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hotdogs/
Qwen35-Agent-R2A103

Name: Qwen35-Agent-R2A103
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🚀 Qwen35-Agent-R2A103 — R2 + Agents-A1 Model Soup (0.7 : 0.3)

Building on hotdogs/Qwen35B-Agent-R2 as the base, blended with insights from InternScience/Agents-A1 via model soup (0.7 : 0.3).

🧬 How This Model Was Built

┌──────────────────────────────────────────────────────────────────┐
│               Qwen35-Agent-R2A103 Construction                   │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌─────────────────────┐      ┌──────────────────────────┐       │
│  │  Qwen35B-Agent-R2   │      │   InternScience/Agents-A1│       │
│  │  (7 LoRAs fused)    │      │   (Multi-teacher distilled)│      │
│  │  - Opus | Fable     │      │   - Tool Use | Reasoning  │       │
│  │  - Tool | Routing   │      │   - Search | Engineering  │       │
│  │  - Math | Mythos    │      │   - Scientific | Instruct │       │
│  │  - ToolFmt          │      │   - Full-domain SFT       │       │
│  └─────────┬───────────┘      └────────────┬──────────────┘       │
│            │                                │                      │
│            └────────── Model Soup ──────────┘                      │
│                        │  0.7 : 0.3                               │
│                        ▼                                          │
│            ┌──────────────────────┐                               │
│            │  Qwen35-Agent-R2A103 │                               │
│            │  31,666 tensors      │                               │
│            │  70.2 GB             │                               │
│            └──────────────────────┘                               │
│                        │                                          │
│                        ▼                                          │
│            ┌──────────────────────┐                               │
│            │  GGUF Quantization   │                               │
│            ├──────────────────────┤                               │
│            │ f16     → 65 GB      │                               │
│            │ Q4_K_M  → 20 GB      │                               │
│            │ Q6_K    → 27 GB      │                               │
│            └──────────────────────┘                               │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Model Soup (Weight Averaging)

Building on hotdogs/Qwen35B-Agent-R2 as the base, we blend corresponding weight tensors with InternScience/Agents-A1:

W_R2A103 = 0.7 × W_R2 + 0.3 × W_Agents-A1

This preserves the agentic reasoning and tool-use capabilities of R2 while incorporating the broader multi-domain agent skills (long-horizon search, engineering, scientific research) from Agents-A1.

Architecture Compatibility

Both models share the same qwen3_5_moe architecture:

Property	Value
Architecture	Qwen3.5 MoE
Hidden size	2048
Layers	40
Attention heads	16
KV heads	2
Experts	256 (8 active per token)
Shared experts	1
Vocab size	248,320
Context length	32,768

📦 Files

File	Size	Format
Safetensors (14 shards)	70 GB	Transformers
`GGUF/Qwen35-Agent-R2A103.f16.gguf`	65 GB	GGUF f16
`GGUF/Qwen35-Agent-R2A103.Q4_K_M.gguf`	20 GB	GGUF Q4_K_M
`GGUF/Qwen35-Agent-R2A103.Q6_K.gguf`	27 GB	GGUF Q6_K

🚀 Usage

With Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hotdogs/Qwen35-Agent-R2A103",
    device_map="auto",
    trust_remote_code=True,
    torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained("hotdogs/Qwen35-Agent-R2A103")

messages = [{"role": "user", "content": "What is the capital of Thailand?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.6)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

With llama.cpp (GGUF)

bash

# Q4_K_M (recommended - best size/speed/quality balance)
llama-cli \
  -m GGUF/Qwen35-Agent-R2A103.Q4_K_M.gguf \
  -n 256 -p "What is the capital of Thailand?" --temp 0.6 -ngl 99

# Or run as server:
llama-server \
  -m GGUF/Qwen35-Agent-R2A103.Q4_K_M.gguf \
  --port 8080 --host 0.0.0.0 -ngl 99 -c 4096

With Ollama

bash

ollama create qwen35-r2a103 -f Modelfile
ollama run qwen35-r2a103

Modelfile:

dockerfile

FROM ./GGUF/Qwen35-Agent-R2A103.Q4_K_M.gguf

PARAMETER temperature 0.6
PARAMETER top_k 40
PARAMETER top_p 0.9
PARAMETER min_p 0.05
PARAMETER repeat_penalty 1.03

TEMPLATE "{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"

🧠 Capabilities

This model inherits skills from both parents:

Skill	From R2	From Agents-A1
✅ Tool calling	✓	✓
✅ Multi-step reasoning	✓	✓
✅ Instruction following	✓	✓
✅ Code generation	✓	✓
✅ Thai language	✓	✓
✅ Long-horizon search	-	✓
✅ Engineering tasks	-	✓
✅ Scientific reasoning	-	✓
✅ Vision (multimodal)	-	(via separate mmproj)

📊 Performance

Format	Size	BPW	Notes
f16	65 GB	16.0	Full precision reference
Q6_K	27 GB	6.58	High quality, fast
Q4_K_M	20 GB	4.88	Recommended
Q4_K_M inference	20 GB	—	~110 t/s on 7×RTX 3090

Benchmarked on 7× NVIDIA RTX 3090 with llama.cpp:

Prompt processing: 41.7 t/s (11 tokens)
Token generation: 92.1–110 t/s

🔗 References

R2 Base: hotdogs/Qwen35B-Agent-R2
Agents-A1: InternScience/Agents-A1
Qwen3.5 MoE: Qwen/Qwen-AgentWorld-35B-A3B
GGUF: llama.cpp

🙏 Credits

hotdogs — Qwen35B-Agent-R2 as the base model
InternScience / Agents-A1 — Multi-domain agent capabilities (tool-use, search, engineering, scientific reasoning, instruction following). Check out their paper
Qwen Team (Alibaba) — Qwen3.5 MoE architecture
llama.cpp — GGUF conversion and inference framework

📄 License

Apache 2.0