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

Libraries

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

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

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

Use Docker

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

SGLang

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

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

Command-R 35B — SFT (Supervised Fine-Tuning on Synthetic QA)

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

Overview

commandr-35b-sft is a supervised fine-tuned variant of Cohere’s Command-R 35B model.
Fine-tuning was performed on a high-quality instruction-following dataset using LoRA adapters, enabling improved conversational reasoning and question answering.

Training was conducted on the Leonardo EuroHPC system.

Training Setup

Objective: Supervised fine-tuning (instruction following)
Adapter type: LoRA
Precision: bfloat16
Hardware: 8 nodes × 2 × NVIDIA A100 64GB GPUs
Framework: DeepSpeed ZeRO-1, Axolotl, PyTorch 2.5.1+cu121
Runtime: ~6 hours
Dataset split: 70% train / 30% validation

Dataset

Name: axolotl_deduplicated_synthetic_qa.jsonl
Type: Instruction-following synthetic QA dataset

Each sample follows a QA/chat format used in the alpaca_chat.load_qa schema.

Hyperparameters

Parameter	Value
Sequence length	2048
Micro batch size	1
Gradient accumulation	2
Epochs	1
Learning rate	0.0001
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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