Fable Traces
Fable Traces by AliesTaha, a text-generation model. Understand and compare features, benchmarks, and capabilities.
Comparison
| Feature | Fable Traces | Interfaze |
|---|---|---|
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 32.8K | 1M |
| Tool Calling | No | Tool calling supported + built in browser, code execution and web search |
Scaling
| Feature | Fable Traces | Interfaze |
|---|---|---|
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |
View model card on Hugging Face
A compact instruction-tuned language model built on
Qwen/Qwen3-4B-Instruct-2507.
fable-traces is tuned for short, conversational replies and runs comfortably on a
single mid-range GPU.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "AliesTaha/fable-traces"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto")
messages = [{"role": "user", "content": "Tell me something interesting."}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=100, do_sample=False)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))Serve with vLLM:
vllm serve AliesTaha/fable-tracesDetails
| Base model | Qwen3-4B-Instruct-2507 |
| Parameters | ~4B |
| Precision | bfloat16 (safetensors) |
| Prompt format | ChatML — use the tokenizer's chat template |
| Context length | inherits the base model |
License
Apache 2.0, following the base model.
This is a joke. This is not an actual model. Please read the full post first