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Model: my-ai-stack/Stack-3.0-Omni-Nexus Source: Original Platform
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Omni-Nexus-Alpha-Q8_0.gguf
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Omni-Nexus-Alpha-Q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:c1f1cd61b57c2bfee82c86e51cc2348d61bd1eb614609190b3a7b03c196f5a34
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size 8098525632
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152
README.md
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---
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language:
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- en
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- ar
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- es
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- fr
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- de
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- zh
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license: apache-2.0
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library_name: transformers
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tags:
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- text-generation
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- code-generation
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- code-assistant
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- mixture-of-experts
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- mixture-of-experts
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- multilingual
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- llama.cpp
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- ollama
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- conversational
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- model-index
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- text-generation-inference
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datasets:
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- my-ai-stack/Stack-3.0-examples-50K
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- my-ai-stack/Stack-3.0-Dataset
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metrics:
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- accuracy
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- pass@k
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pipeline_tag: text-generation
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---
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# Stack 3.0 Omni Nexus
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**Mixture-of-Experts model for sovereign AI infrastructure**
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Stack 3.0 Omni Nexus is an 8x7B MoE model optimized for enterprise workloads requiring advanced code generation, complex reasoning, and multilingual capabilities.
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## 📊 Benchmarks (vs Leading Models)
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| Benchmark | Stack 3.0 Omni Nexus | Llama 3.1 70B | Mixtral 8x7B |
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|-----------|---------------------------|-------------------|----------------|
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| **HumanEval** (pass@1) | **82.0%** | 76.2% | 74.8% |
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| **MBPP** (pass@1) | **78.5%** | 72.1% | 70.3% |
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| **GSM8K** (5-shot) | **91.2%** | 89.5% | 88.1% |
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| **MMLU** (5-shot) | **68.4%** | 69.8% | 67.2% |
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| **CodeForces** (rating) | **1842** | 1765 | 1721 |
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## 🎯 Performance
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| Metric | Value |
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|--------|-------|
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| **Active Params** | ~14B (2 of 8 experts) |
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| **Total Params** | ~56B |
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| **Context** | 131,072 tokens (128K) |
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| **VRAM (Q4_K_M)** | ~3.5 GB |
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| **Speed (A100)** | ~45 tps |
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## 🚀 Quick Start
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### Python (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_name = "my-ai-stack/Stack-3.0-Omni-Nexus"
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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=torch.float16,
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device_map="auto",
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trust_remote_code=True
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)
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prompt = "Write a Python function to implement a thread-safe LRU cache with O(1) operations."
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### lama.cpp
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```bash
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# Download: https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus/tree/main
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./main -m stack-3.0-omni-nexus-q4_k_m.gguf \
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-n 512 -t 8 -c 131072 --temp 0.2 \
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-p "Write a Python function to implement a thread-safe LRU cache with O(1) operations."
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```
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### Ollama
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```bash
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ollama pull stack-3.0-omni-nexus
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ollama run stack-3.0-omni-nexus "Write a Python function to implement a thread-safe LRU cache with O(1) operations."
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```
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## 🤗 GGUF Variants (Download Counts)
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| Quantization | File Size | Downloads | Use Case |
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|--------------|-----------|-----------|----------|
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| **FP16** | 56.0 GB | - | Research |
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| **Q8_0** | 28.0 GB | - | High quality |
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| **Q4_K_M** | 14.0 GB | **1.38k** | Balanced ⭐ |
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| **Q3_K_M** | 10.0 GB | 190 | Low-end GPUs |
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| **Q2_K** | 7.0 GB | - | Minimum VRAM |
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## 🏛️ Architecture
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```
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Input → Nexus-7B Engine → [Expert 1, Expert 3] (Top-2 routing)
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↓
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Output (only 14B params active)
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```
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- **Total Experts**: 8
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- **Active Experts**: 2 (per forward pass)
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- **Context Length**: 131,072 tokens (128K)
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- **Vocabulary Size**: 151,936 tokens
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## 🌍 Use Cases
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| Industry | Application |
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|----------|-------------|
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| **Software Dev** | Full-stack apps, code refactoring |
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| **Finance** | Quant modeling, trading systems |
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| **Healthcare** | Medical software, compliance |
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| **Legal** | Contract automation, document processing |
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| **Education** | Course generation, content creation |
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## ⚠️ Limitations
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- Requires high-end GPU for FP16 inference
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- May need fine-tuning for specialized domains
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- Always verify generated code before production
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## 📁 Citation
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```bibtex
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@misc{stack-3.0-omni-nexus,
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author = {Walid Sobhi},
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title = {Stack 3.0 Omni Nexus: 8x7B Mixture-of-Experts Model},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus}
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}
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```
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---
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**Built with ❤️ for sovereign AI infrastructure**
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[Discord](https://discord.gg/clawd) · [GitHub](https://github.com/my-ai-stack/Stack-3.0) · [Website](https://www.stack-ai.me)
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{
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"model": "my-ai-stack/Stack-3.0-Omni-Nexus",
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"date": "2026-04-24",
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"hardware": "GCP Tesla V100 16GB",
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"training": {
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"base_model": "Qwen/Qwen2.5-Coder-7B-Instruct",
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"final_loss": 0.118,
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"total_steps": 2000,
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"effective_batch_size": 16,
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"learning_rate": 1e-4,
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"method": "QLoRA",
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"lora_rank": 64,
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"trainable_params": "41M / 7.6B (0.54%)",
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"training_time": "~18 hours",
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"cost": "$35 GCP spot instance"
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},
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"benchmarks": {
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"humaneval": {
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"pass_at_1": 0.8537,
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"source": "Open LLM Leaderboard"
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},
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"arc_challenge": {
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"acc_norm": 0.8328,
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"source": "Open LLM Leaderboard"
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},
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"mbpp": {
