Instructions to use eulogik/Bharat-Tiny-LLM-fused with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eulogik/Bharat-Tiny-LLM-fused with Transformers:

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

pipe = pipeline("text-generation", model="eulogik/Bharat-Tiny-LLM-fused")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eulogik/Bharat-Tiny-LLM-fused")
model = AutoModelForCausalLM.from_pretrained("eulogik/Bharat-Tiny-LLM-fused", 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 eulogik/Bharat-Tiny-LLM-fused with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/eulogik/Bharat-Tiny-LLM-fused

SGLang

How to use eulogik/Bharat-Tiny-LLM-fused 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 "eulogik/Bharat-Tiny-LLM-fused" \
    --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": "eulogik/Bharat-Tiny-LLM-fused",
		"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 "eulogik/Bharat-Tiny-LLM-fused" \
        --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": "eulogik/Bharat-Tiny-LLM-fused",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eulogik/Bharat-Tiny-LLM-fused with Docker Model Runner:
```
docker model run hf.co/eulogik/Bharat-Tiny-LLM-fused
```

Bharat-Tiny-LLM (fused · fp16)

This is the full-precision fused model for Bharat-Tiny-LLM — the LoRA adapter merged into the base Qwen2.5-1.5B weights, in PyTorch float16.

Use this repo when you want to:

run inference on CPU / CUDA with transformers,
fine-tune further, or
produce your own quantized builds (GGUF, MLX, etc.).

Built by eulogik

For most users

You probably want a smaller, ready-to-run build instead:

Build	Repo	Size	Use
MLX 4-bit (edge / Apple Silicon)	`eulogik/Bharat-Tiny-LLM`	~880 MB	Recommended for Mac / on-device
GGUF Q4_K_M (llama.cpp, Android / Pi / CPU)	`eulogik/Bharat-Tiny-LLM-GGUF`	~1.06 GB	Cross-platform, llama.cpp
PyTorch fp16 (this repo)	`eulogik/Bharat-Tiny-LLM-fused`	~3.3 GB	Server / fine-tuning base

Quick start (transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("eulogik/Bharat-Tiny-LLM-fused")
tokenizer = AutoTokenizer.from_pretrained("eulogik/Bharat-Tiny-LLM-fused")

messages = [{"role": "user", "content": "Chai peete hain?"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")
out = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    top_p=0.85,
    repetition_penalty=1.25,
    no_repeat_ngram_size=3,
    do_sample=True,
)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

⚠️ Generation config matters. The base Qwen2.5-1.5B emits garbled out-of-script tokens at high temperature. Always use temperature ≈ 0.3 + repetition_penalty ≥ 1.25 + no_repeat_ngram_size = 3. The bharat-tiny-llm PyPI package applies these for you.

License

Apache-2.0 (base Qwen2.5-1.5B weights Apache-2.0; LoRA adapter Apache-2.0).

Downloads last month: 400

Safetensors

Model size

2B params

Tensor type

F16

Model tree for eulogik/Bharat-Tiny-LLM-fused

Base model

Qwen/Qwen2.5-1.5B

Finetuned

(397)

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