Instructions to use afrideva/phi-2_dolly_instruction_polish-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use afrideva/phi-2_dolly_instruction_polish-GGUF with PEFT:
```
Task type is invalid.
```

How to use afrideva/phi-2_dolly_instruction_polish-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/phi-2_dolly_instruction_polish-GGUF",
	filename="phi-2_dolly_instruction_polish.fp16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use afrideva/phi-2_dolly_instruction_polish-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/phi-2_dolly_instruction_polish-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/phi-2_dolly_instruction_polish-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/phi-2_dolly_instruction_polish-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/phi-2_dolly_instruction_polish-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/phi-2_dolly_instruction_polish-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/phi-2_dolly_instruction_polish-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/phi-2_dolly_instruction_polish-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/phi-2_dolly_instruction_polish-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/phi-2_dolly_instruction_polish-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/phi-2_dolly_instruction_polish-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/phi-2_dolly_instruction_polish-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/phi-2_dolly_instruction_polish-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/phi-2_dolly_instruction_polish-GGUF:Q4_K_M

Ollama
How to use afrideva/phi-2_dolly_instruction_polish-GGUF with Ollama:
```
ollama run hf.co/afrideva/phi-2_dolly_instruction_polish-GGUF:Q4_K_M
```

Unsloth Studio new

How to use afrideva/phi-2_dolly_instruction_polish-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/phi-2_dolly_instruction_polish-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/phi-2_dolly_instruction_polish-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/phi-2_dolly_instruction_polish-GGUF to start chatting

Docker Model Runner
How to use afrideva/phi-2_dolly_instruction_polish-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/phi-2_dolly_instruction_polish-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/phi-2_dolly_instruction_polish-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/phi-2_dolly_instruction_polish-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.phi-2_dolly_instruction_polish-GGUF-Q4_K_M

List all available models

lemonade list

s3nh/phi-2_dolly_instruction_polish-GGUF

Quantized GGUF model files for phi-2_dolly_instruction_polish from s3nh

Name	Quant method	Size
phi-2_dolly_instruction_polish.fp16.gguf	fp16	5.56 GB
phi-2_dolly_instruction_polish.q2_k.gguf	q2_k	1.17 GB
phi-2_dolly_instruction_polish.q3_k_m.gguf	q3_k_m	1.48 GB
phi-2_dolly_instruction_polish.q4_k_m.gguf	q4_k_m	1.79 GB
phi-2_dolly_instruction_polish.q5_k_m.gguf	q5_k_m	2.07 GB
phi-2_dolly_instruction_polish.q6_k.gguf	q6_k	2.29 GB
phi-2_dolly_instruction_polish.q8_0.gguf	q8_0	2.96 GB

Original Model Card:

phi-2-sft-out

This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2813

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0	1	1.7973
1.9767	0.25	5290	1.4832
1.8474	0.5	10580	1.4356
1.8121	0.75	15870	1.4022
1.8333	1.0	21160	1.3678
1.6601	1.25	26450	1.3508
1.5452	1.5	31740	1.3357
1.7381	1.75	37030	1.3191
1.6256	2.0	42320	1.3090
1.5521	2.25	47610	1.2961
1.8318	2.5	52900	1.2910
1.6761	2.75	58190	1.2901
1.6312	3.0	63480	1.2879
1.7003	3.25	68770	1.2820
1.6915	3.5	74060	1.2814
1.5757	3.75	79350	1.2813

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.16.0
Tokenizers 0.15.0

Training procedure

The following bitsandbytes quantization config was used during training:

quant_method: bitsandbytes
load_in_8bit: False
load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: True
bnb_4bit_compute_dtype: bfloat16

Framework versions

PEFT 0.6.0

Downloads last month: 70

GGUF

Model size

3B params

Architecture

phi2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for afrideva/phi-2_dolly_instruction_polish-GGUF

Base model

microsoft/phi-2

Adapter

s3nh/phi-2_dolly_instruction_polish

Adapter

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