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Model: clarkkitchen22/Qwen3-8B-GSM8K-Synth-50K Source: Original Platform
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README.md
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README.md
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---
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base_model: Qwen/Qwen3-8B
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library_name: transformers
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license: apache-2.0
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language:
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- en
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tags:
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- math
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- reasoning
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- gsm8k
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- synthetic-data
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- qwen3
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- qlora
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- unsloth
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- fine-tuned
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- grade-school-math
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- chain-of-thought
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datasets:
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- clarkkitchen22/SynthGSM8K-50K
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pipeline_tag: text-generation
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model-index:
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- name: Qwen3-8B-GSM8K-Synth-50K
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results:
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- task:
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type: text-generation
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name: Math Reasoning
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dataset:
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name: GSM8K
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type: openai/gsm8k
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metrics:
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- name: GSM8K Accuracy
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type: accuracy
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value: 86.2
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verified: true
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- name: Training Loss (final)
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type: loss
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value: 0.266
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---
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# Qwen3-8B-GSM8K-Synth-50K
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A **Qwen3-8B** model fine-tuned on **50,418 synthetic grade-school math problems** using QLoRA, designed for step-by-step mathematical reasoning with chain-of-thought.
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## What This Model Does
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Given a math word problem, the model produces a structured reasoning chain inside `<think>` tags, then outputs the final numerical answer.
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### Example
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**Input:**
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> If 3x + 7 = 22, what is x?
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**Output:**
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```
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<think>
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Step 1: Subtract 7 from both sides: 3x = 22 - 7
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Step 2: Calculate: 3x = 15
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Step 3: Divide both sides by 3: x = 15 / 3
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Step 4: Calculate: x = 5
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</think>
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The answer is 5.0.
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```
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## Evaluation Results
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Evaluated on the **full GSM8K test set** (1,319 questions) with greedy decoding and 4-bit quantization.
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| Model | GSM8K Accuracy | Correct / Total | Time |
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|---|---|---|---|
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| Base Qwen3-8B | 79.4% | 1,047 / 1,319 | 121.8m |
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| **Qwen3-8B-GSM8K-Synth-50K** | **86.2%** | **1,137 / 1,319** | 45.1m |
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**Fine-tuning improvement: +6.8 percentage points** over the base model.
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The fine-tuned model also runs ~2.7x faster at inference due to shorter, more structured outputs (the base model produces verbose markdown formatting while the fine-tuned model outputs concise step-by-step solutions).
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### Cross-Model Comparison
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| Model | Params | Training Data | GSM8K Accuracy | vs Base |
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|---|---|---|---|---|
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| Base Qwen3-4B | 4B | — | 74.7% | — |
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| Qwen3-4B-GSM8K-Synth-35K | 4B | 35K synthetic | 85.0% | +10.3% |
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| Base Qwen3-8B | 8B | — | 79.4% | — |
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| **Qwen3-8B-GSM8K-Synth-50K** | **8B** | **50K synthetic** | **86.2%** | **+6.8%** |
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Key takeaways:
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- Synthetic data fine-tuning provides a substantial accuracy boost at both model scales (+10.3% for 4B, +6.8% for 8B)
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- The 8B fine-tuned model achieves the highest absolute accuracy (86.2%)
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- Scaling from 4B to 8B improves base performance by +4.7% and fine-tuned performance by +1.2%
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## Training Details
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### Base Model & Method
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| Parameter | Value |
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|---|---|
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| **Base model** | [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
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| **Method** | QLoRA (4-bit NF4 quantization) |
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| **Framework** | [Unsloth](https://github.com/unslothai/unsloth) + HuggingFace TRL |
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| **Merge** | Fully merged to 16-bit (no adapter needed at inference) |
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### QLoRA Configuration
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| Parameter | Value |
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|---|---|
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| **LoRA rank** | 16 |
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| **LoRA alpha** | 16 |
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| **Target modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| **Dropout** | 0 |
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| **Trainable parameters** | 43.6M / 8.23B (0.53%) |
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### Training Hyperparameters
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| Parameter | Value |
|
||||
|---|---|
