Instructions to use JamesX421/SOLID-opt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JamesX421/SOLID-opt with Transformers:

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

pipe = pipeline("text-generation", model="JamesX421/SOLID-opt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JamesX421/SOLID-opt")
model = AutoModelForCausalLM.from_pretrained("JamesX421/SOLID-opt", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JamesX421/SOLID-opt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JamesX421/SOLID-opt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JamesX421/SOLID-opt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JamesX421/SOLID-opt

SGLang

How to use JamesX421/SOLID-opt 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 "JamesX421/SOLID-opt" \
    --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": "JamesX421/SOLID-opt",
		"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 "JamesX421/SOLID-opt" \
        --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": "JamesX421/SOLID-opt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JamesX421/SOLID-opt with Docker Model Runner:
```
docker model run hf.co/JamesX421/SOLID-opt
```

SOLID-opt / README.md

JamesX421

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metadata

base_model: Qwen/Qwen3-4B-Instruct-2507
library_name: transformers
pipeline_tag: text-generation
tags:
  - qwen3
  - operations-research
  - linear-programming
  - grpo
  - self-distillation

SOLID-opt

SOLID-opt is a Qwen3-4B-Instruct-2507 checkpoint specialized for operations-research modeling. It is intended to turn natural-language optimization problems into structured reasoning, linear-programming formulations, and solver-oriented Python code.

This release is the checkpoint from training step 125. Training combines GRPO, solver-objective majority voting, and teacher KL localized with solver-information signals. The training configuration uses Gurobi-style nine-step outputs and a teacher-KL coefficient of 0.001.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "JamesX421/SOLID-opt"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Formulate and solve this optimization problem: ..."}
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Intended use and limitations

This model is released for research on mathematical optimization and operations-research reasoning. Generated formulations, coefficients, constraints, solver code, and claimed solutions may be incorrect, infeasible, or unsafe to use without review. Validate outputs with an appropriate solver and independent checks before using them in consequential settings.

No standalone evaluation results are included in this initial model card.