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

Libraries

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

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

pipe = pipeline("text-generation", model="ubitech-edg/commandr-35b-cpt-sft")
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-sft")
model = AutoModelForCausalLM.from_pretrained("ubitech-edg/commandr-35b-cpt-sft")
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-sft 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-sft"
# 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-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Command-R 35B — CPT + SFT

Model type: Causal Language Model
Base model: commandr-35b-cpt
License: Apache 2.0
Framework: Axolotl

Overview

commandr-35b-cpt-sft combines both continual pretraining (CPT) and supervised fine-tuning (SFT) in a two-stage process. The model first learns additional general-domain representations (CPT), then undergoes supervised instruction tuning (SFT) on synthetic QA data.
This combination enhances factual grounding, fluency, and instruction adherence.

Training was performed on the Leonardo EuroHPC system.

Training Setup

Stage 1 (CPT): Domain-adaptive continual pretraining
Stage 2 (SFT): Instruction fine-tuning
Adapter type: LoRA
Precision: bfloat16
Hardware: 8 nodes × 2 × NVIDIA A100 64GB GPUs
Framework: DeepSpeed ZeRO-1, Axolotl, PyTorch 2.5.1+cu121

Datasets

CPT Stage:

arxiv.jsonl
gov.jsonl
news.jsonl
wiki.jsonl

SFT Stage:

axolotl_deduplicated_synthetic_qa.jsonl

Hyperparameters

Parameter	Value
Sequence length	2048
Micro batch size	1
Gradient accumulation	2
Epochs	1
Learning rate	0.00008
LR scheduler	cosine
Optimizer	AdamW (8-bit)
Warmup steps	20
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	8.0
Loss watchdog patience	20

Tokenizer

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

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

Base model

CohereLabs/c4ai-command-r-v01

Adapter

ubitech-edg/commandr-35b-cpt

Adapter

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this model

Adapters

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commandr-35b-cpt-sft