Instructions to use manishiitg/open-aditi-hi-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use manishiitg/open-aditi-hi-v2 with Transformers:

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

pipe = pipeline("text-generation", model="manishiitg/open-aditi-hi-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("manishiitg/open-aditi-hi-v2")
model = AutoModelForCausalLM.from_pretrained("manishiitg/open-aditi-hi-v2", 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 manishiitg/open-aditi-hi-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "manishiitg/open-aditi-hi-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "manishiitg/open-aditi-hi-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/manishiitg/open-aditi-hi-v2

SGLang

How to use manishiitg/open-aditi-hi-v2 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 "manishiitg/open-aditi-hi-v2" \
    --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": "manishiitg/open-aditi-hi-v2",
		"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 "manishiitg/open-aditi-hi-v2" \
        --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": "manishiitg/open-aditi-hi-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use manishiitg/open-aditi-hi-v2 with Docker Model Runner:
```
docker model run hf.co/manishiitg/open-aditi-hi-v2
```

open-aditi-hi-v2 / README.md

manishiitg

Adding Evaluation Results (#1)

e55fc6e verified over 2 years ago

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metadata

language:
  - hi
  - en
license: apache-2.0
base_model: teknium/OpenHermes-2.5
model-index:
  - name: open-aditi-hi-v2
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 59.39
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=manishiitg/open-aditi-hi-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 82.01
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=manishiitg/open-aditi-hi-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 61.41
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=manishiitg/open-aditi-hi-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 45.84
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=manishiitg/open-aditi-hi-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 77.19
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=manishiitg/open-aditi-hi-v2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 30.02
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=manishiitg/open-aditi-hi-v2
          name: Open LLM Leaderboard

Model trained on Hindi and English data.

Try it out: https://colab.research.google.com/drive/1A_hbsq1vrCeAh3dEMvtwxxNxcNZ1BUyW?usp=sharing

For sample responose on different prompts checkout: https://github.com/manishiitg/hi-llm-eval

Language Hi

Model	implicit_hate	flores	indicwikibio	hellaswag-indic	truthfulqa-hi	boolq-hi	indicheadline	indic-arc-easy	indicqa	indic-arc-challenge	indicsentiment	xlsum-hi	indicxparaphrase	mmlu_hi
open-aditi-hi-v2	11.5021	43.6822	0.4846	0.2404	0.6934	0.8541	0.4565	0.4979	0.0795	0.4462	0.9729	0.4213	0.6838	0.3253
OpenHermes-2.5-Mistral-7B	0.2068	30.3465	0.3332	0.2485	0.3234	0.5979	0.1996	0.3523	0.2721	0.3396	0.9048	0.1774	0.8766	0.2769
open-aditi-hi-v1	8.6105	40.2376	0.4104	0.0848	0.4230	0.3758	0.4248	0.3889	0.1306	0.3558	0.8798	0.4212	0.5939	0.1398
Airavata	0.0663	58.0555	0.0637	0.0254	0.2122	0.0373	0.4346	0.1128	0.1008	0.0836	0.8437	0.4650	0.3277	0.1336

Language En

Model	boolq	hellaswag	mmlu	truthfulqa	xlsum	arc-easy-exact	arc-challenge
OpenHermes-2.5-Mistral-7B	0.4061	0.7999	0.5991	0.2081	0.4328	0.8687	0.7790
open-aditi-hi-v2	0.3982	0.4738	0.5544	0.2999	0.4349	0.8388	0.7235
open-aditi-hi-v1	0.0434	0.3509	0.2597	0.3317	0.4288	0.7588	0.6271
Airavata	0.0437	0.0277	0.1165	0.3586	0.4393	0.2534	0.1630

Task: flores Metric: chrf

Task: implicit_hate Metric: chrf

Task: indicsentiment Metric: accuracy

Task: indicxparaphrase Metric: accuracy

Task: boolq-hi Metric: accuracy

Task: truthfulqa-hi Metric: accuracy

Task: indic-arc-easy Metric: accuracy

Task: indicwikibio Metric: bleurt

Task: xlsum-hi Metric: bleurt

Task: indicheadline Metric: bleurt

Task: indic-arc-challenge Metric: accuracy

Task: mmlu_hi Metric: average_acc

Task: indicqa Metric: accuracy

Task: hellaswag-indic Metric: accuracy

Task: arc-easy-exact Metric: accuracy

Task: hellaswag Metric: accuracy

Task: arc-challenge Metric: accuracy

Task: mmlu Metric: average_acc

Task: xlsum Metric: bleurt

Task: boolq Metric: accuracy

Task: truthfulqa Metric: accuracy

Model evaluation on OpenLLM LeaderBoard

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	59.31
AI2 Reasoning Challenge (25-Shot)	59.39
HellaSwag (10-Shot)	82.01
MMLU (5-Shot)	61.41
TruthfulQA (0-shot)	45.84
Winogrande (5-shot)	77.19
GSM8k (5-shot)	30.02