Instructions to use harsh12100/Llama-3.2-1b-bnb-4bit-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harsh12100/Llama-3.2-1b-bnb-4bit-python with Transformers:

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

pipe = pipeline("text-generation", model="harsh12100/Llama-3.2-1b-bnb-4bit-python")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("harsh12100/Llama-3.2-1b-bnb-4bit-python")
model = AutoModelForCausalLM.from_pretrained("harsh12100/Llama-3.2-1b-bnb-4bit-python", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use harsh12100/Llama-3.2-1b-bnb-4bit-python with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf harsh12100/Llama-3.2-1b-bnb-4bit-python: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 harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf harsh12100/Llama-3.2-1b-bnb-4bit-python: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 harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M

Use Docker

docker model run hf.co/harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M

LM Studio
Jan

vLLM

How to use harsh12100/Llama-3.2-1b-bnb-4bit-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harsh12100/Llama-3.2-1b-bnb-4bit-python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harsh12100/Llama-3.2-1b-bnb-4bit-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M

SGLang

How to use harsh12100/Llama-3.2-1b-bnb-4bit-python 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 "harsh12100/Llama-3.2-1b-bnb-4bit-python" \
    --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": "harsh12100/Llama-3.2-1b-bnb-4bit-python",
		"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 "harsh12100/Llama-3.2-1b-bnb-4bit-python" \
        --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": "harsh12100/Llama-3.2-1b-bnb-4bit-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use harsh12100/Llama-3.2-1b-bnb-4bit-python with Ollama:
```
ollama run hf.co/harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M
```

Unsloth Studio

How to use harsh12100/Llama-3.2-1b-bnb-4bit-python 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 harsh12100/Llama-3.2-1b-bnb-4bit-python 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 harsh12100/Llama-3.2-1b-bnb-4bit-python to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for harsh12100/Llama-3.2-1b-bnb-4bit-python to start chatting

Docker Model Runner
How to use harsh12100/Llama-3.2-1b-bnb-4bit-python with Docker Model Runner:
```
docker model run hf.co/harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M
```

Lemonade

How to use harsh12100/Llama-3.2-1b-bnb-4bit-python with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull harsh12100/Llama-3.2-1b-bnb-4bit-python:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-1b-bnb-4bit-python-Q4_K_M

List all available models

lemonade list

Atomic Chat

harsh12100 commited on Nov 25, 2024

Commit

aaba733

verified ·

1 Parent(s): 4a6fb6c

Trained with Unsloth

Browse files

Files changed (2) hide show

config.json +37 -2
pytorch_model.bin +1 -1

config.json CHANGED Viewed

@@ -1,3 +1,38 @@
 {
-    "model_type": "llama"
-}

 {
+  "_name_or_path": "unsloth/Llama-3.2-1B-bnb-4bit",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.46.2",
+  "unsloth_version": "2024.11.9",
+  "use_cache": true,
+  "vocab_size": 128256
+}

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