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
```

Command-R 35B — CPT (Continual Pretraining with LoRA)

Model type: Causal Language Model
Base model: CohereLabs/c4ai-command-r-v01
License: Apache 2.0
Framework: Axolotl

Overview

commandr-35b-cpt is a continual-pretrained version of Cohere's Command-R 35B model, trained with LoRA adapters for efficient enregy doman adaptation. The goal of CPT is to extend the model’s general reasoning, factual grounding, and domain knowledge across science, governance, and energy-domain text.

Training was performed on the Leonardo EuroHPC system using Axolotl with DeepSpeed ZeRO-1 optimization.

Training Setup

Objective: Language modeling (unsupervised continual pretraining)
Adapter type: LoRA
Precision: bfloat16
Hardware: 8 nodes × 2 × NVIDIA A100 64GB GPUs
Framework: DeepSpeed ZeRO-1, Axolotl, PyTorch 2.5.1+cu121
Runtime: ~24 hours
Checkpoints: Saved every 1/5 of an epoch

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
Epochs	1
Max steps	10000
Learning rate	0.0002
LR scheduler	cosine
Optimizer	AdamW (8-bit)
Warmup steps	10
Weight decay	0.0
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05
LoRA target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Gradient checkpointing	✅
Flash attention	✅
Auto resume	✅
Loss watchdog threshold	5.0
Loss watchdog patience	3

Tokenizer

Tokenizer type: AutoTokenizer
Special token: <|end_of_text|> as pad_token

Downloads last month: 4

Model tree for ubitech-edg/commandr-35b-cpt

Base model

CohereLabs/c4ai-command-r-v01

Adapter

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Adapters

1 model

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commandr-35b-cpt