Instructions to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1")
model = AutoModelForCausalLM.from_pretrained("ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1")
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]:]))

llama-cpp-python

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1",
	filename="Llama-3.2-1B-Instruct-RLHF-v0.1-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Local Apps Settings

llama.cpp

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 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 ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1: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 ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1: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 ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M

Use Docker

docker model run hf.co/ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M

LM Studio
Jan

vLLM

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M

SGLang

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 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 "ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1" \
    --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": "ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1",
		"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 "ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1" \
        --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": "ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with Ollama:
```
ollama run hf.co/ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M
```

Unsloth Studio

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 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 ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 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 ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 to start chatting

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with Docker Model Runner:
```
docker model run hf.co/ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M
```

Lemonade

How to use ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-1B-Instruct-RLHF-v0.1-Q4_K_M

List all available models

lemonade list

This model's benchmark results

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.4253	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.3768	±	N/A
		strict-match	5	exact_match	↑	0.3768	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.5379	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.4483	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.4217	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.5366	±	N/A

Original `meta-llama/Llama-3.2-1B-Instruct` benchmark results

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.4145	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.3412	±	N/A
		strict-match	5	exact_match	↑	0.3412	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.5335	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.4298	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.4288	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.5366	±	N/A

Below is a side-by-side comparison of the two result sets. For each task, the higher value (i.e., “better” on that metric) is highlighted in bold:

Task	this	orig	Better?
tinyArc (acc_norm)	0.4253	0.4145	v1 higher
tinyGSM8k (exact_match)	0.3768	0.3412	v1 higher
tinyHellaswag (acc_norm)	0.5379	0.5335	v1 higher
tinyMMLU (acc_norm)	0.4483	0.4298	v1 higher
tinyTruthfulQA (acc)	0.4217	0.4288	v2 higher
tinyWinogrande (acc_norm)	0.5366	0.5366	tie

Observations

Ours outperforms the original on four tasks (tinyArc, tinyGSM8k, tinyHellaswag, tinyMMLU).
The original outperforms ours on one task (tinyTruthfulQA).
One task is a tie (tinyWinogrande).

Given these comparisons, our results are stronger overall because it has higher scores on the majority of tasks. The only exception is on tinyTruthfulQA, where the original scores slightly better, and on tinyWinogrande, both versions tie.

Downloads last month: 39

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1

Base model

meta-llama/Llama-3.2-1B-Instruct

Quantized

(401)

this model

Quantizations

1 model

ericflo
/

Llama-3.2-1B-Instruct-RLHF-v0.1

This model's benchmark results

Original `meta-llama/Llama-3.2-1B-Instruct` benchmark results

Observations

Model tree for ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1

Dataset used to train ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1

This model's benchmark results

Original meta-llama/Llama-3.2-1B-Instruct benchmark results

Observations

Model tree for ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1

Dataset used to train ericflo/Llama-3.2-1B-Instruct-RLHF-v0.1

Original `meta-llama/Llama-3.2-1B-Instruct` benchmark results