Instructions to use Cognitive-Lab/Ambari-7B-Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cognitive-Lab/Ambari-7B-Instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Cognitive-Lab/Ambari-7B-Instruct-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cognitive-Lab/Ambari-7B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("Cognitive-Lab/Ambari-7B-Instruct-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Cognitive-Lab/Ambari-7B-Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cognitive-Lab/Ambari-7B-Instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cognitive-Lab/Ambari-7B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Cognitive-Lab/Ambari-7B-Instruct-v0.1

SGLang

How to use Cognitive-Lab/Ambari-7B-Instruct-v0.1 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 "Cognitive-Lab/Ambari-7B-Instruct-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cognitive-Lab/Ambari-7B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Cognitive-Lab/Ambari-7B-Instruct-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cognitive-Lab/Ambari-7B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Cognitive-Lab/Ambari-7B-Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/Cognitive-Lab/Ambari-7B-Instruct-v0.1
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Ambari-7B-Instruct-v0.1

Overview

Ambari-7B-Instruct-v0.1 is an extension of the Ambari series, a bilingual English/Kannada model developed and released by Cognitivelab.in. This model is specialized for natural language understanding tasks, particularly in the context of instructional pairs. It is built upon the Ambari-7B-Base-v0.1 model, utilizing a fine-tuning process with a curated dataset of translated instructional pairs.

Usage

To use the Ambari-7B-Instruct-v0.1 model, you can follow the example code below:

# Usage
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM

model = LlamaForCausalLM.from_pretrained('Cognitive-Lab/Ambari-7B-Instruct-v0.1')
tokenizer = LlamaTokenizer.from_pretrained('Cognitive-Lab/Ambari-7B-Instruct-v0.1')

prompt = "Give me 10 Study tips in Kannada."
inputs = tokenizer(prompt, return_tensors="pt")

# Generate
generate_ids = model.generate(inputs.input_ids, max_length=1000)
decoded_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

print(decoded_output)

Learn More

Read more about Ambari-7B-Instruct-v0.1 and its applications in natural language understanding tasks on the Cognitivelab.in blog.

Dataset Information

The model is fine-tuned using the Kannada Instruct Dataset, a collection of translated instructional pairs. The dataset includes English instruction and output pairs, as well as their corresponding translations in Kannada. The intentional diversification of the dataset, encompassing various language combinations, enhances the model's proficiency in cross-lingual tasks.

Bilingual Instruct Fine-tuning

The model underwent a pivotal stage of supervised fine-tuning with low-rank adaptation, focusing on bilingual instruct fine-tuning. This approach involved training the model to respond adeptly in either English or Kannada based on the language specified in the user prompt or instruction.

References

Downloads last month: 25

Safetensors

Model size

7B params

Tensor type

BF16

Dataset used to train Cognitive-Lab/Ambari-7B-Instruct-v0.1

Collection including Cognitive-Lab/Ambari-7B-Instruct-v0.1

Ambari

Collection

A series of Open Source Bilingual Kannada-English Large Language Models • 5 items • Updated Dec 1, 2025 • 7