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Felladrin_-_Llama-160M-Chat-v1-gguf
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Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


Llama-160M-Chat-v1 - GGUF
- Model creator: https://huggingface.co/Felladrin/
- Original model: https://huggingface.co/Felladrin/Llama-160M-Chat-v1/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-160M-Chat-v1.Q2_K.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q2_K.gguf) | Q2_K | 0.07GB |
| [Llama-160M-Chat-v1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.IQ3_XS.gguf) | IQ3_XS | 0.07GB |
| [Llama-160M-Chat-v1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.IQ3_S.gguf) | IQ3_S | 0.07GB |
| [Llama-160M-Chat-v1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q3_K_S.gguf) | Q3_K_S | 0.07GB |
| [Llama-160M-Chat-v1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.IQ3_M.gguf) | IQ3_M | 0.08GB |
| [Llama-160M-Chat-v1.Q3_K.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q3_K.gguf) | Q3_K | 0.08GB |
| [Llama-160M-Chat-v1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q3_K_M.gguf) | Q3_K_M | 0.08GB |
| [Llama-160M-Chat-v1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q3_K_L.gguf) | Q3_K_L | 0.08GB |
| [Llama-160M-Chat-v1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.IQ4_XS.gguf) | IQ4_XS | 0.09GB |
| [Llama-160M-Chat-v1.Q4_0.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q4_0.gguf) | Q4_0 | 0.09GB |
| [Llama-160M-Chat-v1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.IQ4_NL.gguf) | IQ4_NL | 0.09GB |
| [Llama-160M-Chat-v1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q4_K_S.gguf) | Q4_K_S | 0.09GB |
| [Llama-160M-Chat-v1.Q4_K.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q4_K.gguf) | Q4_K | 0.1GB |
| [Llama-160M-Chat-v1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q4_K_M.gguf) | Q4_K_M | 0.1GB |
| [Llama-160M-Chat-v1.Q4_1.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q4_1.gguf) | Q4_1 | 0.1GB |
| [Llama-160M-Chat-v1.Q5_0.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q5_0.gguf) | Q5_0 | 0.11GB |
| [Llama-160M-Chat-v1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q5_K_S.gguf) | Q5_K_S | 0.11GB |
| [Llama-160M-Chat-v1.Q5_K.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q5_K.gguf) | Q5_K | 0.11GB |
| [Llama-160M-Chat-v1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q5_K_M.gguf) | Q5_K_M | 0.11GB |
| [Llama-160M-Chat-v1.Q5_1.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q5_1.gguf) | Q5_1 | 0.12GB |
| [Llama-160M-Chat-v1.Q6_K.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q6_K.gguf) | Q6_K | 0.12GB |
| [Llama-160M-Chat-v1.Q8_0.gguf](https://huggingface.co/RichardErkhov/Felladrin_-_Llama-160M-Chat-v1-gguf/blob/main/Llama-160M-Chat-v1.Q8_0.gguf) | Q8_0 | 0.16GB |




Original model description:
---
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:
  - messages:
      - role: system
        content: You are a helpful assistant, who answers with empathy.
      - role: user
        content: Got a question for you!
      - role: assistant
        content: "Sure! What's it?"
      - role: user
        content: Why do you love cats so much!? 🐈
  - messages:
      - role: system
        content: "You are a helpful assistant who answers user's questions with empathy."
      - role: user
        content: Who is Mona Lisa?
  - messages:
      - role: system
        content: You are a helpful assistant who provides concise responses.
      - role: user
        content: Heya!
      - role: assistant
        content: Hi! How may I help you today?
      - role: user
        content: I need to build a simple website. Where should I start learning about web development?
  - messages:
      - role: user
        content: Invited some friends to come home today. Give me some ideas for games to play with them!
  - 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?
  - messages:
      - role: system
        content: You are a helpful assistant who gives creative responses.
      - role: user
        content: Write the specs of a game about mages in a fantasy world.
  - messages:
      - role: system
        content: "You are a helpful assistant who answers user's questions with details."
      - role: user
        content: Tell me about the pros and cons of social media.
  - messages:
      - role: system
        content: "You are a helpful assistant who answers user's questions with confidence."
      - role: user
        content: What is a dog?
      - role: assistant
        content: '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.'
      - role: user
        content: What is the color of an apple?
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|