Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

nur-dev
/
farabi-1.7b

Text Generation
Transformers
Safetensors
Kazakh
Russian
English
qwen3
kazakh
multilingual
instruction-tuned
function-calling
conversational
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use nur-dev/farabi-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nur-dev/farabi-1.7b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="nur-dev/farabi-1.7b")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("nur-dev/farabi-1.7b")
    model = AutoModelForCausalLM.from_pretrained("nur-dev/farabi-1.7b")
    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
  • vLLM

    How to use nur-dev/farabi-1.7b with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "nur-dev/farabi-1.7b"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "nur-dev/farabi-1.7b",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/nur-dev/farabi-1.7b
  • SGLang

    How to use nur-dev/farabi-1.7b 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 "nur-dev/farabi-1.7b" \
        --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": "nur-dev/farabi-1.7b",
    		"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 "nur-dev/farabi-1.7b" \
            --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": "nur-dev/farabi-1.7b",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use nur-dev/farabi-1.7b with Docker Model Runner:

    docker model run hf.co/nur-dev/farabi-1.7b

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.

Log in or Sign Up to review the conditions and access this model content.

Gated model
You can list files but not access them

Preview of files found in this repository
  • .gitattributes
    1.57 kB
    Farabi-1.7B: initial release 3 months ago
  • README.md
    3.75 kB
    README: anti-loop sampling defaults (temp 0.4, top_p 0.9, presence/freq penalties) about 23 hours ago
  • all_results.json
    253 Bytes
    upload pronto-v15: grammar removed, KK-think reasoning, native-lang sysprompts 17 days ago
  • benchmark_chart.png
    80.9 kB
    Update benchmark chart 3 months ago
  • chat_template.jinja
    4.17 kB
    Farabi-1.7B: initial release 3 months ago
  • config.json
    1.42 kB
    Initial publish of Farabi-1.7B (v23-sft-12000, KazMMLU 0.4563) 1 day ago
  • generation_config.json
    187 Bytes
    Initial publish of Farabi-1.7B (v23-sft-12000, KazMMLU 0.4563) 1 day ago
  • model.safetensors
    4.06 GB
    xet
    Update to v23-sft-56000 about 1 hour ago
  • tokenizer.json
    11.4 MB
    xet
    Farabi-1.7B: initial release 3 months ago
  • tokenizer_config.json
    1.42 kB
    Initial publish of Farabi-1.7B (v23-sft-12000, KazMMLU 0.4563) 1 day ago
  • train_results.json
    253 Bytes
    upload pronto-v15: grammar removed, KK-think reasoning, native-lang sysprompts 17 days ago