From 3ee877e26dbfda81a11e059f7e8bbd89bb5bdf8f Mon Sep 17 00:00:00 2001 From: grey <0xgr3y@users.noreply.huggingface.co> Date: Sat, 15 Nov 2025 20:42:04 +0000 Subject: [PATCH] Training w/ 13,55% --- README.md | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 4b269af..612bc0a 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ base_model:

Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm Agent-ID (tall_tame_panther)

-

Gensyn RL-Swarm: Training & GGUF Inference for Quantized LLMs

+

Gensyn RL-Swarm: Training & GGUF Quantized LLMs for Inference

Model @@ -41,16 +41,22 @@ base_model: License

+
+ +[![Gensyn](https://img.shields.io/badge/Powered%20by-Gensyn%20AI-pink?style=for-the-badge)](https://gensyn.ai) + +
+ --- ## Model Overview -Our pick an experimental (advanced) mode at this model a continuously trained **Qwen2.5-Coder-0.5B-Instruct** fine-tuned using **Gensyn RL-Swarm** framework with **GRPO (Group Relative Policy Optimization)** and supported format **GGUF (llama.cpp)** for enhanced code generation capabilities. **Note: Current training focuses on programming challenges with adaptive weighted sampling**. +Our pick an **experimental (advanced) mode** at this model a continuously trained `Qwen2.5-Coder-0.5B-Instruct` fine-tuned using **Gensyn RL-Swarm** framework with **GRPO (Group Relative Policy Optimization)** and supported format **GGUF (llama.cpp)** for enhanced code generation capabilities. **Note: Current training focuses on programming challenges with adaptive weighted sampling**. - **Agent ID:** `tall_tame_panther` - **Training Status:** 🟢 LIVE - Model updates automatically every 5-10 minutes - **Auto-Sync GGUF Pipeline Status:** 🟢 LIVE - Commits update automatically every hour -- **Current Progress:** Round 13,054+ / 100,000 (13.05%) +- **Current Progress:** Round 13,533+ / 100,000 (13.53%) - **Framework Version:** Gensyn RL-Swarm v0.7.0 - **Contract:** SwarmCoordinator v0.4.2 @@ -59,7 +65,7 @@ Our pick an experimental (advanced) mode at this model a continuously trained ** - **Real-time Training**: Continuous learning with distributed RL across Gensyn swarm network - **Adaptive System**: Dynamic quality enhanced and dataset weighting for optimal learning - **Multi-domain Coding**: Trained on MBPP and CodeContests datasets with adaptive sampling -- **GGUF Support**: Multiple quantized formats available (F16, Q3_K_M, Q4_K_M, Q5_K_M) +- **GGUF Support**: Multiple quantized formats available (F16, Q3_K_M, Q4_K_M, Q5_K_M, Q6_K) - **llama.cpp Compatible**: Ready for edge deployment and local inference - **BF16 Precision**: Trained with bfloat16 for optimal performance - **TGI Compatible**: Supports Text Generation Inference for production deployment @@ -219,17 +225,19 @@ ollama create qwen2.5-coder-swarm -f Modelfile ollama run qwen2.5-coder-swarm "Write a function to calculate the factorial of a number." ``` -## Available Quantization Formats +## Available GGUF Quantization | Format | Size | Precision | Use Case | Download | |--------|------|-----------|----------|----------| | Safetensors (BF16) | 988 MB | BF16 | Full precision training/fine-tuning | `model.safetensors` | | GGUF F16 | 994 MB | FP16 | High quality inference | `Qwen2.5-Coder-0.5B-F16.gguf` | +| GGUF Q6_K | 506 MB | 6-bit | High quality compression | `Qwen2.5-Coder-0.5B-Q6_K.gguf` | | GGUF Q5_K_M | 420 MB | 5-bit | Balanced quality/size | `Qwen2.5-Coder-0.5B-Q5_K_M.gguf` | | GGUF Q4_K_M | 398 MB | 4-bit | **Recommended** for production | `Qwen2.5-Coder-0.5B-Q4_K_M.gguf` | | GGUF Q3_K_M | 355 MB | 3-bit | Smallest, fastest | `Qwen2.5-Coder-0.5B-Q3_K_M.gguf` | -All GGUF formats are **llama.cpp compatible** and auto-updated hourly. +> All GGUF formats are **llama.cpp is compatible** ready to use **Inferences chat** and auto-update be hourly. + ## Chat Format & Conversational @@ -386,8 +394,8 @@ Check commit history for exact timestamps. | Metric | Value | Target | |--------|-------|--------| -| Completed Rounds | 13,054+ | 100,000 | -| Training Progress | 13.05% | 100% | +| Completed Rounds | 13,533+ | 100,000 | +| Training Progress | 13.53% | 100% | | Update Frequency | 5-10 min | Continuous | **Note**: **average\@k:** Average performance across `k` attempts, measuring consistency. **pass\@k:** Probability of at least one correct solution in `k` attempts, measuring capability.Current metrics track training rounds completed in decentralized swarm. @@ -463,7 +471,7 @@ git checkout
-**🤖 Trained with ❤️ using Gensyn RL-Swarm** +**Trained with 🩷 using Gensyn RL-Swarm** [![Gensyn](https://img.shields.io/badge/Powered%20by-Gensyn%20AI-pink?style=for-the-badge)](https://gensyn.ai)