Instructions to use ubitech-edg/commandr-35b-cpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubitech-edg/commandr-35b-cpt with Transformers:

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

pipe = pipeline("text-generation", model="ubitech-edg/commandr-35b-cpt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ubitech-edg/commandr-35b-cpt")
model = AutoModelForCausalLM.from_pretrained("ubitech-edg/commandr-35b-cpt")
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

vLLM

How to use ubitech-edg/commandr-35b-cpt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubitech-edg/commandr-35b-cpt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubitech-edg/commandr-35b-cpt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubitech-edg/commandr-35b-cpt

SGLang

How to use ubitech-edg/commandr-35b-cpt 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 "ubitech-edg/commandr-35b-cpt" \
    --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": "ubitech-edg/commandr-35b-cpt",
		"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 "ubitech-edg/commandr-35b-cpt" \
        --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": "ubitech-edg/commandr-35b-cpt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ubitech-edg/commandr-35b-cpt with Docker Model Runner:
```
docker model run hf.co/ubitech-edg/commandr-35b-cpt
```

kosmylo1992 commited on Oct 19, 2025

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----
-license: apache-2.0
----

+# Command-R 35B — CPT (Continual Pretraining)
+**Model type:** Causal Language Model
+**Base model:** [CohereLabs/c4ai-command-r-v01](https://huggingface.co/CohereLabs/c4ai-command-r-v01)
+**License:** Apache 2.0
+**Framework:** [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
+---
+## Overview
+`commandr-CPT` is a **continual-pretrained** version of Cohere's Command-R 35B model, trained to further improve domain adaptation and general reasoning abilities.
+The continual pretraining was performed using Axolotl on the Leonardo EuroHPC system.
+---
+## Training Setup
+**Objective:** Language modeling (unsupervised continual pretraining)
+**Adapter type:** LoRA
+**Precision:** bfloat16
+**Hardware:** 8 nodes × 2 × NVIDIA A100 64GB GPUs
+**Training duration:** 24 hours
+**Framework:** DeepSpeed ZeRO-1, Axolotl, PyTorch 2.5.1+cu121
+---
+## Dataset
+Public energy domain text sources:
+- `arxiv.jsonl` — scientific and technical papers
+- `gov.jsonl` — public governmental documents
+- `news.jsonl` — news articles
+- `wiki.jsonl` — Wikipedia text
+---
+## Hyperparameters
+| Parameter | Value |
+|------------|-------|
+| Sequence length | 2048 |
+| Micro batch size | 1 |
+| Gradient accumulation | 4 |
+| Learning rate | 2e-4 |
+| LR scheduler | cosine |
+| Optimizer | AdamW (8-bit) |
+| LoRA rank (r) | 16 |
+| LoRA alpha | 32 |
+| LoRA dropout | 0.05 |
+| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Epochs | 1 |
+| Warmup steps | 10 |
+| Weight decay | 0.0 |