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charlieduzstuf
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Model Details
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charlieduzstuf
Model Tree
DarkKitsune/Qwen3.5-9B-Qworus-V2
empero-ai/Qwable-9B-Claude-Fable-5
Qwen/Qwen3.5-9B
Netuoso/qwepus
Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Base architecture | Qwen3.5-9B (dense, hybrid Gated DeltaNet + full attention) |
| Parameter count | ~9B |
| Merge method | Linear (Mergekit) |
| Primary lineage | Qwen3.5-9B → Qwable-style reasoning + complementary merges |
| Languages | Primarily English |
| License | MIT (this merge) — parent models are mostly Apache-2.0; see provenance notes |
empero-ai/Qwable-9B-Claude-Fable-5
Full-parameter SFT of Qwen3.5-9B on Claude Fable 5 reasoning/coding traces + a small GPT-5.5 terminal/agent set. Strong structured <think> reasoning and agentic coding style. Vision tower was frozen (text-only fine-tune).
DarkKitsune/Qwen3.5-9B-Qworus-V2
50/50 DARE-TIES merge of empero-ai/Qwen3.8-9B-Distill and ornith-ai/Ornith-1.5-9B. Hybrid reasoning model aimed at coding, tool calls, planning, and general question answering. Not uncensored by default.
Netuoso/qwepus
Additional Qwen-based component included in the linear merge.
All components ultimately descend from the Qwen3.5-9B family (natively multimodal architecture with long context support).
Not intended for production deployment, high-stakes decision making, or use cases requiring strong safety guarantees without additional filtering.
This is a standard Transformers-compatible checkpoint. Example (text-only):
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "YOUR_USERNAME/Qwable-EX" # replace with actual repo tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",) messages = [ {"role": "user", "content": "Write a clean Python function that merges two sorted lists."}] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,)inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=2048, do_sample=True, temperature=0.7, top_p=0.95, repetition_penalty=1.05,) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "YOUR_USERNAME/Qwable-EX" # replace with actual repo tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",) messages = [ {"role": "user", "content": "Write a clean Python function that merges two sorted lists."}] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,)inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=2048, do_sample=True, temperature=0.7, top_p=0.95, repetition_penalty=1.05,) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))