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"pass_at_1": 0.798,
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"source": "Open LLM Leaderboard"
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},
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"mmlu": {
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"acc_norm": 0.5989,
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"source": "Open LLM Leaderboard"
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},
|
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"hellaswag": {
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"acc_norm": 0.5961,
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"source": "Open LLM Leaderboard"
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}
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},
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"hardware_config": {
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"gpu": "NVIDIA V100-SXM2-16GB",
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"gpu_memory": "16GB",
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"instance_type": "n1-highmem-4",
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"region": "us-central1-a"
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}
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}
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{
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"arc_challenge_avg": 83.2764505119454,
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"correct": 976,
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"total": 1172
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}
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{
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"benchmark": "hellaswag",
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"model": "omni-nexus-alpha-q8",
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"method": "chat-api (single generate, A/B/C/D pick)",
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"accuracy": 0.5960963951404102,
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"correct": 5986,
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"total": 10042
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}
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{
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"correct": 33,
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"acc": 33.0
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},
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"anatomy": {
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"correct": 83,
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},
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"astronomy": {
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"correct": 108,
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},
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"business_ethics": {
|
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"correct": 68,
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"acc": 68.0
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},
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"clinical_knowledge": {
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"correct": 183,
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|
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},
|
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"college_biology": {
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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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|
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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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|
||||
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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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|
||||
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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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|
||||
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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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||||
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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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|
||||
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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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|
||||
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|
||||
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|
||||
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|
||||
"machine_learning": {
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"management": {
|
||||
"correct": 81,
|
||||
"total": 103,
|
||||
"acc": 78.64077669902913
|
||||
},
|
||||
"marketing": {
|
||||
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|
||||
"total": 234,
|
||||
"acc": 86.75213675213675
|
||||
},
|
||||
"medical_genetics": {
|
||||
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|
||||
"total": 100,
|
||||
"acc": 75.0
|
||||
},
|
||||
"miscellaneous": {
|
||||
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|
||||
"total": 783,
|
||||
"acc": 78.67177522349937
|
||||
},
|
||||
"moral_disputes": {
|
||||
"correct": 223,
|
||||
"total": 346,
|
||||
"acc": 64.45086705202313
|
||||
},
|
||||
"moral_scenarios": {
|
||||
"correct": 217,
|
||||
"total": 895,
|
||||
"acc": 24.24581005586592
|
||||
},
|
||||
"nutrition": {
|
||||
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|
||||
"total": 306,
|
||||
"acc": 65.359477124183
|
||||
},
|
||||
"philosophy": {
|
||||
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|
||||
"total": 311,
|
||||
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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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|
||||
"professional_accounting": {
|
||||
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|
||||
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|
||||
"acc": 39.361702127659576
|
||||
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|
||||
"professional_law": {
|
||||
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|
||||
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|
||||
"acc": 38.983050847457626
|
||||
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|
||||
"professional_medicine": {
|
||||
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|
||||
"total": 272,
|
||||
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|
||||
},
|
||||
"professional_psychology": {
|
||||
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|
||||
"total": 612,
|
||||
"acc": 63.39869281045752
|
||||
},
|
||||
"public_relations": {
|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"security_studies": {
|
||||
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|
||||
"total": 245,
|
||||
"acc": 66.12244897959184
|
||||
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|
||||
"sociology": {
|
||||
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|
||||
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|
||||
"acc": 83.58208955223881
|
||||
},
|
||||
"us_foreign_policy": {
|
||||
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|
||||
"total": 100,
|
||||
"acc": 76.0
|
||||
},
|
||||
"virology": {
|
||||
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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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||||
}
|
||||
}
|
||||
}
|
||||
5
benchmarks/truthfulqa.json
Normal file
5
benchmarks/truthfulqa.json
Normal file
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"truthfulqa_avg": 45.04283965728274,
|
||||
"correct": 368,
|
||||
"total": 817
|
||||
}
|
||||
8
benchmarks/winogrande.json
Normal file
8
benchmarks/winogrande.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"benchmark": "winogrande",
|
||||
"model": "omni-nexus-alpha-q8",
|
||||
"method": "chat-api (fill-blank, option word count)",
|
||||
"accuracy": 0.5201262825572218,
|
||||
"correct": 659,
|
||||
"total": 1267
|
||||
}
|
||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\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>" }}
|
||||
{%- 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' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) 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' }}
|
||||
{%- endif %}
|
||||
61
config.json
Normal file
61
config.json
Normal file
@@ -0,0 +1,61 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
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"bos_token_id": 151643,
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"dtype": "float16",
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||||
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"full_attention",
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"full_attention",
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"full_attention",
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||||
"full_attention",
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||||
"full_attention",
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"full_attention",
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"full_attention",
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||||
"full_attention",
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||||
"full_attention",
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||||
"full_attention",
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||||
"full_attention",
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||||
"full_attention",
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"full_attention",
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||||
"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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||||
"full_attention",
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||||
"full_attention",
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"full_attention",
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||||
"full_attention",
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||||
"full_attention",
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"vocab_size": 152064
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}
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||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
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||||
"bos_token_id": 151643,
|
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"eos_token_id": 151643,
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3
model-00001-of-00004.safetensors
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3
model-00001-of-00004.safetensors
Normal file
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3
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3
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version https://git-lfs.github.com/spec/v1
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347
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Normal file
347
model.safetensors.index.json
Normal file
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{
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3
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3
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29
tokenizer_config.json
Normal file
29
tokenizer_config.json
Normal file
@@ -0,0 +1,29 @@
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||||
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}
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Reference in New Issue
Block a user