Instructions to use iamabhayaditya/EfficientMath-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use iamabhayaditya/EfficientMath-AI with PEFT:
```
Task type is invalid.
```

How to use iamabhayaditya/EfficientMath-AI with Transformers:

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

pipe = pipeline("text-generation", model="iamabhayaditya/EfficientMath-AI")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamabhayaditya/EfficientMath-AI")
model = AutoModelForCausalLM.from_pretrained("iamabhayaditya/EfficientMath-AI")

llama-cpp-python

How to use iamabhayaditya/EfficientMath-AI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="iamabhayaditya/EfficientMath-AI",
	filename="Meta-Llama-3.1-8B.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use iamabhayaditya/EfficientMath-AI with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf iamabhayaditya/EfficientMath-AI:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf iamabhayaditya/EfficientMath-AI:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf iamabhayaditya/EfficientMath-AI:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf iamabhayaditya/EfficientMath-AI:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf iamabhayaditya/EfficientMath-AI:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf iamabhayaditya/EfficientMath-AI:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf iamabhayaditya/EfficientMath-AI:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf iamabhayaditya/EfficientMath-AI:Q4_K_M

Use Docker

docker model run hf.co/iamabhayaditya/EfficientMath-AI:Q4_K_M

LM Studio
Jan

vLLM

How to use iamabhayaditya/EfficientMath-AI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamabhayaditya/EfficientMath-AI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamabhayaditya/EfficientMath-AI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iamabhayaditya/EfficientMath-AI:Q4_K_M

SGLang

How to use iamabhayaditya/EfficientMath-AI 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 "iamabhayaditya/EfficientMath-AI" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamabhayaditya/EfficientMath-AI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "iamabhayaditya/EfficientMath-AI" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamabhayaditya/EfficientMath-AI",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use iamabhayaditya/EfficientMath-AI with Ollama:
```
ollama run hf.co/iamabhayaditya/EfficientMath-AI:Q4_K_M
```

Unsloth Studio

How to use iamabhayaditya/EfficientMath-AI with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iamabhayaditya/EfficientMath-AI to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iamabhayaditya/EfficientMath-AI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for iamabhayaditya/EfficientMath-AI to start chatting

Docker Model Runner
How to use iamabhayaditya/EfficientMath-AI with Docker Model Runner:
```
docker model run hf.co/iamabhayaditya/EfficientMath-AI:Q4_K_M
```

Lemonade

How to use iamabhayaditya/EfficientMath-AI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull iamabhayaditya/EfficientMath-AI:Q4_K_M

Run and chat with the model

lemonade run user.EfficientMath-AI-Q4_K_M

List all available models

lemonade list

iamabhayaditya commited on Mar 24

Commit

5caf4f9

verified ·

1 Parent(s): 22423e2

Trained with Unsloth - config

Browse files

Files changed (1) hide show

config.json +33 -0

config.json ADDED Viewed

	@@ -0,0 +1,33 @@

+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "torch_dtype": "float16",
+    "eos_token_id": 128001,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "pad_token_id": 128004,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "rope_theta": 500000.0,
+        "rope_type": "default"
+    },
+    "tie_word_embeddings": false,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.3.10",
+    "use_cache": true,
+    "vocab_size": 128256
+}