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

4cc735b

verified ·

1 Parent(s): 13c4502

Conform model card to THOX Model Card Standard v1.0 (tags, header, files, sections, hygiene)

Browse files

Files changed (1) hide show

README.md +35 -4

README.md CHANGED Viewed

@@ -1,9 +1,12 @@
 ---
-base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
 license: apache-2.0
 pipeline_tag: text-generation
 language:
 - en
 tags:
 - thox
 - rust
@@ -11,16 +14,36 @@ tags:
 - sft
 - thoxos
 - meshstack
-base_model_relation: finetune
-library_name: transformers
 ---
 # ThoxEdge-RustCoder-0.5B — Rust Completion Model
-![License](https://img.shields.io/badge/License-Apache_2.0-05A451) ![Params](https://img.shields.io/badge/Params-0.5B-05A451)
 ThoxEdge-RustCoder-0.5B is a Rust-leaning fine-tune trained by THOX.ai on a public Apache-2.0 Rust code corpus (74,606 filtered code-producing examples): a LoRA fine-tune (rank 32), merged into the base weights. It serves cheap Rust completion in ThoxRoute's lowest tier. It is a 0.5B model and is **not** a strong Rust coder; the numbers below are a same-harness delta measurement, nothing more. It is a hosted/desktop model: its GGUF ladder spans only about 260 MB because a large share of its parameters sit in an embedding that does not quantize down, so it does not fit THOX's smallest devices.
 ## Run it
 This repo ships safetensors only (no GGUF):
@@ -42,6 +65,10 @@ model = AutoModelForCausalLM.from_pretrained("Thox-ai/ThoxEdge-RustCoder-0.5B")
 - Cheap Rust code completion on hosts, inside the THOX stack.
 - Out of scope: medical, legal, financial, safety-critical, or other high-stakes decisions without human review; unattended control of physical devices without a confirmation step.
 ## Evaluation
 MultiPL-E HumanEval-Rust, 156 problems, executed (each generation is compiled and its assertions run), greedy decoding, both models scored in the same job:
@@ -60,6 +87,10 @@ The noise floor of this harness is about ±1 problem (±0.64 pp); both deltas cl
 - A training-renderer defect placed auxiliary output fields inside the code fence in about half the rows of three training categories; it caused no measured regression, and this checkpoint retains the prompt sensitivity described above.
 - Not sized for THOX's smallest devices (see summary).
 ## License
 Apache-2.0. The THOX fine-tune and additions in this repo are covered under Apache-2.0; the training corpus is also Apache-2.0. See the license text for terms.

 ---
 license: apache-2.0
 pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-Coder-0.5B-Instruct
 language:
 - en
+base_model_relation: finetune
+library_name: transformers
 tags:
 - thox
 - rust
 - sft
 - thoxos
 - meshstack
+- thoxedge
+- qwen2.5
+- fine-tune
+- local-first
 ---
 # ThoxEdge-RustCoder-0.5B — Rust Completion Model
+![License](https://img.shields.io/badge/License-Apache_2.0-05A451) ![Params](https://img.shields.io/badge/Params-0.5B-05A451) ![Base](https://img.shields.io/badge/Base-Qwen2.5--Coder--0.5B--Instruct-05A451) ![Access](https://img.shields.io/badge/Access-Public-05A451)
+> **Your AI. Your Data. Your Rules.™**
+## Summary
 ThoxEdge-RustCoder-0.5B is a Rust-leaning fine-tune trained by THOX.ai on a public Apache-2.0 Rust code corpus (74,606 filtered code-producing examples): a LoRA fine-tune (rank 32), merged into the base weights. It serves cheap Rust completion in ThoxRoute's lowest tier. It is a 0.5B model and is **not** a strong Rust coder; the numbers below are a same-harness delta measurement, nothing more. It is a hosted/desktop model: its GGUF ladder spans only about 260 MB because a large share of its parameters sit in an embedding that does not quantize down, so it does not fit THOX's smallest devices.
+## Files
+| File | Size | Purpose |
+|---|---:|---|
+| `model.safetensors` | 988,097,824 B | Model weights (safetensors) |
+| `tokenizer.json` | 11,421,892 B | Tokenizer files |
+| `chat_template.jinja` | 2,507 B | Chat template |
+| `.gitattributes` | 1,570 B | Repository file |
+| `config.json` | 1,284 B | Model configuration |
+| `tokenizer_config.json` | 693 B | Tokenizer files |
+| `generation_config.json` | 217 B | Generation configuration |
 ## Run it
 This repo ships safetensors only (no GGUF):
 - Cheap Rust code completion on hosts, inside the THOX stack.
 - Out of scope: medical, legal, financial, safety-critical, or other high-stakes decisions without human review; unattended control of physical devices without a confirmation step.
+## Training data and procedure
+THOX fine-tune of `Qwen/Qwen2.5-Coder-0.5B-Instruct`. The corpus and procedure are described in this card as published at release (see Summary and Evaluation); no training beyond what this card describes is claimed for this repository.
 ## Evaluation
 MultiPL-E HumanEval-Rust, 156 problems, executed (each generation is compiled and its assertions run), greedy decoding, both models scored in the same job:
 - A training-renderer defect placed auxiliary output fields inside the code fence in about half the rows of three training categories; it caused no measured regression, and this checkpoint retains the prompt sensitivity described above.
 - Not sized for THOX's smallest devices (see summary).
+## Provenance
+Published by THOX.ai as `Thox-ai/ThoxEdge-RustCoder-0.5B`. Upstream base: `Qwen/Qwen2.5-Coder-0.5B-Instruct`. This card was conformed to THOX Model Card Standard v1.0 on 2026-10-31 (frontmatter, tags, header, files, and section structure); the technical content is as published at release.
 ## License
 Apache-2.0. The THOX fine-tune and additions in this repo are covered under Apache-2.0; the training corpus is also Apache-2.0. See the license text for terms.