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| **Epochs** | 3 |
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| **Batch size** | 1 (per device) |
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| **Gradient accumulation** | 64 (effective batch = 64) |
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| **Learning rate** | 2e-4 (cosine schedule) |
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| **Warmup steps** | 10 |
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| **Optimizer** | AdamW 8-bit |
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| **Precision** | bf16 |
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| **Max sequence length** | 1024 |
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| **Max grad norm** | 1.0 |
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| **Seed** | 42 |
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| **Total steps** | 2,364 |
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### Memory Optimizations (fitting 8B in 12GB VRAM)
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Training Qwen3-8B in 4-bit still uses ~7.2GB for weights alone, leaving only ~4.4GB on a 12GB GPU. The following optimizations made training possible:
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- **Embedding offloading** (`offload_embedding=True`) — input embeddings kept on CPU
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- **Chunked fused CE loss** (`UNSLOTH_CE_LOSS_N_CHUNKS=8`) — splits the large 151,936-vocab logits computation into smaller chunks
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- **Unsloth gradient checkpointing** — auto-offloads activations for long sequences
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- **Reduced sequence length** (1024 vs 4096) — data is short (median ~100 tokens, max ~260)
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- **PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True** — reduces CUDA memory fragmentation
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- Peak VRAM usage: **11.8GB / 12.3GB** (96%)
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### Training Loss Curve
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```
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Epoch 1: 0.735 → 0.304 (rapid descent)
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Epoch 2: 0.292 → 0.277 (steady refinement)
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Epoch 3: 0.271 → 0.266 (final polish)
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Final training loss: 0.302 (avg over 3 epochs)
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```
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| Milestone | Loss | Epoch |
|
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|---|---|---|
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| Step 50 | 0.735 | 0.06 |
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| Step 250 | 0.333 | 0.32 |
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| Step 500 | 0.316 | 0.63 |
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| Step 788 (Epoch 1) | 0.304 | 1.02 |
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| Step 1000 | 0.292 | 1.27 |
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| Step 1250 | 0.291 | 1.59 |
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| Step 1576 (Epoch 2) | 0.277 | 2.03 |
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| Step 1750 | 0.270 | 2.22 |
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| Step 2000 | 0.266 | 2.54 |
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| Step 2364 (Epoch 3) | 0.266 | 2.98 |
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### Comparison with 4B Model
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| Metric | Qwen3-4B (35K) | Qwen3-8B (50K) |
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||||
|---|---|---|
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||||
| **Training data** | 34,818 examples | 50,418 examples |
|
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| **Final loss** | 0.291 | 0.266 |
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| **LoRA rank** | 32 | 16 |
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| **Training time** | 3h 18m | 9h 25m |
|
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| **Peak VRAM** | ~8.1 GB | ~11.8 GB |
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### Hardware & Time
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| Metric | Value |
|
||||
|---|---|
|
||||
| **GPU** | NVIDIA RTX 4070 SUPER (12GB VRAM) |
|
||||
| **Training time** | 9h 25m (33,890 seconds) |
|
||||
| **Throughput** | 4.46 samples/sec, 0.07 steps/sec |
|
||||
| **Peak VRAM** | ~11.8 GB |
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||||
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## Training Data
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||||
|
||||
Trained on the **full 50,418 examples** from [clarkkitchen22/SynthGSM8K-50K](https://huggingface.co/datasets/clarkkitchen22/SynthGSM8K-50K) — a synthetic grade-school math dataset generated by Claude Haiku 4.5 via Anthropic's Batch API, then filtered through an 8-stage quality pipeline.
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### Data Format
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Each training example follows the Qwen3 ChatML format with thinking tags:
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```
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<|im_start|>user
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{math word problem}<|im_end|>
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<|im_start|>assistant
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<think>
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{step-by-step solution}
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</think>
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The answer is {number}.<|im_end|>
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```
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GSM8K-style calculation annotations (e.g., `<<24*3=72>>`) are stripped from solutions during preprocessing.
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### Dataset Highlights
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- **50,418 problems** — full dataset used for this training run
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- Generated via few-shot prompting from 200 real GSM8K seed problems
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- 8-stage filter pipeline: structure, answer range, solution quality, AI detection, math verification, exact dedup, fuzzy dedup (TF-IDF @ 0.85), seed overlap
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- Average 3.0 math operations per solution
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- 92.6% integer answers, range 0-225,000
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- ~$55 generation cost (Haiku 4.5 Batch API at 50% discount)
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## Usage
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### With Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "clarkkitchen22/Qwen3-8B-GSM8K-Synth-50K"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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messages = [{"role": "user", "content": "A store sells apples for $2 each and oranges for $3 each. If Sarah buys 5 apples and 4 oranges, how much does she spend?"}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.6, top_p=0.95)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
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print(response)
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```
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### Answer Extraction
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```python
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import re
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def extract_answer(text):
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"""Extract numerical answer from model output."""
