Instructions to use nithiyn/codestral-neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nithiyn/codestral-neuron with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nithiyn/codestral-neuron")
model = AutoModelForCausalLM.from_pretrained("nithiyn/codestral-neuron", 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 nithiyn/codestral-neuron with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nithiyn/codestral-neuron

SGLang

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

Docker Model Runner
How to use nithiyn/codestral-neuron with Docker Model Runner:
```
docker model run hf.co/nithiyn/codestral-neuron
```

codestral-neuron

File size: 792 Bytes

---
language:
- en
base_model:
- mistralai/Codestral-22B-v0.1
pipeline_tag: text-generation
tags:
- code
- code-generation
---
This repository contains AWS Inferentia2 and neuronx compatible checkpoints for [Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1). You can find detailed information about the base model on its [Model Card](https://huggingface.co/mistralai/Codestral-22B-v0.1).

This model has been exported to the neuron format using specific input_shapes and compiler parameters detailed in the paragraphs below.

It has been compiled to run on an inf2.24xlarge instance on AWS. Note that while the inf2.24xlarge has 12 cores, this compilation uses 12.

- SEQUENCE_LENGTH = 4096
- BATCH_SIZE = 4
- NUM_CORES = 12 
- PRECISION = "bf16"

---
license: mnpl
---