Text Generation
GGUF
English
enigma
valiant
valiant-labs
llama
llama-3.2
llama-3.2-instruct
llama-3.2-instruct-3b
llama-3
llama-3-instruct
llama-3-instruct-3b
3b
code
code-instruct
python
conversational
chat
instruct
Eval Results (legacy)
Instructions to use QuantFactory/Llama3.2-3B-Enigma-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Llama3.2-3B-Enigma-GGUF", filename="Llama3.2-3B-Enigma.Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Llama3.2-3B-Enigma-GGUF: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 QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Llama3.2-3B-Enigma-GGUF: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 QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/Llama3.2-3B-Enigma-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/Llama3.2-3B-Enigma-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Ollama:
ollama run hf.co/QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF 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 QuantFactory/Llama3.2-3B-Enigma-GGUF 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 QuantFactory/Llama3.2-3B-Enigma-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/Llama3.2-3B-Enigma-GGUF to start chatting
- Pi
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf QuantFactory/Llama3.2-3B-Enigma-GGUF: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": "QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf QuantFactory/Llama3.2-3B-Enigma-GGUF: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 QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Llama3.2-3B-Enigma-GGUF-Q4_K_M
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
ADDED
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| 1 |
+
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| 2 |
+
---
|
| 3 |
+
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
+
license: llama3.2
|
| 7 |
+
tags:
|
| 8 |
+
- enigma
|
| 9 |
+
- valiant
|
| 10 |
+
- valiant-labs
|
| 11 |
+
- llama
|
| 12 |
+
- llama-3.2
|
| 13 |
+
- llama-3.2-instruct
|
| 14 |
+
- llama-3.2-instruct-3b
|
| 15 |
+
- llama-3
|
| 16 |
+
- llama-3-instruct
|
| 17 |
+
- llama-3-instruct-3b
|
| 18 |
+
- 3b
|
| 19 |
+
- code
|
| 20 |
+
- code-instruct
|
| 21 |
+
- python
|
| 22 |
+
- conversational
|
| 23 |
+
- chat
|
| 24 |
+
- instruct
|
| 25 |
+
base_model: meta-llama/Llama-3.2-3B-Instruct
|
| 26 |
+
datasets:
|
| 27 |
+
- sequelbox/Tachibana
|
| 28 |
+
- sequelbox/Supernova
|
| 29 |
+
pipeline_tag: text-generation
|
| 30 |
+
model_type: llama
|
| 31 |
+
model-index:
|
| 32 |
+
- name: Llama3.2-3B-Enigma
|
| 33 |
+
results:
|
| 34 |
+
- task:
|
| 35 |
+
type: text-generation
|
| 36 |
+
name: Text Generation
|
| 37 |
+
dataset:
|
| 38 |
+
name: Winogrande (5-Shot)
|
| 39 |
+
type: winogrande
|
| 40 |
+
args:
|
| 41 |
+
num_few_shot: 5
|
| 42 |
+
metrics:
|
| 43 |
+
- type: acc
|
| 44 |
+
value: 67.96
|
| 45 |
+
name: acc
|
| 46 |
+
- task:
|
| 47 |
+
type: text-generation
|
| 48 |
+
name: Text Generation
|
| 49 |
+
dataset:
|
| 50 |
+
name: ARC Challenge (25-Shot)
|
| 51 |
+
type: arc-challenge
|
| 52 |
+
args:
|
| 53 |
+
num_few_shot: 25
|
| 54 |
+
metrics:
|
| 55 |
+
- type: acc_norm
|
| 56 |
+
value: 47.18
|
| 57 |
+
name: normalized accuracy
|
| 58 |
+
- task:
|
| 59 |
+
