Instructions to use afrideva/evolvedSeeker_1_3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/evolvedSeeker_1_3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/evolvedSeeker_1_3-GGUF",
	filename="evolvedseeker_1_3.q2_k.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use afrideva/evolvedSeeker_1_3-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/evolvedSeeker_1_3-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 afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/evolvedSeeker_1_3-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 afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/evolvedSeeker_1_3-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 afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/evolvedSeeker_1_3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/evolvedSeeker_1_3-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": "afrideva/evolvedSeeker_1_3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M

Ollama
How to use afrideva/evolvedSeeker_1_3-GGUF with Ollama:
```
ollama run hf.co/afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M
```

Unsloth Studio new

How to use afrideva/evolvedSeeker_1_3-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 afrideva/evolvedSeeker_1_3-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 afrideva/evolvedSeeker_1_3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/evolvedSeeker_1_3-GGUF to start chatting

Docker Model Runner
How to use afrideva/evolvedSeeker_1_3-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/evolvedSeeker_1_3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/evolvedSeeker_1_3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.evolvedSeeker_1_3-GGUF-Q4_K_M

List all available models

lemonade list

TokenBender/evolvedSeeker_1_3-GGUF

Quantized GGUF model files for evolvedSeeker_1_3 from TokenBender

Name	Quant method	Size
evolvedseeker_1_3.fp16.gguf	fp16	2.69 GB
evolvedseeker_1_3.q2_k.gguf	q2_k	631.71 MB
evolvedseeker_1_3.q3_k_m.gguf	q3_k_m	704.97 MB
evolvedseeker_1_3.q4_k_m.gguf	q4_k_m	873.58 MB
evolvedseeker_1_3.q5_k_m.gguf	q5_k_m	1.00 GB
evolvedseeker_1_3.q6_k.gguf	q6_k	1.17 GB
evolvedseeker_1_3.q8_0.gguf	q8_0	1.43 GB

Original Model Card:

evolvedSeeker-1_3

EvolvedSeeker v0.0.1 (First phase)

This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on 50k instructions for 3 epochs.

I have mostly curated instructions from evolInstruct datasets and some portions of glaive coder.

Around 3k answers were modified via self-instruct.

Collaborate or Consult me - Twitter, Discord

Recommended format is ChatML, Alpaca will work but take care of EOT token

Chat Model Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TokenBender/evolvedSeeker_1_3", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TokenBender/evolvedSeeker_1_3", trust_remote_code=True).cuda()
messages=[
    { 'role': 'user', 'content': "write a program to reverse letters in each word in a sentence without reversing order of words in the sentence."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
# 32021 is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

Model description

First model of Project PIC (Partner-in-Crime) in 1.3B range. Almost all the work is pending right now for this model hence v0.0.1

Intended uses & limitations

Superfast Copilot Run near lossless quantized in 1G RAM. Useful for code dataset curation and evaluation.

Limitations - This is a smol model, so smol brain, may have crammed a few things. Reasoning tests may fail beyond a certain point.

Training procedure

SFT

Training results

Humaneval Score - 68.29%

Framework versions

Transformers 4.35.2
Pytorch 2.0.1
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 109

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for afrideva/evolvedSeeker_1_3-GGUF

Base model

deepseek-ai/deepseek-coder-1.3b-base

Finetuned

TokenBender/evolvedSeeker_1_3

Quantized

(4)

this model