Instructions to use Cannae-AI/ReasoningLlama-Math-1B-IT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cannae-AI/ReasoningLlama-Math-1B-IT with Transformers:

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

pipe = pipeline("text-generation", model="Cannae-AI/ReasoningLlama-Math-1B-IT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cannae-AI/ReasoningLlama-Math-1B-IT")
model = AutoModelForCausalLM.from_pretrained("Cannae-AI/ReasoningLlama-Math-1B-IT")
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 Cannae-AI/ReasoningLlama-Math-1B-IT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cannae-AI/ReasoningLlama-Math-1B-IT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cannae-AI/ReasoningLlama-Math-1B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Cannae-AI/ReasoningLlama-Math-1B-IT

SGLang

How to use Cannae-AI/ReasoningLlama-Math-1B-IT 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 "Cannae-AI/ReasoningLlama-Math-1B-IT" \
    --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": "Cannae-AI/ReasoningLlama-Math-1B-IT",
		"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 "Cannae-AI/ReasoningLlama-Math-1B-IT" \
        --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": "Cannae-AI/ReasoningLlama-Math-1B-IT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Cannae-AI/ReasoningLlama-Math-1B-IT with Docker Model Runner:
```
docker model run hf.co/Cannae-AI/ReasoningLlama-Math-1B-IT
```

CannaeAI commited on Nov 18, 2025

Commit

3031856

verified ·

1 Parent(s): e460ff2

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@@ -24,13 +24,13 @@ This is a fine-tuned version of [unsloth/Llama-3.2-1B](https://huggingface.co/un
 - **License :** apache-2.0
 - **Finetuned from model :** unsloth/Llama-3.2-1B
 ## Benchmarks:
-We evaluate ReasoningLlama-Math-1B-IT on GSM8K using the standard lm-eval 5-shot exact-match protocol. Under identical decoding and extraction settings, the model achieves:
 | Model                         | Params | GSM8K (5-shot, EM) |
 | ----------------------------- | ------ | ------------------ |
 | **ReasoningLlama-Math-1B-IT** | 1B     | **30.7%**          |
-| Llama-3.2-1B-Instruct         | 1B     | 6.8%               |
 <p align="center">
-  <img alt="gpt-oss-safeguard-20b" src="https://huggingface.co/Cannae-AI/ReasoningLlama-Math-1B-IT/resolve/main/ChatGPT%20Image%2018%20nov.%202025%2C%2020_55_23.png">
 </p>

 - **License :** apache-2.0
 - **Finetuned from model :** unsloth/Llama-3.2-1B
 ## Benchmarks:
+We evaluate ReasoningLlama-Math-1B-IT on GSM8K using the standard lm-eval 5-shot exact-match protocol. Under identical decoding and extraction settings,ReasoningLlama-Math-1B-IT outperforms Meta’s Llama-3.2-1B by more than 4×, demonstrating a substantial improvement in small-model mathematical reasoning capability.
 | Model                         | Params | GSM8K (5-shot, EM) |
 | ----------------------------- | ------ | ------------------ |
 | **ReasoningLlama-Math-1B-IT** | 1B     | **30.7%**          |
+| Llama-3.2-1B                  | 1B     | 6.8%               |
 <p align="center">
+  <img alt="ReasonningLlama-Math-1B-IT" src="https://huggingface.co/Cannae-AI/ReasoningLlama-Math-1B-IT/resolve/main/ChatGPT%20Image%2018%20nov.%202025%2C%2020_55_23.png">
 </p>