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hotdogs/Qwen35b-agent-R2O3
Qwen35b-agent-R2O3 is a text generation model from hotdogs. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as agpl-3.0.
<p align="center" <img src="https://img.shields.io/badge/license-AGPL--3.0-red" <img src="https://img.shields.io/badge/Qwen3.5-35B%20A3B-blue" <img src="https://img.shields.io/badge/MoE-256%20experts-orange" <img src=…
Downloads · 30 days
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.gguf241 GB · 78%
From the Hugging Face model README
┌─────────────────────────────────────────────────────────────┐
│ Qwen35b-Agent-R2O3 Construction │
├─────────────────────────────────────────────────────────────┤
│ │
│ Qwen35B-Agent-R2 (Base) ────────── 70% weights kept │
│ │ │
│ ├── 7 LoRAs already fused: │
│ │ Opus | Fable | Routing | Tool | Math | Mythos │
│ │ | ToolFmt (all trained via SFT) │
│ │ │
│ └── + Ornith LoRA (α=0.3) ← SVD Weight-Diff │
│ │
│ Ornith-1.0-35B Qwen-AgentWorld │
│ │ │ │
│ └──────── Weight-Diff SVD ──────────────┘ │
│ │ │
│ ┌──────┴──────┐ │
│ │ LoRA r=32 │ → Merged at α=0.3 │
│ │ 422 tensors│ │
│ └─────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
We extract Ornith's unique knowledge by computing the weight difference between Ornith-1.0-35B and the shared Huihui-Qwen-AgentWorld base:
delta = W_ornith - W_base # What Ornith learned
U, S, Vh = torch.linalg.svd(delta) # Decompose
lora_A = diag(S[:32].sqrt()) @ Vh[:32, :]
lora_B = U[:, :32] @ diag(S[:32].sqrt())
422 tensors extracted across:
gate_proj, up_proj, down_proj) — knowledge executionlinear_attn vs Ornith's self_attn (incompatible architecture)The extracted Ornith LoRA (r=32, α=64) is merged into Qwen35B-Agent-R2 at scale α=0.3:
merged = R2 * 0.7 + Ornith_LoRA * 0.3
This preserves 70% of R2's original capabilities (its 7 LoRAs) while adding 30% of Ornith's algorithm/reasoning strength.
| Scale | R2 Preserved | Ornith Added | Best For |
|---|---|---|---|
| 0.3 | 70% | 30% | Balanced — general agent use |
| 0.4 | 60% | 40% | Algorithm-heavy tasks |
| 0.5+ | <50% | >50% | ⚠️ May dilute tool-calling |
MoE models (256 experts) require special handling for SVD extraction:
| Component | Standard Approach | MoE Adaptation |
|---|---|---|
| 2D tensors (MLP, norms) | SVD(delta) — normal | Same |
3D expert tensors [out, in, 256] | N/A | flatten → SVD → reshape |
| Attention mismatch | Direct diff | ❌ Skipped (R2 uses linear_attn) |
| language_model prefix | Exact match | Strip prefix after loading |
Expert tensor handling:
delta = W_a - W_b # [512, 2048, 256]
delta_flat = delta.transpose(0,2).reshape(-1, delta.shape[1]) # [131072, 2048]
U, S, Vh = torch.linalg.svd(delta_flat)
lora_B = U[:, :32] @ diag(S[:32].sqrt()) # [131072, 32]
# On merge: reconstruct
delta = lora_B @ lora_A # [131072, 2048]
delta = delta.reshape(512, 256, 2048).permute(0, 2, 1) # [512, 2048, 256]
| Capability | Source | Retained |
|---|---|---|
| 🧠 Reasoning (Opus 4.8) | R2 | ✅ 100% |
| 🔧 Tool Calling | R2 | ✅ 100% |
| 🧭 Agent Routing | R2 | ✅ 100% |
| 📐 Math | R2 + Ornith | ✅ Enhanced |
| ⚡ Algorithm | Ornith 🆕 | ✅ +30% |
| 💬 Conversation (Fable) | R2 | ✅ 100% |
| 🎭 Creative (Mythos) | R2 | ✅ 100% |
| Aspect | Other Models | Agent-R2O3 |
|---|---|---|
| Tool Call Format | ❌ Often malformed | ✅ Guaranteed valid <tool_call> |
| Algorithm Tasks | ❌ Struggles on hard | ✅ Orithm-enhanced |
| Thai Support | ❌ Poor tokenization | ✅ Native Thai + English |
| Knowledge | ❌ Single source | ✅ R2 (7 LoRAs) + Ornith |
# llama.cpp
./llama-cli -m Qwen35b-agent-R2O3.Q4_K_M.gguf \
-p "Hello" -n 100 --temp 0.6
# Full server with tool calling
./llama-server \
-m Qwen35b-agent-R2O3.Q4_K_M.gguf \
--host 0.0.0.0 --port 8081 -c 262144 -ngl 99 \
--cache-type-k bf16 --cache-type-v bf16 \
--flash-attn on --tools all --cont-batching \
--temp 0.6 --top-k 40 --top-p 0.9 \
--min-p 0.05 --repeat-penalty 1.03 \
--jinja
| File | Size | Quant |
|---|---|---|
Qwen35b-agent-R2O3.Q4_K_M.gguf | 20 GB | Recommended |
Qwen35b-agent-R2O3.Q6_K.gguf | 27 GB | High quality |
Qwen35b-agent-R2O3.f16.gguf | 65 GB | Full precision |
| Contribution | Source |
|---|---|
| Base Agent Model | hotdogs/Qwen35B-Agent-R2 |
| Algorithm Knowledge | deepreinforce-ai/Ornith-1.0-35B |
| SVD Extraction Method | Weight-Diff SVD (Universial Adapter Extraction) |
| Infrastructure | Nous Research — Hermes Agent |
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