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

Libraries

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
```

Umranz commited on Aug 13

Commit

83ca8d0

verified ·

1 Parent(s): 8ba5f9f

Embed graphical sweep results table image into README

Browse files

Files changed (1) hide show

README.md +3 -5

README.md CHANGED Viewed

@@ -119,11 +119,9 @@ Conducted a 3-run hyperparameter sweep using LiquidAI LEAP Finetune to evaluate
 ### Hyperparameter Sweep Results
-| Run | Configuration | Epochs | Batch Size | Learning Rate | Train Loss | Eval Loss | Checkpoint |
-|:---|:---|:---:|:---:|:---:|:---:|:---:|:---:|
-| **Run B** | **LoRA r=32, alpha=64, drop=0.05** | **4** | **32** | **2.0e-5** | **`0.3500`** | **`0.4074`** | **Deployed (Merged)** |
-| **Run A** | LoRA r=64, alpha=128, drop=0.10 | 5 | 32 | 1.5e-5 | `0.3826` | `0.4238` | Archived |
-| **Run C** | LoRA r=64, alpha=128, drop=0.05 | 3 | 32 | 2.5e-5 | `0.4029` | `0.4269` | Archived |
 ### Weights & Biases Telemetry

 ### Hyperparameter Sweep Results
+<div align="center">
+<img src="https://huggingface.co/Umranz/Shruti-Soft-2.6b/resolve/main/sweep_results_table.png" width="100%" alt="Hyperparameter Sweep Results Table" style="border-radius: 8px; margin: 12px 0;" />
+</div>
 ### Weights & Biases Telemetry