Instructions to use ordlibrary/deepsol-clawd-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ordlibrary/deepsol-clawd-code with Transformers:

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

pipe = pipeline("text-generation", model="ordlibrary/deepsol-clawd-code")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ordlibrary/deepsol-clawd-code")
model = AutoModelForCausalLM.from_pretrained("ordlibrary/deepsol-clawd-code", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ordlibrary/deepsol-clawd-code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ordlibrary/deepsol-clawd-code

SGLang

How to use ordlibrary/deepsol-clawd-code 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 "ordlibrary/deepsol-clawd-code" \
    --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": "ordlibrary/deepsol-clawd-code",
		"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 "ordlibrary/deepsol-clawd-code" \
        --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": "ordlibrary/deepsol-clawd-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ordlibrary/deepsol-clawd-code with Docker Model Runner:
```
docker model run hf.co/ordlibrary/deepsol-clawd-code
```

deepsol-clawd-code / README.md

ordlibrary

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	---
	license: apache-2.0
	library_name: transformers
	tags:
	- gpt2
	- solana
	- clawd
	- code
	- text-generation
	language:
	- en
	pipeline_tag: text-generation
	---

	# deepsol-clawd-code

	Merged GPT-2 checkpoint from the Solana Clawd AI training stack (`deepsol-clawd-code-merged`).

	## Model details

	\| \| \|
	\|---\|---\|
	\| Architecture \| `GPT2LMHeadModel` \|
	\| Layers \| 12 \|
	\| Hidden size \| 768 \|
	\| Heads \| 12 \|
	\| Context \| 1024 \|
	\| Vocab \| 50257 (GPT-2 tokenizer) \|
	\| Weights dtype \| float16 (`model.safetensors`) \|
	\| Size \| ~237 MB \|

	## Files

	- `model.safetensors` — merged weights
	- `config.json` / `generation_config.json`
	- `tokenizer.json` / `tokenizer_config.json`

	## Quick start

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo = "ordlibrary/deepsol-clawd-code"
	tok = AutoTokenizer.from_pretrained(repo)
	model = AutoModelForCausalLM.from_pretrained(repo)

	prompt = "def transfer_sol("
	inputs = tok(prompt, return_tensors="pt")
	out = model.generate(**inputs, max_new_tokens=64)
	print(tok.decode(out[0], skip_special_tokens=True))
	```

	## Intended use

	Research / experimentation around Solana-oriented code and tooling assistants in the Clawd training pipeline. This is a small GPT-2-scale model, not a production 7B+ coder.

	## Limitations

	- Small capacity vs modern LLMs; expect weak long-context and complex reasoning.
	- Training data and merge recipe are project-internal; evaluate before any production use.
	- Do not rely on it for financial advice or unsigned transaction construction without human review.

	## Citation

	```bibtex
	@misc{deepsol-clawd-code,
	title = {deepsol-clawd-code},
	author = {ordlibrary},
	year = {2026},
	howpublished = {\url{https://huggingface.co/ordlibrary/deepsol-clawd-code}}
	}
	```