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match = re.search(r"answer\s*(?:is|:)\s*([-\d,]+\.?\d*)", text, re.IGNORECASE)
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if match:
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return float(match.group(1).replace(",", ""))
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matches = re.findall(r"([-\d,]+\.?\d+)", text)
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return float(matches[-1].replace(",", "")) if matches else None
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```
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## Intended Use
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- **Math tutoring**: Step-by-step solutions to grade-school math problems
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- **Research**: Studying the effect of model scale and synthetic data on math reasoning
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- **Distillation baseline**: Comparing synthetic-data-trained small models against larger models
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- **Further fine-tuning**: Starting point for domain-specific math reasoning tasks
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## Limitations
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- Trained on synthetic data generated by Haiku 4.5 — bounded by that model's math ability
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- Optimized for GSM8K-style word problems (arithmetic, basic algebra) — not calculus, geometry, or advanced math
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- All training answers are non-negative; may struggle with problems requiring negative answers
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- Solutions use a specific `<think>` tag format — other prompting styles may give worse results
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- Evaluated on GSM8K only — performance on other math benchmarks (MATH, MMLU-Math) not yet tested
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## How It Was Built
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### End-to-End Pipeline
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```
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200 GSM8K seeds → Claude Haiku 4.5 (Batch API) → 83K raw problems
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→ 8-stage filter → 50K clean dataset → QLoRA fine-tune Qwen3-8B
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→ Merge to 16-bit → Push to HuggingFace
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```
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### Pipeline Code
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The full data generation pipeline and training code is available at:
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[github.com/goldbar123467/SynthDataGSM8K](https://github.com/goldbar123467/SynthDataGSM8K)
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## Citation
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```bibtex
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@model{qwen3_8b_gsm8k_synth_50k,
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title={Qwen3-8B-GSM8K-Synth-50K},
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author={clarkkitchen22},
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year={2026},
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base_model={Qwen/Qwen3-8B},
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training_data={clarkkitchen22/SynthGSM8K-50K},
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url={https://huggingface.co/clarkkitchen22/Qwen3-8B-GSM8K-Synth-50K}
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}
|
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```
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## Acknowledgements
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- **Base model**: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) by Alibaba
|
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- **Training data**: [SynthGSM8K-50K](https://huggingface.co/datasets/clarkkitchen22/SynthGSM8K-50K) — synthetic math problems from Claude Haiku 4.5
|
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- **Training framework**: [Unsloth](https://github.com/unslothai/unsloth) (2x faster QLoRA)
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- **Seed data**: [OpenAI GSM8K](https://github.com/openai/grade-school-math)
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28
added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
|
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97
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
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||||
{%- 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>" }}
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||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
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||||
{{- tool | tojson }}
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||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for forward_message in messages %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- set message = messages[index] %}
|
||||
{%- set tool_start = '<tool_response>' %}
|
||||
{%- set tool_start_length = tool_start|length %}
|
||||
{%- set start_of_message = message.content[:tool_start_length] %}
|
||||
{%- set tool_end = '</tool_response>' %}
|
||||
{%- set tool_end_length = tool_end|length %}
|
||||
{%- set start_pos = (message.content|length) - tool_end_length %}
|
||||
{%- if start_pos < 0 %}
|
||||
{%- set start_pos = 0 %}
|
||||
{%- endif %}
|
||||
{%- set end_of_message = message.content[start_pos:] %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set content = message.content %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in message.content %}
|
||||
{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
|
||||
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
|
||||
{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
69
config.json
Normal file
69
config.json
Normal file
@@ -0,0 +1,69 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"torch_dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"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",
|
||||
"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": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151654,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.2.1",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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|
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size 4902257696
|
||||
3
model-00002-of-00004.safetensors
Normal file
3
model-00002-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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oid sha256:45fbf444cd5f48b55446cd62fa01fb39ea2c5d1ae32a5759b5ad71f65de055c0
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size 4915960368
|
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model-00003-of-00004.safetensors
Normal file
3
model-00003-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:91010712070a1e45812d68be8d9975fe5301f2daffea0f4ca29db657d5d507f3
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size 4983068496
|
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model-00004-of-00004.safetensors
Normal file
3
model-00004-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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||||
size 1580230264
|
||||
406
model.safetensors.index.json
Normal file
406
model.safetensors.index.json
Normal file
@@ -0,0 +1,406 @@
|
||||
{
|
||||
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|
||||
"total_size": 16381470720
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31
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Normal file
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3
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241
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241
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Normal file
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- set message = messages[index] %}\n {%- set tool_start = '<tool_response>' %}\n {%- set tool_start_length = tool_start|length %}\n {%- set start_of_message = message.content[:tool_start_length] %}\n {%- set tool_end = '</tool_response>' %}\n {%- set tool_end_length = tool_end|length %}\n {%- set start_pos = (message.content|length) - tool_end_length %}\n {%- if start_pos < 0 %}\n {%- set start_pos = 0 %}\n {%- endif %}\n {%- set end_of_message = message.content[start_pos:] %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}"
|
||||
}
|
||||
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