Instructions to use Neural-Hacker/KLM-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neural-Hacker/KLM-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Neural-Hacker/KLM-1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Neural-Hacker/KLM-1", device_map="auto") - PEFT
How to use Neural-Hacker/KLM-1 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Neural-Hacker/KLM-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Neural-Hacker/KLM-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Neural-Hacker/KLM-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Neural-Hacker/KLM-1
- SGLang
How to use Neural-Hacker/KLM-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 "Neural-Hacker/KLM-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": "Neural-Hacker/KLM-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 "Neural-Hacker/KLM-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": "Neural-Hacker/KLM-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Neural-Hacker/KLM-1 with Docker Model Runner:
docker model run hf.co/Neural-Hacker/KLM-1
| license: cc-by-nc-sa-4.0 | |
| language: | |
| - en | |
| - hi | |
| base_model: Qwen/Qwen3-0.6B | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| tags: | |
| - lora | |
| - peft | |
| - qwen | |
| - agriculture | |
| # KLM-1 | |
| KLM-1 is a bilingual agricultural language model supporting Hindi and English. | |
| ## Base Model | |
| Qwen3-0.6B | |
| ## Evaluation | |
| Benchmark: bharatgenai/BhashaBench-Krishi | |
| ## Benchmark Results | |
| | **Model** | **BBK_English** | **BBK_Hindi** | | |
| |-------|------------:|----------:| | |
| | Llama-3.2-1B | 29.71 | 25.21 | | |
| | Llama-3.2-1B-Instruct | 29.16 | 26.33 | | |
| | Llama-3.2-3B | 32.68 | 28.69 | | |
| | Granite-3.1-3b-a800m-base | 33.36 | 26.70 | | |
| | Sarvam-2B-v0.5 | 28.14 | 25.57 | | |
| | Sarvam-1-2B | 30.82 | 27.57 | | |
| | BharatGen-AgriParam (2.9B) | 33.10 | 27.97 | | |
| | SoketAI-Sarthi-Agri-v1 (27B) | 29.19 | 28.15 | | |
| | **KLM-1 (0.6B)** | **47.11** | **38.52** | | |
| | **KLM-2 (0.6B)** | **60.59** | **48.89** | | |
| ## Intended Use | |
| KLM-1 is intended for **research, experimentation, and academic evaluation** of bilingual agricultural language models. The model may be useful for: | |
| - Agricultural question answering | |
| - Crop advisory research | |
| - Evaluation on agriculture-specific benchmarks | |
| - Instruction-following experiments in English and Hindi | |
| The model has **not** been validated for production deployments or high-stakes agricultural decision-making. | |
| ## Access | |
| This repository is **gated**. | |
| Access requests are reviewed on a case-by-case basis. The model is shared solely for **research and evaluation purposes**. Access may be denied if the intended use does not align with the project's research objectives or licensing terms. | |
| ## Limitations | |
| - This is a research model and may generate inaccurate, incomplete, or outdated agricultural information. | |
| - Model outputs should **not** be considered professional agricultural advice. | |
| - Performance may vary across crops, regions, languages, and farming practices. | |
| - Users should independently verify all critical recommendations before taking action. | |
| ## License | |
| This model is released under the **CC BY-NC-SA 4.0** license. | |
| Commercial use, redistribution for commercial purposes, or use in commercial products or services is **not permitted** without prior authorization from the model authors. |