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")

# pip install -U transformers accelerate
# 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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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

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

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Safetensors

Model size

0.1B params

Tensor type

F16

Model tree for ordlibrary/deepsol-clawd-code

Quantizations

1 model

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deepsol-clawd-code