type: text-generation
|
| 60 |
+
name: Text Generation
|
| 61 |
+
dataset:
|
| 62 |
+
name: IFEval (0-Shot)
|
| 63 |
+
type: HuggingFaceH4/ifeval
|
| 64 |
+
args:
|
| 65 |
+
num_few_shot: 0
|
| 66 |
+
metrics:
|
| 67 |
+
- type: inst_level_strict_acc and prompt_level_strict_acc
|
| 68 |
+
value: 47.75
|
| 69 |
+
name: strict accuracy
|
| 70 |
+
source:
|
| 71 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-Enigma
|
| 72 |
+
name: Open LLM Leaderboard
|
| 73 |
+
- task:
|
| 74 |
+
type: text-generation
|
| 75 |
+
name: Text Generation
|
| 76 |
+
dataset:
|
| 77 |
+
name: BBH (3-Shot)
|
| 78 |
+
type: BBH
|
| 79 |
+
args:
|
| 80 |
+
num_few_shot: 3
|
| 81 |
+
metrics:
|
| 82 |
+
- type: acc_norm
|
| 83 |
+
value: 18.81
|
| 84 |
+
name: normalized accuracy
|
| 85 |
+
source:
|
| 86 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-Enigma
|
| 87 |
+
name: Open LLM Leaderboard
|
| 88 |
+
- task:
|
| 89 |
+
type: text-generation
|
| 90 |
+
name: Text Generation
|
| 91 |
+
dataset:
|
| 92 |
+
name: MATH Lvl 5 (4-Shot)
|
| 93 |
+
type: hendrycks/competition_math
|
| 94 |
+
args:
|
| 95 |
+
num_few_shot: 4
|
| 96 |
+
metrics:
|
| 97 |
+
- type: exact_match
|
| 98 |
+
value: 6.65
|
| 99 |
+
name: exact match
|
| 100 |
+
source:
|
| 101 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-Enigma
|
| 102 |
+
name: Open LLM Leaderboard
|
| 103 |
+
- task:
|
| 104 |
+
type: text-generation
|
| 105 |
+
name: Text Generation
|
| 106 |
+
dataset:
|
| 107 |
+
name: GPQA (0-shot)
|
| 108 |
+
type: Idavidrein/gpqa
|
| 109 |
+
args:
|
| 110 |
+
num_few_shot: 0
|
| 111 |
+
metrics:
|
| 112 |
+
- type: acc_norm
|
| 113 |
+
value: 1.45
|
| 114 |
+
name: acc_norm
|
| 115 |
+
source:
|
| 116 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-Enigma
|
| 117 |
+
name: Open LLM Leaderboard
|
| 118 |
+
- task:
|
| 119 |
+
type: text-generation
|
| 120 |
+
name: Text Generation
|
| 121 |
+
dataset:
|
| 122 |
+
name: MuSR (0-shot)
|
| 123 |
+
type: TAUR-Lab/MuSR
|
| 124 |
+
args:
|
| 125 |
+
num_few_shot: 0
|
| 126 |
+
metrics:
|
| 127 |
+
- type: acc_norm
|
| 128 |
+
value: 4.54
|
| 129 |
+
name: acc_norm
|
| 130 |
+
source:
|
| 131 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-Enigma
|
| 132 |
+
name: Open LLM Leaderboard
|
| 133 |
+
- task:
|
| 134 |
+
type: text-generation
|
| 135 |
+
name: Text Generation
|
| 136 |
+
dataset:
|
| 137 |
+
name: MMLU-PRO (5-shot)
|
| 138 |
+
type: TIGER-Lab/MMLU-Pro
|
| 139 |
+
config: main
|
| 140 |
+
split: test
|
| 141 |
+
args:
|
| 142 |
+
num_few_shot: 5
|
| 143 |
+
metrics:
|
| 144 |
+
- type: acc
|
| 145 |
+
value: 15.41
|
| 146 |
+
name: accuracy
|
| 147 |
+
source:
|
| 148 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.2-3B-Enigma
|
| 149 |
+
name: Open LLM Leaderboard
|
| 150 |
+
|
| 151 |
+
---
|
| 152 |
+
|
| 153 |
+
[](https://hf.co/QuantFactory)
|
| 154 |
+
|
| 155 |
+
|
| 156 |
+
# QuantFactory/Llama3.2-3B-Enigma-GGUF
|
| 157 |
+
This is quantized version of [ValiantLabs/Llama3.2-3B-Enigma](https://huggingface.co/ValiantLabs/Llama3.2-3B-Enigma) created using llama.cpp
|
| 158 |
+
|
| 159 |
+
# Original Model Card
|
| 160 |
+
|
| 161 |
+
|
| 162 |
+
|
| 163 |
+

|
| 164 |
+
|
| 165 |
+
|
| 166 |
+
Enigma is a code-instruct model built on Llama 3.2 3b.
