Instructions to use CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha with Transformers:

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

pipe = pipeline("text-generation", model="CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha")
model = AutoModelForCausalLM.from_pretrained("CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha

SGLang

How to use CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha 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 "CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha" \
    --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": "CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha",
		"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 "CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha" \
        --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": "CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha with Docker Model Runner:
```
docker model run hf.co/CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha
```

Mistral-Nemo-12B-R1-v0.1alpha / README.md

CreitinGameplays

Update README.md

48679ba verified over 1 year ago

762 Bytes

license: mit
datasets:
  - CreitinGameplays/r1_annotated_math-mistral
  - CreitinGameplays/DeepSeek-R1-Distill-Qwen-32B_NUMINA_train_amc_aime-mistral
language:
  - en
base_model:
  - mistralai/Mistral-Nemo-Instruct-2407
pipeline_tag: text-generation
library_name: transformers

Run the model:

import torch
from transformers import pipeline

model_id = "CreitinGameplays/Mistral-Nemo-12B-R1-v0.1alpha"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
messages = [
    {"role": "user", "content": "How many r's are in strawberry?"},
]
outputs = pipe(
    messages,
    temperature=0.8,
    top_p=1.0,
    top_k=50,
    max_new_tokens=4096,
)
print(outputs[0]["generated_text"][-1])