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
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Umranz
/
Shruti-Soft-2.6b

Text Generation
Transformers
Safetensors
PyTorch
English
Hindi
lfm2
conversational
roleplay
companion
character
uncensored
fine-tuned
merged
sft
chat
liquidai
lfm
lfm2.5
chatml
Model card Files Files and versions
xet
Community

Instructions to use Umranz/Shruti-Soft-2.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Umranz/Shruti-Soft-2.6b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Umranz/Shruti-Soft-2.6b")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Umranz/Shruti-Soft-2.6b")
    model = AutoModelForCausalLM.from_pretrained("Umranz/Shruti-Soft-2.6b", 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 Umranz/Shruti-Soft-2.6b with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Umranz/Shruti-Soft-2.6b"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Umranz/Shruti-Soft-2.6b",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Umranz/Shruti-Soft-2.6b
  • SGLang

    How to use Umranz/Shruti-Soft-2.6b 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 "Umranz/Shruti-Soft-2.6b" \
        --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": "Umranz/Shruti-Soft-2.6b",
    		"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 "Umranz/Shruti-Soft-2.6b" \
            --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": "Umranz/Shruti-Soft-2.6b",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use Umranz/Shruti-Soft-2.6b with Docker Model Runner:

    docker model run hf.co/Umranz/Shruti-Soft-2.6b
Shruti-Soft-2.6b / LFM2.5-2.6B-heretic-sft-shruti-tra-all-lr2em05-w0p1-lora_a-e0s100-20260813_093801
17.9 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
Umranz's picture
Umranz
Phase 3: Shruti-Soft fine-tune (source: Umranz/Shruti-Soft-2.6b-run-b)
8095de3 verified about 1 month ago
  • README.md
    5.21 kB
    Phase 3: Shruti-Soft fine-tune (source: Umranz/Shruti-Soft-2.6b-run-b) about 1 month ago
  • adapter_config.json
    1.26 kB
    Phase 3: Shruti-Soft fine-tune (source: Umranz/Shruti-Soft-2.6b-run-b) about 1 month ago
  • rng_state.pth
    14.7 kB
    xet
    Phase 3: Shruti-Soft fine-tune (source: Umranz/Shruti-Soft-2.6b-run-b) about 1 month ago
  • scheduler.pt
    1.47 kB
    xet
    Phase 3: Shruti-Soft fine-tune (source: Umranz/Shruti-Soft-2.6b-run-b) about 1 month ago
  • tokenizer.json
    17.9 MB
    xet
    Phase 3: Shruti-Soft fine-tune (source: Umranz/Shruti-Soft-2.6b-run-b) about 1 month ago
  • tokenizer_config.json
    468 Bytes
    Phase 3: Shruti-Soft fine-tune (source: Umranz/Shruti-Soft-2.6b-run-b) about 1 month ago