Instructions to use Jagneshdeveloper/Ekant-14b-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jagneshdeveloper/Ekant-14b-small with Transformers:

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

pipe = pipeline("text-generation", model="Jagneshdeveloper/Ekant-14b-small")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jagneshdeveloper/Ekant-14b-small")
model = AutoModelForCausalLM.from_pretrained("Jagneshdeveloper/Ekant-14b-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/Ekant-14b-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jagneshdeveloper/Ekant-14b-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/Ekant-14b-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jagneshdeveloper/Ekant-14b-small

SGLang

How to use Jagneshdeveloper/Ekant-14b-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/Ekant-14b-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/Ekant-14b-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/Ekant-14b-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/Ekant-14b-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jagneshdeveloper/Ekant-14b-small with Docker Model Runner:
```
docker model run hf.co/Jagneshdeveloper/Ekant-14b-small
```

Jagneshdeveloper commited on 8 days ago

Commit

c614cae

verified ·

1 Parent(s): e73eb8e

Update README.md

Browse files

Files changed (1) hide show

README.md +13 -6

README.md CHANGED Viewed

@@ -32,7 +32,7 @@ pipeline_tag: text-generation
 ---
-`📄 License: APACHE-2.0` | `⚙️ Parameters: 14 Billion` | `💻 Focus: Elite Coding, Reasoning & Agents`
 </div>
@@ -70,7 +70,7 @@ This model was compiled under a strict resource-constrained hardware architectur
 * **Developer:** Jagneshdeveloper
 * **Base Architecture:** Built on top of Microsoft Phi-4 (Phi3 For Causal LM Core Class)
 * **Parameters:** 14 Billion (14B)
-* **License:** APACHE-2.0 (Permissive Open-Source)
 * **Primary Language:** English (en)
 ---
@@ -83,7 +83,8 @@ You can quickly load and deploy **Ekant-14B-small** using the Hugging Face `tran
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-model_name = "Jagneshdeveloper/Ekant-14b-small"
 # Load the optimized tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -97,7 +98,13 @@ model = AutoModelForCausalLM.from_pretrained(
 # Test prompt for deep reasoning & agentic execution
 prompt = "Write an optimized Python script to scrape website data dynamically, handle API authentication token refreshes, and format it into a structured JSON array."
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.5, do_sample=True)
 print(tokenizer.decode(outputs, skip_special_tokens=True))
 ```
@@ -119,8 +126,8 @@ print(tokenizer.decode(outputs, skip_special_tokens=True))
 ## 🤝 Attribution & Support
-Created with ❤️ by **Jagneshdeveloper** in India. This model is distributed under the open and permissive **MIT License**, providing full freedom for commercial deployment, adjustments, and derivatives.
 Special credit and attribution are extended to **Microsoft** for their foundational open-weights research contributions (`phi-4` and `Phi-4-reasoning-plus`), which served as the essential structural pillars and base anchors for this advanced mathematical crossover fusion project.
-For feedback, feature requests, or collaborations, feel free to open a discussion in the community tab!

 ---
+`📄 License: Apache 2.0` | `⚙️ Parameters: 14 Billion` | `💻 Focus: Elite Coding, Reasoning & Agents`
 </div>
 * **Developer:** Jagneshdeveloper
 * **Base Architecture:** Built on top of Microsoft Phi-4 (Phi3 For Causal LM Core Class)
 * **Parameters:** 14 Billion (14B)
+* **License:** Apache 2.0 (Permissive Open-Source)
 * **Primary Language:** English (en)
 ---
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+# Real repository target path verified on your profile
+model_name = "Jagneshdeveloper/ultimate-Ekant-14b"
 # Load the optimized tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 # Test prompt for deep reasoning & agentic execution
 prompt = "Write an optimized Python script to scrape website data dynamically, handle API authentication token refreshes, and format it into a structured JSON array."
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.5,
+    do_sample=True,
+    pad_token_id=tokenizer.eos_token_id
+)
 print(tokenizer.decode(outputs, skip_special_tokens=True))
 ```
 ## 🤝 Attribution & Support
+Created with ❤️ by **Jagneshdeveloper** in India. This model is distributed under the open and permissive **Apache 2.0 License**, providing full freedom for commercial deployment, modifications, and distributed derivatives.
 Special credit and attribution are extended to **Microsoft** for their foundational open-weights research contributions (`phi-4` and `Phi-4-reasoning-plus`), which served as the essential structural pillars and base anchors for this advanced mathematical crossover fusion project.
+For feedback, feature requests, or collaborations, feel free to open a discussion in the community tab!