Instructions to use reaperdoesntknow/Echo_R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reaperdoesntknow/Echo_R with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="reaperdoesntknow/Echo_R", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/Echo_R", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use reaperdoesntknow/Echo_R with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/Echo_R"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/Echo_R",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/Echo_R

SGLang

How to use reaperdoesntknow/Echo_R with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "reaperdoesntknow/Echo_R" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/Echo_R",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "reaperdoesntknow/Echo_R" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/Echo_R",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use reaperdoesntknow/Echo_R with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/Echo_R
```

reaperdoesntknow commited on 2 days ago

Commit

028dee3

verified ·

1 Parent(s): acead07

Document Echo_R architecture and runtime requirements

Browse files

Files changed (1) hide show

README.md +43 -176

README.md CHANGED Viewed

@@ -3,198 +3,65 @@ library_name: transformers
 tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-### Training Data
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-<!-- cix-keeper-ts:2026-11-12T14:15:40Z -->

 tags: []
 ---
+# Echo_R
+**Echo_R** 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 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`](./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 | `dense` |
+| 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 `dense` attention backend. The separate `echo` export selects `flex`; 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`](./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
+- [Repository files](https://huggingface.co/reaperdoesntknow/Echo_R/tree/main)
+- [Published configuration](https://huggingface.co/reaperdoesntknow/Echo_R/blob/main/config.json)
+- [Custom model implementation](https://huggingface.co/reaperdoesntknow/Echo_R/blob/main/modeling_tamelm.py)
+<!-- cix-keeper-ts:2026-11-12T14:15:40Z -->