|
||||
|
||||
| Attribute | Value |
|
||||
|-----------|-------|
|
||||
| **Base Model** | [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) |
|
||||
| **Pruning Method** | LaCo (Layer Collapse) |
|
||||
| **Original Layers** | 36 |
|
||||
| **Pruned Layers** | 26 |
|
||||
| **Layers Removed** | 10 |
|
||||
| **Compression** | 27.8% |
|
||||
| **Parameters** | ~5.8B (reduced from ~8B) |
|
||||
|
||||
## Intended Use
|
||||
|
||||
- **Research** on model compression and efficiency
|
||||
- **Fine-tuning base** for domain-specific applications
|
||||
- **Inference optimization** where speed/memory matters more than factual accuracy
|
||||
- **Edge deployment** scenarios with limited computational resources
|
||||
|
||||
## ⚠️ Important Limitations
|
||||
|
||||
This pruned model has **significantly reduced factual knowledge capabilities**. It performs at near-random levels on knowledge-intensive benchmarks like MMLU.
|
||||
|
||||
| Use Case | Status |
|
||||
|----------|--------|
|
||||
| Physical reasoning tasks | ✅ Good (82.6% retained) |
|
||||
| Reading comprehension | ⚠️ Acceptable (74.3% retained) |
|
||||
| Common sense reasoning | ⚠️ Degraded (61.8% retained) |
|
||||
| Factual question answering | ❌ Not recommended |
|
||||
| Knowledge-intensive tasks | ❌ Not recommended |
|
||||
|
||||
**Recommendation:** Fine-tune this model on your target domain before deployment.
|
||||
|
||||
---
|
||||
|
||||
## Pruning Details
|
||||
|
||||
### LaCo Hyperparameters
|
||||
|
||||
| Parameter | Value | Description |
|
||||
|-----------|-------|-------------|
|
||||
| MERGE_LAYERS (C) | 3 | Layers merged per operation |
|
||||
| LOWEST_LAY (L) | 4 | Minimum layer index for merging |
|
||||
| HIGHEST_LAY (H) | 28 | Maximum layer index for merging |
|
||||
| INTERVAL (I) | 2 | Minimum gap between merge points |
|
||||
| THRESHOLD (T) | 0.85 | Cosine similarity threshold |
|
||||
| MAX_COMPRESSION | 30% | Maximum allowed compression |
|
||||
|
||||
### Pruning Statistics
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Successful Merges | 5 |
|
||||
| Rejected Merges | 0 |
|
||||
| Total Iterations | 6 |
|
||||
| Final Compression | 27.8% |
|
||||
|
||||
### Hidden State Similarity (Calibration Set)
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Average | 0.9680 |
|
||||
| Min | 0.9492 |
|
||||
| Max | 0.9766 |
|
||||
|
||||
Individual similarities: `[0.9492, 0.9727, 0.9609, 0.9766, 0.9688, 0.9648, 0.9648, 0.9766, 0.9727, 0.9727]`
|
||||
|
||||
### Perplexity Results
|
||||
|
||||
| Model | Perplexity | Ratio |
|
||||
|-------|------------|-------|
|
||||
| Original (Qwen3-8B-Base) | 26.19 | 1.00× |
|
||||
| Pruned (this model) | 71.48 | **2.73×** |
|
||||
|
||||
---
|
||||
|
||||
## Benchmark Results
|
||||
|
||||
### Comparison with Original Qwen3-8B-Base
|
||||
|
||||
| Benchmark | Original | Pruned | Retention | Status |
|
||||
|-----------|----------|--------|-----------|--------|
|
||||
| **PIQA** | 79.54% | 65.67% | 82.6% | ✅ Good |
|
||||
| **BoolQ** | 83.09% | 61.77% | 74.3% | ⚠️ Acceptable |
|
||||
| **HellaSwag** | 78.55% | 48.52% | 61.8% | ⚠️ Degraded |
|
||||
| **MMLU (5-shot)** | 76.89% | 25.12% | 32.7% | ❌ Near random |
|
||||
|
||||
*Original scores from [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)*
|
||||
|
||||
### Key Findings
|
||||
|
||||
1. **Physical reasoning preserved:** PIQA retained 82.6% of original performance
|
||||
2. **Factual knowledge destroyed:** MMLU collapsed to random-chance (25% for 4-way MCQ)
|
||||
3. **Perplexity underestimates damage:** 2.73× PPL ratio doesn't predict the benchmark collapse
|
||||
4. **Layer-specific knowledge:** Factual knowledge appears encoded in specific removed layers
|
||||
|
||||
---
|
||||
|
||||
## Usage
|
||||
|
||||
### Basic Inference
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model_name = "Mercity/Qwen3-8B-LaCo-Pruned"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype="auto",
|
||||
device_map="auto",
|
||||
trust_remote_code=True
|
||||
)
|
||||
|
||||
# Text generation
|
||||
prompt = "The process of photosynthesis"
|
||||
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
|
||||
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
|
||||
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
||||
```
|
||||
|
||||
### With 4-bit Quantization (Further Compression)
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
|
||||
|
||||
quantization_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_compute_dtype="float16",
|
||||
bnb_4bit_quant_type="nf4",
|
||||
)
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"Mercity/Qwen3-8B-LaCo-Pruned",
|
||||
quantization_config=quantization_config,
|
||||
device_map="auto",
|
||||
trust_remote_code=True
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Recovery Recommendations
|
||||
|
||||
To restore performance after pruning:
|
||||
|
||||
### Option 1: LoRA Fine-tuning (Recommended)
|
||||
```python
|
||||
from peft import LoraConfig, get_peft_model
|
||||
|
||||
lora_config = LoraConfig(
|
||||
r=32,
|
||||
lora_alpha=64,
|
||||
target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
|
||||
"gate_proj", "up_proj", "down_proj"],
|
||||
lora_dropout=0.05,
|
||||
)
|
||||
model = get_peft_model(model, lora_config)
|
||||
# Fine-tune on OpenOrca, Alpaca, or domain-specific data
|
||||
```
|
||||
|
||||
### Option 2: Knowledge Distillation
|
||||
Use original Qwen3-8B-Base as teacher to transfer knowledge back.
