Instructions to use Jagneshdeveloper/Prakrit1.0-7b-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jagneshdeveloper/Prakrit1.0-7b-small with Transformers:

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

pipe = pipeline("text-generation", model="Jagneshdeveloper/Prakrit1.0-7b-small")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jagneshdeveloper/Prakrit1.0-7b-small")
model = AutoModelForCausalLM.from_pretrained("Jagneshdeveloper/Prakrit1.0-7b-small", 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 Jagneshdeveloper/Prakrit1.0-7b-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jagneshdeveloper/Prakrit1.0-7b-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jagneshdeveloper/Prakrit1.0-7b-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jagneshdeveloper/Prakrit1.0-7b-small

SGLang

How to use Jagneshdeveloper/Prakrit1.0-7b-small 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 "Jagneshdeveloper/Prakrit1.0-7b-small" \
    --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": "Jagneshdeveloper/Prakrit1.0-7b-small",
		"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 "Jagneshdeveloper/Prakrit1.0-7b-small" \
        --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": "Jagneshdeveloper/Prakrit1.0-7b-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jagneshdeveloper/Prakrit1.0-7b-small with Docker Model Runner:
```
docker model run hf.co/Jagneshdeveloper/Prakrit1.0-7b-small
```

Jagneshdeveloper commited on 8 days ago

Commit

1c87483

verified ·

1 Parent(s): 946e336

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -18,9 +18,9 @@ pipeline_tag: text-generation
 # 🇮🇳 Prakrit1.0-7B-Small
-<img src="https://wikimedia.org" alt="Flag of India" width="160" style="border-radius: 8px; box-shadow: 0px 4px 10px rgba(0,0,0,0.15); margin: 15px 0;">
-<h3>🚀 A High-Performance Specialist Model Made in India</h3>
 <p align="center">
   Developed by <b>Jagneshdeveloper</b>
@@ -28,12 +28,12 @@ pipeline_tag: text-generation
 ---
-[![License: Apache 2.0](https://shields.io)](https://opensource.org)
-[![Parameters](https://shields.io)]()
-[![Focus](https://shields.io)]()
 </div>
 ## 📌 Overview
 **Prakrit1.0-7B-Small** is an advanced 7-billion parameter large language model proudly developed by **Jagneshdeveloper**. Built on top of the powerful **Qwen2.5** architecture, this model has been custom-engineered and fine-tuned specifically for autonomous agentic workflows and elite coding tasks.
@@ -84,7 +84,7 @@ prompt = "Write a Python script to scrape website data and format it into a stru
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.3)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 ---

 # 🇮🇳 Prakrit1.0-7B-Small
+## 🌟 **Made in India** 🌟
+<h3>🚀 A High-Performance Specialist Model</h3>
 <p align="center">
   Developed by <b>Jagneshdeveloper</b>
 ---
+`📄 License: Apache 2.0` | `⚙️ Parameters: 7 Billion` | `💻 Focus: Coding & Agents`
 </div>
+---
 ## 📌 Overview
 **Prakrit1.0-7B-Small** is an advanced 7-billion parameter large language model proudly developed by **Jagneshdeveloper**. Built on top of the powerful **Qwen2.5** architecture, this model has been custom-engineered and fine-tuned specifically for autonomous agentic workflows and elite coding tasks.
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.3)
+print(tokenizer.decode(outputs, skip_special_tokens=True))
 ```
 ---