neuralnets
/

sarvam-30b-4bit

@@ -1,199 +1,120 @@
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+# Sarvam-30B 4-Bit (BitsAndBytes)
+This repository provides a **4-bit NF4 quantized version** of the base model **`sarvamai/sarvam-30b`** using **bitsandbytes**.
+The quantization significantly reduces GPU memory usage while preserving strong inference performance.
+Base model
+`sarvamai/sarvam-30b`
+Architecture
+`SarvamMoEForCausalLM`
+---
+# Quantization Details
+Quantization method: **BitsAndBytes 4-bit (NF4)**
+Configuration used:
+* load_in_4bit = True
+* bnb_4bit_quant_type = nf4
+* bnb_4bit_compute_dtype = float16
+* bnb_4bit_use_double_quant = True
+Approximate GPU memory usage:
+| Model         | GPU VRAM  |
+| ------------- | --------- |
+| FP16 original | ~60 GB    |
+| 4-bit NF4     | ~16-18 GB |
+This version is recommended for most users who want to run the model with reduced hardware requirements.
+---
+# Installation
+Install the required libraries.
+```bash
+pip install transformers accelerate bitsandbytes torch safetensors
+```
+---
+# Loading the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "neuralnets/sarvam-30b-4bit",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "neuralnets/sarvam-30b-4bit",
+    trust_remote_code=True
+)
+```
+---
+# Example Inference
+```python
+prompt = "Explain mixture of experts in simple terms."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=200
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+---
+# Hardware Requirements
+Recommended GPUs:
+* A100 40GB or 80GB
+* RTX 4090
+* RTX 3090 (with offloading)
+CPU RAM recommendation:
+* 32 GB or higher
+---
+# Notes
+* This model uses **bitsandbytes quantization** integrated into Hugging Face Transformers.
+* The Sarvam architecture requires `trust_remote_code=True`.
+* Designed primarily for **inference workloads**.
+---
+# Base Model
+Original model:
+`sarvamai/sarvam-30b`
+Please refer to the base repository for model training details and benchmarks.
+---
+# License
+This repository distributes a **quantized derivative** of the original model.
+Users must follow the license of the upstream model:
+`sarvamai/sarvam-30b`