Instructions to use dwivedula/Samrudh-1-Brahma-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dwivedula/Samrudh-1-Brahma-7B with Transformers:

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

pipe = pipeline("text-generation", model="dwivedula/Samrudh-1-Brahma-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dwivedula/Samrudh-1-Brahma-7B")
model = AutoModelForCausalLM.from_pretrained("dwivedula/Samrudh-1-Brahma-7B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dwivedula/Samrudh-1-Brahma-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dwivedula/Samrudh-1-Brahma-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": "dwivedula/Samrudh-1-Brahma-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dwivedula/Samrudh-1-Brahma-7B

SGLang

How to use dwivedula/Samrudh-1-Brahma-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 "dwivedula/Samrudh-1-Brahma-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": "dwivedula/Samrudh-1-Brahma-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 "dwivedula/Samrudh-1-Brahma-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": "dwivedula/Samrudh-1-Brahma-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use dwivedula/Samrudh-1-Brahma-7B with Docker Model Runner:
```
docker model run hf.co/dwivedula/Samrudh-1-Brahma-7B
```

👑 Samrudh-1: Sovereign 7B Indic Reasoning Model

Thought in Bharat. Built for the World. Sovereign by Design, Impact by Intent.

🌟 Executive Summary

Samrudh-1 (7B) is a sovereign, post-trained instruction-tuned reasoning model engineered and released by Samrudh (dwivedula).

Trained on an Apache 2.0 foundation using high-efficiency 4-bit NormalFloat4 (NF4) QLoRA, Samrudh-1 was specifically post-trained to serve as the core cognitive brain for an upcoming sovereign autonomous intelligence operating system. It bridges high-precision enterprise compliance, quantitative financial risk models, biomedical pharmacology, and authentic regional Indic dialects.

📦 Repository Artifacts

Standalone 16-bit Full Model: dwivedula/Samrudh-1-Brahma-7B
Offline 4-bit GGUF Model (Ollama / Llama.cpp): dwivedula/Samrudh-1-Brahma-7B-GGUF

⚡ Core Architectural Capabilities

1. 🗣️ Native Multi-Dialect Indic Reasoning

Unlike models that produce stilted, machine-translated Telugu, Samrudh-1 natively captures cultural cadence, humor, and syntactic precision across regional linguistic registers:

Telangana Dialect: Youth vernacular, colloquial cadence, and conversational expressions ("కిర్రాక్ మవా, గమ్మత్గుంది!").
Rayalaseema Dialect: Bold colloquial syntax and cultural idiom ("చూడబ్బా నాయనా!").
Coastal Andhra Dialect: Formal, respectful phrasing ("ఏవండీ బాబాయ్!").
Paninian Sanskrit Generative Syntax: AST code transformations structured around ancient Ashtadhyayi formal grammar rules.

2. 🎯 Conquering "Lost in the Middle" (100% NIAH Recall)

Most LLMs suffer from severe attention degradation across long documents (the U-shaped attention curve). Samrudh-1 was trained using bidirectional prompt sandwiching and strict verbatim quote grounding:

1.5k Window: 100% precision needle extraction.
4k Window: 100% precision needle extraction.
8k Window: 100% precision needle extraction.
Zero semantic hallucination on middle-ground document tokens.

3. ⚖️ Enterprise Legal Governance (6 Fatal SaaS Traps)

Native comprehension and audit capabilities against dangerous contractual clauses:

COPPA Safe Harbor: Age-gating defenses against $53,000 FTC violations.
GDPR Local Font Compliance: Detection of external Google Fonts IP-leakage risks (Munich Regional Court rulings).
California CIPA Wiretapping Defense: Masking password and credit card inputs on session replays ($5,000 statutory penalties).
CAN-SPAM & ROSCA: Automated unsubscribe verification and explicit subscription auto-renewal terms.
DMCA §512: Automated designated agent compliance.

4. 📈 Quantitative Finance & Risk Gatekeeping

95% Value-at-Risk (VaR): Parametric and historical Monte Carlo risk simulations.
Kuvera Circuit Breaker: Automated liquidation triggers upon reaching a 14.85% maximum drawdown threshold.
Kelly Criterion: Fractional optimal position sizing under volatility constraints.

5. 🧬 Biomedical Pharmacology & Clinical Decision Support

Pharmacology Telemetry: Precision binding constants (e.g., IC50 affinity modeling down to sub-nanomolar scales).
Clinical Trial Ingestion: ClinicalTrials.gov APIv2 schema comprehension and exclusion criteria auditing.

🛠️ Model Specifications

Parameter	Specification
Base Architecture	Qwen 2.5 7B Instruct (Apache 2.0)
Quantization	4-bit NormalFloat4 (NF4) with Double Quantization
LoRA Rank ($r$)	16
LoRA Alpha ($\alpha$)	32
Target Projections	All linear attention matrices (`q`, `k`, `v`, `o`, `gate`, `up`, `down`)
Hardware Used	1x Tesla T4 GPU (15GB VRAM) on Google Colab
Peak VRAM Consumption	<6.8 GB VRAM (Zero OOM)
Optimizer	Paged 8-bit AdamW
Context Window	2,048 (Train) / Up to 32,768 (Inference via RoPE)
License	Apache License 2.0 (100% Royalty-Free & Commercial)

🚀 Quickstart Usage

Option 1: Python (`transformers`)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dwivedula/Samrudh-1-Brahma-7B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Who are you and what makes your reasoning architecture sovereign?

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Option 2: 1-Click Local Execution with Ollama

Run directly on your local machine (even on laptops without a dedicated GPU):

# Pull and run the 4-bit quantized model directly from Hugging Face
ollama run hf.co/dwivedula/Samrudh-1-Brahma-7B-GGUF

Or using the local GGUF file:

# 1. Download Qwen2.5-7B-Instruct.Q4_K_M.gguf and Modelfile
# 2. Build and launch:
ollama create samrudh-1 -f Modelfile
ollama run samrudh-1

📊 Benchmark Evaluations (Needle-In-A-Haystack)

Evaluated across varying context depths (Start 10%, Middle 50%, End 90%):

Document Length	Start (Primacy)	Middle (U-Curve Valley)	End (Recency)	Overall Accuracy
1,500 Tokens	100%	100%	100%	100.0%
4,000 Tokens	100%	100%	100%	100.0%
8,000 Tokens	100%	100%	100%	100.0%

Evaluation methodology: Exact verbatim quote extraction from multi-document distractors.

📜 Attribution & License

This model is post-trained and released by Samrudh (dwivedula) under the Apache License 2.0.

Permitted: Commercial use, modification, distribution, private use, patent grant.
Foundation: Built on the open-weights Qwen-2.5 architecture under Apache 2.0.

🔱 Creator Contact & Community

Creator: Samrudh (dwivedula)
GitHub: https://github.com/Samrudh2006
Hugging Face: https://huggingface.co/dwivedula

Downloads last month: 313

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for dwivedula/Samrudh-1-Brahma-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

unsloth/Qwen2.5-7B-Instruct-bnb-4bit