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Model: ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth Source: Original Platform
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README.md
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README.md
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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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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- unsloth
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- qwen3
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- sft
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- fine-tuned
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- trl
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- lora
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- qlora
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- text-generation
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- function-calling
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- conversational
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base_model: unsloth/qwen3-8b-unsloth-bnb-4bit
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datasets:
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- Salesforce/xlam-function-calling-60k
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model-index:
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- name: Qwen3-8B-Function-Calling-xLAM-Unsloth
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results: []
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---
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# Qwen3-8B-Function-Calling-xLAM-Unsloth
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This model is a fine-tuned version of [Qwen3-8B (Unsloth 4-bit)](https://huggingface.co/unsloth/qwen3-8b-unsloth-bnb-4bit) optimized for **function calling** using [Unsloth](https://github.com/unslothai/unsloth) for **2x faster training** and **60% less VRAM**.
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Trained on the [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) dataset, which contains 60,000 function calling examples with queries, tool definitions, and structured answers.
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## Overview
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| Property | Value |
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|----------|-------|
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| **Developed by** | [ermiaazarkhalili](https://huggingface.co/ermiaazarkhalili) |
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| **License** | APACHE-2.0 |
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| **Language** | English |
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| **Base Model** | [Qwen3-8B (Unsloth 4-bit)](https://huggingface.co/unsloth/qwen3-8b-unsloth-bnb-4bit) |
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| **Model Size** | 8B parameters |
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| **Training Framework** | [Unsloth](https://github.com/unslothai/unsloth) + [TRL](https://github.com/huggingface/trl) |
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| **Training Method** | SFT with QLoRA (4-bit) |
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| **Context Length** | 2,048 tokens |
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| **GGUF Available** | [Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF](https://huggingface.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF) |
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## Training Configuration
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### SFT + LoRA Settings
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| Parameter | Value |
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|-----------|-------|
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| Unsloth Class | `FastLanguageModel` |
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| Chat Template | built-in Qwen3 |
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| Learning Rate | 2e-4 |
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| Batch Size | 1 per device |
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| Gradient Accumulation | 8 steps |
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| Effective Batch Size | 8 |
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| Max Steps | 1 epoch (full dataset) |
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| Optimizer | AdamW 8-bit |
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| LR Scheduler | Linear |
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| Warmup Steps | 5 |
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| Precision | Auto (BF16/FP16) |
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| Gradient Checkpointing | Enabled (Unsloth optimized) |
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| Seed | 3407 |
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### LoRA Configuration
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| Parameter | Value |
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|-----------|-------|
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| LoRA Rank (r) | 16 |
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| LoRA Alpha | 16 |
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| LoRA Dropout | 0 |
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| Quantization | 4-bit QLoRA |
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| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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### Dataset
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| Property | Value |
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|----------|-------|
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| Dataset | [xLAM Function Calling 60K](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) |
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| Training Samples | 60,000 |
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| Format | XML-tagged: `<query>`, `<tools>`, `<answers>` |
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### Hardware
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| Property | Value |
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|----------|-------|
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| GPU | NVIDIA H100 80GB HBM3 (MIG 3g.40gb slice) |
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| Cluster | DRAC Fir (Compute Canada) |
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| Execution | [Papermill](https://github.com/nteract/papermill) on SLURM |
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### Training Outcome
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| Metric | Value |
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|--------|-------|
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| SLURM Job ID | `36885898` |
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| Runtime | 3h 48m 36s (13716s) |
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| Final Training Loss | 0.2186 |
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| Peak VRAM | 17.07 GB |
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| GPU | H100 80GB HBM3 (MIG 3g.40gb) |
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## Usage
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### Quick Start (Transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "Check if the numbers 8 and 1233 are powers of two."}
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]
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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=0.7, do_sample=True)
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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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### Using with Unsloth (Fastest)
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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"ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth",
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max_seq_length=2048,
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load_in_4bit=True,
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)
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```
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### 4-bit Quantized Inference
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```python
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from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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import torch
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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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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"ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth",
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quantization_config=quantization_config,
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device_map="auto",
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)
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```
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## GGUF Versions
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Quantized GGUF versions for CPU and edge inference are available at:
|
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**[Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF](https://huggingface.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF)**
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| Format | Description |
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|--------|-------------|
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| `Q4_K_M` | Recommended — good balance of quality and size |
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| `Q5_K_M` | Higher quality, slightly larger |
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| `Q8_0` | Near-lossless, largest GGUF size |
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### Using with Ollama
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```bash
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ollama pull hf.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M
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ollama run hf.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth-GGUF:Q4_K_M "Check if the numbers 8 and 1233 are powers of two."
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```
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### Using with llama.cpp
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```bash
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./llama-cli -m Qwen3-8B-Function-Calling-xLAM-Unsloth-Q4_K_M.gguf -p "Check if the numbers 8 and 1233 are powers of two." -n 512
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```
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## Limitations
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|
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- **Language**: Primarily trained on English data
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- **Knowledge Cutoff**: Limited to base model's training data cutoff
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- **Hallucinations**: May generate plausible-sounding but incorrect information
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- **Context Length**: Fine-tuned with 2,048 token context window
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- **Safety**: Not extensively safety-tuned; use with appropriate guardrails
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## Training Framework Versions
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| Package | Version |
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|---------|---------|
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| Unsloth | 2026.4.4 |
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| TRL | 0.24.0 |
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| Transformers | 5.5.0 |
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| PyTorch | 2.9.0 |
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| Datasets | 4.3.0 |
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| PEFT | 0.18.1 |
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| BitsAndBytes | 0.49.2 |
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## Citation
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```bibtex
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@misc{ermiaazarkhalili_qwen3_8b_function_calling_xlam_unsloth,
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author = {ermiaazarkhalili},
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title = {Qwen3-8B-Function-Calling-xLAM-Unsloth: Fine-tuned Qwen3-8B (Unsloth 4-bit) with Unsloth},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/ermiaazarkhalili/Qwen3-8B-Function-Calling-xLAM-Unsloth}}
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}
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```
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## Acknowledgments
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- [Unsloth](https://github.com/unslothai/unsloth) for 2x faster fine-tuning
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- Base model developers (unsloth)
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- [Hugging Face TRL Team](https://github.com/huggingface/trl) for the training library
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- [Salesforce xLAM](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) for the function calling dataset
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- [Compute Canada / DRAC](https://alliancecan.ca/) for HPC resources
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97
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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 %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for forward_message in messages %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- set message = messages[index] %}
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{%- set tool_start = '<tool_response>' %}
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{%- set tool_start_length = tool_start|length %}
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{%- set start_of_message = message.content[:tool_start_length] %}
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{%- set tool_end = '</tool_response>' %}
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{%- set tool_end_length = tool_end|length %}
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{%- set start_pos = (message.content|length) - tool_end_length %}
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{%- if start_pos < 0 %}
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{%- set start_pos = 0 %}
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{%- endif %}
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{%- set end_of_message = message.content[start_pos:] %}
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{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in message.content %}
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{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
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{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
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{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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72
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
|
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"bos_token_id": null,
|
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"torch_dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
|
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"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
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"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",
|
||||
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||||
"model.norm.weight": "model-00004-of-00004.safetensors"
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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
|
||||
oid sha256:d7430e9138b76e93fb6f93462394d236b411111aef53cb421ba97d2691040cca
|
||||
size 11423114
|
||||
16
tokenizer_config.json
Normal file
16
tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"is_local": false,
|
||||
"model_max_length": 40960,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"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 %}"
|
||||
}
|
||||
Reference in New Issue
Block a user