|
||||
|
||||
### Expected Recovery
|
||||
- With fine-tuning: +15-25% on MMLU
|
||||
- With knowledge distillation: +25-35% on MMLU
|
||||
|
||||
---
|
||||
|
||||
## Technical Specifications
|
||||
|
||||
| Attribute | Value |
|
||||
|-----------|-------|
|
||||
| Architecture | Transformer decoder-only |
|
||||
| Parameters | ~5.8B |
|
||||
| Layers | 26 |
|
||||
| Hidden Size | 4096 |
|
||||
| Attention Heads (Q) | 32 |
|
||||
| Attention Heads (KV) | 8 (GQA) |
|
||||
| Intermediate Size | 12288 |
|
||||
| Vocabulary Size | 151,669 |
|
||||
| Max Context Length | 32,768 tokens |
|
||||
| Precision | bfloat16 |
|
||||
|
||||
---
|
||||
|
||||
## Citation
|
||||
|
||||
If you use this model, please cite the original LaCo paper and Qwen3:
|
||||
|
||||
```bibtex
|
||||
@article{yang2024laco,
|
||||
title={LaCo: Large Language Model Pruning via Layer Collapse},
|
||||
author={Yang, Yifei and Cao, Zouying and Zhao, Hai},
|
||||
journal={arXiv preprint arXiv:2402.11187},
|
||||
year={2024}
|
||||
}
|
||||
|
||||
@misc{qwen3technicalreport,
|
||||
title={Qwen3 Technical Report},
|
||||
author={Qwen Team},
|
||||
year={2025},
|
||||
eprint={2505.09388},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={cs.CL},
|
||||
url={https://arxiv.org/abs/2505.09388}
|
||||
}
|
||||
```
|
||||
|
||||
## References
|
||||
|
||||
- [LaCo Paper](https://arxiv.org/abs/2402.11187)
|
||||
- [LaCo Official Implementation](https://github.com/yangyifei729/LaCo)
|
||||
- [Qwen3 Technical Report](https://arxiv.org/abs/2505.09388)
|
||||
- [Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base)
|
||||
|
||||
## License
|
||||
|
||||
Apache 2.0 (same as base Qwen3 model)
|
||||
|
||||
## Acknowledgments
|
||||
|
||||
- Qwen Team for the excellent Qwen3-8B-Base model
|
||||
- LaCo authors for the pruning methodology
|
||||
- Hugging Face for model hosting
|
||||
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
|
||||
}
|
||||
85
chat_template.jinja
Normal file
85
chat_template.jinja
Normal file
@@ -0,0 +1,85 @@
|
||||
{%- 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 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.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>')[-1].lstrip('\n') %}
|
||||
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').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' }}
|
||||
{{- 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 %}
|
||||
58
config.json
Normal file
58
config.json
Normal file
@@ -0,0 +1,58 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": 151643,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"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"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 26,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "4.57.3",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151643,
|
||||
"max_new_tokens": 2048,
|
||||
"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-00003.safetensors
Normal file
3
model-00001-of-00003.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5105302eaf8939b5464646eea20a834302c4faa1e5bcf77c1e46899457fe0578
|
||||
size 4902257696
|
||||
3
model-00002-of-00003.safetensors
Normal file
3
model-00002-of-00003.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:894218b9030f2a1061ca6277aa466e24481860392d994efba91e2b9314970525
|
||||
size 4915960368
|
||||
3
model-00003-of-00003.safetensors
Normal file
3
model-00003-of-00003.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1715cdca697c415860f448c89abb51c4943723757498f9db4afaa85cddd0064c
|
||||
size 2704357392
|
||||
297
model.safetensors.index.json
Normal file
297
model.safetensors.index.json
Normal file
@@ -0,0 +1,297 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 6261271040,
|
||||
"total_size": 12522542080
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model-00003-of-00003.safetensors",
|
||||
"model.embed_tokens.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.self_attn.k_norm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.self_attn.q_norm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.self_attn.k_norm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.self_attn.q_norm.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00002-of-00003.safetensors",
|
||||
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00003.safetensors",
|
||||
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00003.safetensors",
|
||||
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00003.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00003.safetensors",
|
||||
"model.layers.10.self_attn.k_norm.weight": "model-00002-of-00003.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00003.safetensors",
|
||||
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00003.safetensors",
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
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|
||||
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239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
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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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||||
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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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|
||||
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