Instructions to use Thox-ai/ThoxEdge-RustCoder-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Thox-ai/ThoxEdge-RustCoder-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="Thox-ai/ThoxEdge-RustCoder-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Thox-ai/ThoxEdge-RustCoder-0.5B")
model = AutoModelForCausalLM.from_pretrained("Thox-ai/ThoxEdge-RustCoder-0.5B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Thox-ai/ThoxEdge-RustCoder-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Thox-ai/ThoxEdge-RustCoder-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Thox-ai/ThoxEdge-RustCoder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Thox-ai/ThoxEdge-RustCoder-0.5B

SGLang

How to use Thox-ai/ThoxEdge-RustCoder-0.5B 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 "Thox-ai/ThoxEdge-RustCoder-0.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Thox-ai/ThoxEdge-RustCoder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Thox-ai/ThoxEdge-RustCoder-0.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Thox-ai/ThoxEdge-RustCoder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Thox-ai/ThoxEdge-RustCoder-0.5B with Docker Model Runner:
```
docker model run hf.co/Thox-ai/ThoxEdge-RustCoder-0.5B
```

tommytracx commited on 5 days ago

Commit

e084abb

verified ·

1 Parent(s): 21eda30

docs(card): THOX model card standard + ThoxOS/MeshStack usage (Wren pass 2026-10-23)

Browse files

Files changed (1) hide show

README.md +53 -7

README.md CHANGED Viewed

@@ -3,15 +3,19 @@ base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
 license: apache-2.0
 pipeline_tag: text-generation
 datasets:
-  - Fortytwo-Network/Strandset-Rust-v1
 language:
-  - en
 tags:
-  - thox
-  - rust
-  - code
-  - trl
-  - sft
 ---
 # ThoxEdge-RustCoder-0.5B
@@ -109,3 +113,45 @@ retains the sensitivity.
 Training data: [`Fortytwo-Network/Strandset-Rust-v1`](https://huggingface.co/datasets/Fortytwo-Network/Strandset-Rust-v1),
 Apache-2.0 — synthetic Rust generated from idioms mined across 2,300+ crates.io crates,
 consensus-filtered and 94.3% rustc-verified.

 license: apache-2.0
 pipeline_tag: text-generation
 datasets:
+- Fortytwo-Network/Strandset-Rust-v1
 language:
+- en
 tags:
+- thox
+- rust
+- code
+- trl
+- sft
+- thoxos
+- meshstack
+base_model_relation: finetune
+library_name: transformers
 ---
 # ThoxEdge-RustCoder-0.5B
 Training data: [`Fortytwo-Network/Strandset-Rust-v1`](https://huggingface.co/datasets/Fortytwo-Network/Strandset-Rust-v1),
 Apache-2.0 — synthetic Rust generated from idioms mined across 2,300+ crates.io crates,
 consensus-filtered and 94.3% rustc-verified.
+<!-- THOX-STANDARD:BEGIN -->
+## Files
+| File | Size | SHA-256 |
+|---|---:|---|
+| `model.safetensors` | 988.10 MB (988,097,824 B) | `bbb25d81022f1243f48cdd143c83f249ef2ca18ec8eef301803786402c5fec47` |
+## Use with ThoxOS
+This repo ships safetensors only (no GGUF). Run it with 🤗 Transformers on a ThoxOS host (see the usage section above), or convert it with llama.cpp `convert_hf_to_gguf.py` if the architecture is supported.
+```bash
+hf download Thox-ai/ThoxEdge-RustCoder-0.5B --local-dir ./ThoxEdge-RustCoder-0.5B
+```
+## Use with MeshStack
+Serve it behind an OpenAI-compatible server (for example a Transformers/vLLM server on port 8080) and register `http://<node-ip>:8080/v1` with your MeshStack coordinator.
+## Device fit (by file size only)
+- `model.safetensors` (988.10 MB): Medium (<2.5 GB): devices with ≥4 GB RAM — recent phones, laptops, Apple Silicon Macs, MeshStack edge hosts.
+RAM needed ≈ file size + context (KV cache) + runtime overhead. These tiers are derived from file size alone; no throughput, TOPS or battery figures are claimed here.
+## Licence and attribution
+Upstream `Qwen/Qwen2.5-Coder-0.5B-Instruct` is Apache-2.0 (© Alibaba Cloud / Qwen team; verified against upstream Hub metadata 2026-10-23). The Apache-2.0 licence and NOTICE carry forward.
+## Out of scope
+- Medical, legal, financial, safety-critical or other high-stakes decisions without human review.
+- Unattended control of physical devices or actuators without a confirmation step.
+- Any use prohibited by the upstream licence or acceptable-use policy.
+---
+THOX.ai LLC — **Your AI. Your Data. Your Rules.™** · [thox.ai](https://thox.ai) · Contact: [tommy@thox.ai](mailto:tommy@thox.ai)
+<!-- THOX-STANDARD:END -->