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---
language:
- en
license: apache-2.0
tags:
- text-generation
- TensorBlock
- GGUF
base_model: Felladrin/Llama-160M-Chat-v1
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
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 15.75
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 3.17
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 0.0
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 1.01
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 3.17
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 1.51
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
name: Open LLM Leaderboard
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;">
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
</p>
</div>
</div>
## Felladrin/Llama-160M-Chat-v1 - GGUF
This repo contains GGUF format model files for [Felladrin/Llama-160M-Chat-v1](https://huggingface.co/Felladrin/Llama-160M-Chat-v1).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
<div style="text-align: left; margin: 20px 0;">
<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Run them on the TensorBlock client using your local machine ↗
</a>
</div>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama-160M-Chat-v1-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q2_K.gguf) | Q2_K | 0.066 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama-160M-Chat-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q3_K_S.gguf) | Q3_K_S | 0.075 GB | very small, high quality loss |
| [Llama-160M-Chat-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q3_K_M.gguf) | Q3_K_M | 0.080 GB | very small, high quality loss |
| [Llama-160M-Chat-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q3_K_L.gguf) | Q3_K_L | 0.085 GB | small, substantial quality loss |
| [Llama-160M-Chat-v1-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q4_0.gguf) | Q4_0 | 0.092 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama-160M-Chat-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q4_K_S.gguf) | Q4_K_S | 0.092 GB | small, greater quality loss |
| [Llama-160M-Chat-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q4_K_M.gguf) | Q4_K_M | 0.096 GB | medium, balanced quality - recommended |
| [Llama-160M-Chat-v1-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q5_0.gguf) | Q5_0 | 0.108 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama-160M-Chat-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q5_K_S.gguf) | Q5_K_S | 0.108 GB | large, low quality loss - recommended |
| [Llama-160M-Chat-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q5_K_M.gguf) | Q5_K_M | 0.110 GB | large, very low quality loss - recommended |
| [Llama-160M-Chat-v1-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q6_K.gguf) | Q6_K | 0.125 GB | very large, extremely low quality loss |
| [Llama-160M-Chat-v1-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q8_0.gguf) | Q8_0 | 0.161 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/Llama-160M-Chat-v1-GGUF --include "Llama-160M-Chat-v1-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/Llama-160M-Chat-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|