# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/echo", trust_remote_code=True, device_map="auto")echo
echo is a custom TAMELM language-model checkpoint. The repository includes an implementation of routed attention, depth attention, and dropless mixture-of-experts (MoE), together with weights, configuration, and tokenizer assets.
This is a research concept model exploring a particular architectural configuration. Related TAMELM concept models vary in size or setup; each should be evaluated individually.
This card documents the published repository artifacts. Architecture names and configuration fields describe the implementation, not demonstrated reasoning ability or benchmark performance.
Architecture and configuration
Values below are recorded in config.json. A configured sequence limit is not evidence of tested long-context quality; a configured dtype is not a report of training precision.
| Setting | Published value |
|---|---|
| Architecture | TAMELM |
| Model type | tamelm_two_axis |
| Layers | 12 |
| Embedding dimension | 1024 |
| Query heads | 16 |
| Key/value heads | 4 |
| Experts | 4 |
| Expert top-k | 2 |
| Expert hidden dimension | 4096 |
| Depth mode | attention |
| Depth dimension | 768 |
| Depth heads | 8 |
| Depth slots | 16 |
| Attention backend | flex |
| Expert backend | grouped |
| Maximum sequence length | 1024 |
| Vocabulary size | 49152 |
| Configured dtype | float32 |
| Recorded Transformers version | 5.5.0 |
| Use cache | false |
This export selects the flex attention backend. The separate Echo_R export selects dense; this configuration difference does not establish a training lineage or equivalence between their weights.
Repository contents and loading
The repository provides checkpoint weights, tokenizer assets, config.json, and modeling_tamelm.py. Its auto_map maps AutoConfig to modeling_tamelm.TAMEConfig and AutoModelForCausalLM to modeling_tamelm.TAMELM.
This is custom model code, not a built-in Transformers architecture. Review the implementation before enabling remote-code execution, and pin a repository revision for reproducibility. The recorded Transformers version is export metadata, not a fully tested dependency lockfile.
The implementation includes dense/reference alternatives and optional CUDA FlexAttention/Triton grouped-GEMM acceleration. Backend compatibility depends on the runtime, hardware, and installed packages. An end-to-end loading and generation recipe has not been validated for this card; no hardware-independent inference guarantee is made.
Training and evaluation
The inspected repository does not document a complete training recipe or reproducible evaluation report. Training datasets and their licenses, data preprocessing, training duration/token count, optimizer settings, hardware, and checkpoint-selection criteria are not specified here. No benchmark scores, comparative improvements, or production-readiness claims are asserted.
Uses and limitations
This artifact can support research into the published architecture and checkpoint once its runtime requirements are satisfied. Before using generated output in an application, evaluate accuracy, bias, memorization, and safety for that application. Language-model output may be incorrect, biased, or unsafe. Do not rely on it without independent verification for consequential decisions.
License
No repository-wide model license is declared in the existing card metadata. This card does not assign a new license or infer one from a backbone, framework, or dependency. Confirm applicable terms with the repository owner before redistribution or deployment.
Source artifacts
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reaperdoesntknow/echo", trust_remote_code=True)