Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
License: mitModel Details
| Property | Value |
|---|---|
| Base model | mistralai/Mistral-7B-Instruct-v0.3 |
| Fine-tuning method | QLoRA (4-bit NF4 + LoRA adapters) |
| LoRA rank / alpha | r=16 / α=32 |
| Training epochs | 3 |
| Effective batch size | 16 (2 × 8 grad accumulation steps) |
| Learning rate | 2e-4 (cosine schedule, 3 % warmup) |
| Max sequence length | 2048 tokens |
| Training hardware | Kaggle T4 × 2 (via Unsloth) |
| Training framework | Unsloth + HuggingFace TRL SFTTrainer |
| Precision | FP16 |
| Language | French (fr) |
| License | MIT |
Training Dataset
The SFT dataset (togolm/togolm-corpus-v1)
consists of instruction–response pairs generated from the TogoLM corpus — a curated collection of
documents scraped from Togolese official sources:
| Source | Domain |
|---|---|
jo.gouv.tg | Journal Officiel — laws and decrees |
presidence.gouv.tg | Presidency — presidential acts and speeches |
assemblee-nationale.tg | National Assembly — parliamentary texts |
inseed.tg | National Statistics Institute — economic and demographic data |
service-public.gouv.tg | Public services directory |
finances.gouv.tg / education.gouv.tg / agriculture.gouv.tg | Ministries |
icilome.com | Local news and analysis |
Q&A pairs were generated using Gemini 2.5 Flash and formatted in the Alpaca instruction template.
Usage
Load with Unsloth (recommended)
python
from unsloth import FastLanguageModelmodel, tokenizer = FastLanguageModel.from_pretrained(model_name="togolm/togolm-7b-instruct-v1",max_seq_length=2048,load_in_4bit=True,)FastLanguageModel.for_inference(model)prompt = """Below is an instruction about Togo. Write a response that answers it accurately.### Instruction:Quel est le taux d'imposition sur les sociétés au Togo ?### Response:"""inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Load with standard Transformers + PEFT
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelbase = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3",load_in_4bit=True,device_map="auto",)model = PeftModel.from_pretrained(base, "togolm/togolm-7b-instruct-v1")tokenizer = AutoTokenizer.from_pretrained("togolm/togolm-7b-instruct-v1")
Prompt Format
The model was fine-tuned with the Alpaca instruction template:
markdown
Below is an instruction about Togo. Write a response that answers it accurately.### Instruction:{your question about Togo}### Response:
Intended Use
- Answering questions about Togolese law, administration, statistics, and public services in French
- Retrieval-augmented generation (RAG) combined with the TogoLM corpus
- Research on low-resource African languages and francophone AI
Out-of-Scope Use
- General-purpose chat or tasks unrelated to Togo
- Legal or medical advice — always verify with official Togolese sources
- Languages other than French (coverage is limited)
Project
This model is part of TogoLM — the first open-source AI infrastructure layer focused on Togo, covering corpus collection, RAG engine, fine-tuned LLM, and a public REST API.
- GitHub: github.com/omarfarouk228/togolm
- Dataset: togolm/togolm-corpus-v1
Citation
bibtex
@misc{togolm2026,author = {Kougbada, Omar Farouk},title = {TogoLM: Open-Source AI Infrastructure for Togo},year = {2026},howpublished = {\url{https://huggingface.co/togolm/togolm-7b-instruct-v1}},}
Model provider
togolm
Model tree
Base
mistralai/Mistral-7B-Instruct-v0.3
Adapter
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information