|
| 167 |
+
- High quality code instruct performance with the Llama 3.2 Instruct chat format
|
| 168 |
+
- Finetuned on synthetic code-instruct data generated with Llama 3.1 405b. [Find the current version of the dataset here!](https://huggingface.co/datasets/sequelbox/Tachibana)
|
| 169 |
+
- Overall chat performance supplemented with [generalist synthetic data.](https://huggingface.co/datasets/sequelbox/Supernova)
|
| 170 |
+
|
| 171 |
+
|
| 172 |
+
## Version
|
| 173 |
+
|
| 174 |
+
This is the **2024-09-30** release of Enigma for Llama 3.2 3b, enhancing code-instruct and general chat capabilities.
|
| 175 |
+
|
| 176 |
+
Enigma is also available for [Llama 3.1 8b!](https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma)
|
| 177 |
+
|
| 178 |
+
Help us and recommend Enigma to your friends! We're excited for more Enigma releases in the future.
|
| 179 |
+
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| 180 |
+
|
| 181 |
+
## Prompting Guide
|
| 182 |
+
Enigma uses the [Llama 3.2 Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) prompt format. The example script below can be used as a starting point for general chat:
|
| 183 |
+
|
| 184 |
+
```python
|
| 185 |
+
import transformers
|
| 186 |
+
import torch
|
| 187 |
+
|
| 188 |
+
model_id = "ValiantLabs/Llama3.2-3B-Enigma"
|
| 189 |
+
|
| 190 |
+
pipeline = transformers.pipeline(
|
| 191 |
+
"text-generation",
|
| 192 |
+
model=model_id,
|
| 193 |
+
model_kwargs={"torch_dtype": torch.bfloat16},
|
| 194 |
+
device_map="auto",
|
| 195 |
+
)
|
| 196 |
+
|
| 197 |
+
messages = [
|
| 198 |
+
{"role": "system", "content": "You are Enigma, a highly capable code assistant."},
|
| 199 |
+
{"role": "user", "content": "Can you explain virtualization to me?"}
|
| 200 |
+
]
|
| 201 |
+
|
| 202 |
+
outputs = pipeline(
|
| 203 |
+
messages,
|
| 204 |
+
max_new_tokens=1024,
|
| 205 |
+
)
|
| 206 |
+
|
| 207 |
+
print(outputs[0]["generated_text"][-1])
|
| 208 |
+
```
|
| 209 |
+
|
| 210 |
+
## The Model
|
| 211 |
+
Enigma is built on top of Llama 3.2 3b Instruct, using high quality code-instruct data and general chat data in Llama 3.2 Instruct prompt style to supplement overall performance.
|
| 212 |
+
|
| 213 |
+
Our current version of Enigma is trained on code-instruct data from [sequelbox/Tachibana](https://huggingface.co/datasets/sequelbox/Tachibana) and general chat data from [sequelbox/Supernova.](https://huggingface.co/datasets/sequelbox/Supernova)
|
| 214 |
+
|
| 215 |
+
|
| 216 |
+

|
| 217 |
+
|
| 218 |
+
|
| 219 |
+
Enigma is created by [Valiant Labs.](http://valiantlabs.ca/)
|
| 220 |
+
|
| 221 |
+
[Check out our HuggingFace page for Shining Valiant 2 and our other Build Tools models for creators!](https://huggingface.co/ValiantLabs)
|
| 222 |
+
|
| 223 |
+
[Follow us on X for updates on our models!](https://twitter.com/valiant_labs)
|
| 224 |
+
|
| 225 |
+
We care about open source.
|
| 226 |
+
For everyone to use.
|
| 227 |
+
|
| 228 |
+
We encourage others to finetune further from our models.
|
| 229 |
+
|
| 230 |
+
|