Instructions to use Felladrin/Llama-160M-Chat-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Felladrin/Llama-160M-Chat-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Felladrin/Llama-160M-Chat-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
model = AutoModelForCausalLM.from_pretrained("Felladrin/Llama-160M-Chat-v1")
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 Felladrin/Llama-160M-Chat-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Felladrin/Llama-160M-Chat-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Felladrin/Llama-160M-Chat-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Felladrin/Llama-160M-Chat-v1

SGLang

How to use Felladrin/Llama-160M-Chat-v1 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 "Felladrin/Llama-160M-Chat-v1" \
    --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": "Felladrin/Llama-160M-Chat-v1",
		"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 "Felladrin/Llama-160M-Chat-v1" \
        --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": "Felladrin/Llama-160M-Chat-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Felladrin/Llama-160M-Chat-v1 with Docker Model Runner:
```
docker model run hf.co/Felladrin/Llama-160M-Chat-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A Llama Chat Model of 160M Parameters

Base model: JackFram/llama-160m
Datasets:
Availability in other ML formats:
- GGUF: Felladrin/gguf-Llama-160M-Chat-v1
- ONNX: Felladrin/onnx-Llama-160M-Chat-v1
- MLC: Felladrin/mlc-q4f16-Llama-160M-Chat-v1
- MLX: mlx-community/Llama-160M-Chat-v1-4bit-mlx

Recommended Prompt Format

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant

Recommended Inference Parameters

penalty_alpha: 0.5
top_k: 4
repetition_penalty: 1.01

Usage Example

from transformers import pipeline

generate = pipeline("text-generation", "Felladrin/Llama-160M-Chat-v1")

messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant who answers user's questions with details and curiosity.",
    },
    {
        "role": "user",
        "content": "What are some potential applications for quantum computing?",
    },
]

prompt = generate.tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

output = generate(
    prompt,
    max_new_tokens=1024,
    penalty_alpha=0.5,
    top_k=4,
    repetition_penalty=1.01,
)

print(output[0]["generated_text"])

Old Open LLM Leaderboard Evaluation Results

Metric	Value
Avg.	30.27
AI2 Reasoning Challenge (25-Shot)	24.74
HellaSwag (10-Shot)	35.29
MMLU (5-Shot)	26.13
TruthfulQA (0-shot)	44.16
Winogrande (5-shot)	51.30
GSM8k (5-shot)	0.00

New Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	4.10
IFEval (0-Shot)	15.75
BBH (3-Shot)	3.17
MATH Lvl 5 (4-Shot)	0.00
GPQA (0-shot)	1.01
MuSR (0-shot)	3.17
MMLU-PRO (5-shot)	1.51

Downloads last month: 1,009

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for Felladrin/Llama-160M-Chat-v1

Base model

JackFram/llama-160m

Finetuned

(13)

this model

Finetunes

3 models

Merges

1 model

Quantizations

6 models

Datasets used to train Felladrin/Llama-160M-Chat-v1

Spaces using Felladrin/Llama-160M-Chat-v1 17

Collection including Felladrin/Llama-160M-Chat-v1

Trained Models 🏋️

Collection

They may be small, but they're training like giants! • 9 items • Updated Aug 16, 2025 • 20

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

24.740
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

35.290
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

26.130
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

44.160
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

51.300
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

0.000
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

15.750
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

3.170