Instructions to use ipswy/senti-shujaa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ipswy/senti-shujaa with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ipswy/senti-shujaa")
model = AutoModelForCausalLM.from_pretrained("ipswy/senti-shujaa", 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 ipswy/senti-shujaa with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ipswy/senti-shujaa

SGLang

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

Docker Model Runner
How to use ipswy/senti-shujaa with Docker Model Runner:
```
docker model run hf.co/ipswy/senti-shujaa
```

ipswy commited on Jun 10

Commit

7a451e2

verified ·

1 Parent(s): 06f3fdd

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +59 -37

README.md CHANGED Viewed

@@ -1,37 +1,59 @@
----
-license: apache-2.0
-language:
-- en
-- sw
-tags:
-- finance
-- kenya
-- africa
-- qwen2
-- lora
-- senti-ai
-pipeline_tag: text-generation
-library_name: transformers
-base_model: Qwen/Qwen2.5-1.5B-Instruct
----
-# Senti-Shujaa V4
-**Senti-Shujaa** is a finance-specialized language model fine-tuned for Kenya and East Africa.
-## Model Details
-- **Base Model**: Qwen2.5-1.5B-Instruct
-- **Fine-tuning**: LoRA (rank=16, alpha=128) with DPO
-- **Training**: Unsloth + 4-bit QLoRA on finance-specific data
-- **Languages**: English, Swahili, Sheng
-## Capabilities
-- PAYE/Tax calculations routing
-- Budget planning and coaching
-- Credit scoring and risk assessment
-- SACCO portfolio analysis
-- Compliance and regulatory guidance (KRA, SASRA)
-- Swahili and Sheng financial literacy
-## Usage
-This model is served via the HuggingFace Serverless Inference API as the Brain of Senti AI.

+---
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+tags:
+- finance
+- kenya
+- africa
+- text-generation-inference
+pipeline_tag: text-generation
+---
+# Model Card for senti_shujaa_v4
+This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+## Quick start
+```python
+from transformers import pipeline
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+## Training procedure
+This model was trained with SFT.
+### Framework versions
+- PEFT 0.18.1
+- TRL: 0.24.0
+- Transformers: 5.5.0
+- Pytorch: 2.10.0+cu128
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2
+## Citations
+Cite TRL as:
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```