Instructions to use NaolBM/LFM2.5-1.2B-AfriBase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NaolBM/LFM2.5-1.2B-AfriBase with Transformers:

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

pipe = pipeline("text-generation", model="NaolBM/LFM2.5-1.2B-AfriBase")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NaolBM/LFM2.5-1.2B-AfriBase")
model = AutoModelForCausalLM.from_pretrained("NaolBM/LFM2.5-1.2B-AfriBase", 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 NaolBM/LFM2.5-1.2B-AfriBase with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NaolBM/LFM2.5-1.2B-AfriBase"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NaolBM/LFM2.5-1.2B-AfriBase",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NaolBM/LFM2.5-1.2B-AfriBase

SGLang

How to use NaolBM/LFM2.5-1.2B-AfriBase 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 "NaolBM/LFM2.5-1.2B-AfriBase" \
    --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": "NaolBM/LFM2.5-1.2B-AfriBase",
		"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 "NaolBM/LFM2.5-1.2B-AfriBase" \
        --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": "NaolBM/LFM2.5-1.2B-AfriBase",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use NaolBM/LFM2.5-1.2B-AfriBase with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NaolBM/LFM2.5-1.2B-AfriBase to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NaolBM/LFM2.5-1.2B-AfriBase to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NaolBM/LFM2.5-1.2B-AfriBase to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="NaolBM/LFM2.5-1.2B-AfriBase",
    max_seq_length=2048,
)

Docker Model Runner
How to use NaolBM/LFM2.5-1.2B-AfriBase with Docker Model Runner:
```
docker model run hf.co/NaolBM/LFM2.5-1.2B-AfriBase
```

NaolBM commited on Feb 23

Commit

eb52bb5

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1 Parent(s): f02bbb7

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@@ -32,7 +32,7 @@ This model extends the LFM2.5-1.2B base model with a **massively expanded vocabu
 - **Size:** 1.2B parameters
 - **Base vocab:** 64,400
-- **Extended vocab:** **110,405**
 - **Embedding dim:** 2048
 ## 📊 Vocabulary Extension
@@ -41,7 +41,7 @@ This model extends the LFM2.5-1.2B base model with a **massively expanded vocabu
 |--------|-----------|--------------|
 | [NaolBM/Africa-BBPE](https://huggingface.co/NaolBM/Africa-BBPE)  | Swahili, Hausa, Yoruba, Arabic | 50000 |
 | Base model | Original English | 64,400 |
-| **TOTAL** | | **110,405** |
 ## 🏆 Tokenization Performance

 - **Size:** 1.2B parameters
 - **Base vocab:** 64,400
+- **Extended vocab:** **110,397**
 - **Embedding dim:** 2048
 ## 📊 Vocabulary Extension
 |--------|-----------|--------------|
 | [NaolBM/Africa-BBPE](https://huggingface.co/NaolBM/Africa-BBPE)  | Swahili, Hausa, Yoruba, Arabic | 50000 |
 | Base model | Original English | 64,400 |
+| **TOTAL** | | **110,397** |
 ## 🏆 Tokenization Performance