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richardyoung
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| Parameter | Value |
|---|---|
| direction_index | 18.72 |
| attn.o_proj.max_weight | 1.21 |
| attn.o_proj.max_weight_position | 27.88 |
| attn.o_proj.min_weight | 1.02 |
| attn.o_proj.min_weight_distance | 20.65 |
| mlp.down_proj.max_weight | 1.02 |
| mlp.down_proj.max_weight_position | 30.52 |
| mlp.down_proj.min_weight | 0.74 |
| mlp.down_proj.min_weight_distance | 19.34 |
| Metric | This model | Original model (King3Djbl/mythos-9b-merged) |
|---|---|---|
| KL divergence | 0.0211 | 0 (by definition) |
| Refusals | 7/100 | 79/100 |
Uncensored fine-tuned agent model — Built from Qwen3-9B with 47,824 agent traces for tool use, shell commands, and reasoning. Features native thinking mode for complex multi-step tasks.
bash
ollama pull fableforge/mythos-9bollama run fableforge/mythos-9bbash
./llama-cli -m mythos-9b-Q4_K_M.gguf -ngl 99python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("King3Djbl/mythos-9b-merged", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("King3Djbl/mythos-9b-merged")| Category | Score | Notes |
|---|---|---|
| Censorship Resistance | 4.5/5 | 9/10 categories: full answers |
| Tool-Use | 4.8/5 | Shell, code, Docker, K8s, SQL |
| Reasoning | 4.5/5 | Logic, debugging, system design |
| Speed (thinking) | 10.7 tok/s | With thinking tokens |
| Speed (no-think) | ~15-20 tok/s | Estimated |
| Model | Size | Censorship | Best For |
|---|---|---|---|
| ShellWhisperer-1.5B | 1.5B | 3.5/5 | Shell/terminal, edge devices |
| Mythos-9B | 9B | 4.5/5 | General agent, tool calls, reasoning |
| Mythos-9B-Enhanced | 9B | 4.8/5 | Agent + security research |
| Mythos-9B-Unhinged | 9B | 5/5 | Fully uncensored |
Fine-tuned on FableForge Mix A dataset (47,824 examples) — agent traces, shell commands, code generation, and multi-step reasoning. 98.3% of the 2.8M formatted examples remain untapped for future training iterations.
License: Apache 2.0
Built & maintained by Richard Young · DeepNeuro
ollama pull fableforge/mythos-9bollama run fableforge/mythos-9bfrom transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("King3Djbl/mythos-9b-merged", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("King3Djbl/mythos-9b-merged")