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
```

ordlibrary commited on 19 days ago

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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}}
+}
+```