---
language:
- en
license: apache-2.0
tags:
- text-generation
base_model: JackFram/llama-160m
datasets:
- ehartford/wizard_vicuna_70k_unfiltered
- totally-not-an-llm/EverythingLM-data-V3
- Open-Orca/SlimOrca-Dedup
- databricks/databricks-dolly-15k
- THUDM/webglm-qa
widget:
- text: '<|im_start|>system

    You are a helpful assistant, who answers with empathy.<|im_end|>

    <|im_start|>user

    Got a question for you!<|im_end|>

    <|im_start|>assistant

    Sure! What''s it?<|im_end|>

    <|im_start|>user

    Why do you love cats so much!? 🐈<|im_end|>

    <|im_start|>assistant'
- text: '<|im_start|>system

    You are a helpful assistant who answers user''s questions with empathy.<|im_end|>

    <|im_start|>user

    Who is Mona Lisa?<|im_end|>

    <|im_start|>assistant'
- text: '<|im_start|>system

    You are a helpful assistant who provides concise responses.<|im_end|>

    <|im_start|>user

    Heya!<|im_end|>

    <|im_start|>assistant

    Hi! How may I help you today?<|im_end|>

    <|im_start|>user

    I need to build a simple website. Where should I start learning about web development?<|im_end|>

    <|im_start|>assistant'
- text: '<|im_start|>user

    Invited some friends to come home today. Give me some ideas for games to play
    with them!<|im_end|>

    <|im_start|>assistant'
- text: '<|im_start|>system

    You are a helpful assistant who answers user''s questions with details and curiosity.<|im_end|>

    <|im_start|>user

    What are some potential applications for quantum computing?<|im_end|>

    <|im_start|>assistant'
- text: '<|im_start|>system

    You are a helpful assistant who gives creative responses.<|im_end|>

    <|im_start|>user

    Write the specs of a game about mages in a fantasy world.<|im_end|>

    <|im_start|>assistant'
- text: '<|im_start|>system

    You are a helpful assistant who answers user''s questions with details.<|im_end|>

    <|im_start|>user

    Tell me about the pros and cons of social media.<|im_end|>

    <|im_start|>assistant'
- text: '<|im_start|>system

    You are a helpful assistant who answers user''s questions with confidence.<|im_end|>

    <|im_start|>user

    What is a dog?<|im_end|>

    <|im_start|>assistant

    A dog is a four-legged, domesticated animal that is a member of the class Mammalia,
    which includes all mammals. Dogs are known for their loyalty, playfulness, and
    ability to be trained for various tasks. They are also used for hunting, herding,
    and as service animals.<|im_end|>

    <|im_start|>user

    What is the color of an apple?<|im_end|>

    <|im_start|>assistant'
inference:
  parameters:
    max_new_tokens: 250
    penalty_alpha: 0.5
    top_k: 4
    repetition_penalty: 1.01
model-index:
- name: Llama-160M-Chat-v1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 24.74
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 35.29
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 26.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 44.16
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 51.3
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 0.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
---

# A Llama Chat Model of 160M Parameters

- Base model: [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m)
- Datasets:
  - [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
  - [totally-not-an-llm/EverythingLM-data-V3](https://huggingface.co/datasets/totally-not-an-llm/EverythingLM-data-V3)
  - [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
  - [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
  - [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa)
- Availability in other ML formats:
  - GGUF: [Felladrin/gguf-Llama-160M-Chat-v1](https://huggingface.co/Felladrin/gguf-Llama-160M-Chat-v1)
  - ONNX: [Felladrin/onnx-Llama-160M-Chat-v1](https://huggingface.co/Felladrin/onnx-Llama-160M-Chat-v1)
  - MLC: [Felladrin/mlc-q4f16-Llama-160M-Chat-v1](https://huggingface.co/Felladrin/mlc-q4f16-Llama-160M-Chat-v1)
  - MLX: [mlx-community/Llama-160M-Chat-v1-4bit-mlx](https://huggingface.co/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

```yml
penalty_alpha: 0.5
top_k: 4
repetition_penalty: 1.01
```

## Usage Example

```python
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"])
```

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__Llama-160M-Chat-v1)

|             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|